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Big Data Analytics in Smart Grids: A Comprehensive Twenty-Year Review of Methods, Applications, and Emerging Challenges

Submitted:

20 December 2025

Posted:

22 December 2025

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Abstract

Over the past two decades, the transition from conventional power networks to smart grids has accelerated, driven by advances in digital communication and intelligent control technologies. Smart grids integrate sensing devices, automated metering, and data-driven management systems, producing large volumes of heterogeneous information across all operational layers. This review examines 220 publications from the last twenty years, highlighting major research trends, classifying works by publication type, and identifying the most influential journals and conferences. It also summarizes the contribution of each reviewed work and categorizes the analytical methods and smart-grid-related topics addressed. Finally, the paper outlines emerging challenges and future research directions that can further enhance the role of big data analytics in next-generation smart grids.

Keywords: 
big data analytics
;  
data-driven decision making
;  
power system applications
;  
smart grid technologies
Subject: 
Engineering  -   Electrical and Electronic Engineering

1. Introduction

The last decade, technological advancements in power systems and communications have led to improvements in grid stability, performance, and security in order to fulfill the expectations of customers. The scientific community has focused a large amount of attention on the transition to a next generation power system, known as smart grid (SG) [1,2,3,4,5,6]. SG refers to the integration of information and digital communication technologies with power grid systems to enable bi-directional communication and power flow.
The installation of smart meters and a variety of sensors on the network, the expansion of customer facilities and other factors, all contribute to a significant rise in the volume of data that has to be processed for the proper deployment of smart grid features. Figure 1, shows the arrangement of a smart grid as well as the sources and the advanced equipment used to create the big volume of data through the various measurements that are obtained by the devices used. The sources related with the pricing and bidding of energy market dealing with Automated Revenue Metering system, the management, control, and maintenance of grid devices and equipment, the operating utilities, the energy consumption assessed by smart meters, the users’ power consumption patterns and the synchrophasor technology. The major devices used as sensors to collect the data in smart grids, are smart meters, phasor measurement units and remote terminal units.
The characteristics of data received from smart grids are similar with these of big data (BD) [7,8,9]. These characteristics are the volume, velocity, variety, veracity, visibility, and value [10]. Advanced treatments are required to be applied to the data that is produced by smart grids because of the nature of the data itself, as well as its transmission and the real-time re-restrictions of specific demands.
This article presents a review of the big data approaches that are utilized in smart grids. The evaluation takes into consideration a number of 220 works [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230] that have been published in the literature over the course of the past 20 years. The main contribution are as follows:
  • The publications are chronologically distributed to highlight the research interest on the last 20 years. This allows researchers to determine whether or not the topic merits further investigation.
  • All mentioned publications are classified into two major categories, namely journal articles and conference papers.
  • The impact of the journals and conferences that have the highest influence is estimated based on the total number of publications per journal or conference. This provides the researcher with information regarding the frequency of publishing (announcement) in a journal (conference) and facilitates the selection of a journal or conference at which he/she can publish (present) a research work.
  • The contribution of each work is given analytically in a table, allowing the researcher to quickly grasp the topic of the work and gain a general understanding of it.
  • A categorization taking into consideration the various methods or techniques employed with big data is carried out.
  • A classification considering the big data topics in smart grids is conducted.
  • Future works and challenges of big data analytics applications and topics in smart grids are pointed out.
This paper is organized as follows: Section 2 presents the chronological distribution of all publications as well as their classification to journal articles and conference papers along with the corresponding impacts. Section 3 presents the main contribution of each publication, while Section 4 presents the categorization of publications considering the various big data analysis methods or techniques and the big data topics in smart grids. Section 5 discusses some future works and challenges, while Section 6 concludes the paper.

2. Chronological Distribution, Classification of Works and Impacts

In order to carry out this research, the information for publications was retrieved from IEEE Xplore as well as from a number of other digital libraries. The information that was downloaded about the metadata contains information about the title, the abstract, the authors, references, keywords, and date of publications. Journal articles and conference papers that have been published on the subject of big data analytics in smart grids between the years 2003 and 2022 are taken into consideration throughout this study. Note that among these publications, there are 29 literature review or survey publications on different topics related with big data analysis in smart grids.
Based on the date of publication of each work, we depicted the chronological distribution of the publications as shown in Figure 2. Based on the results, it can be concluded that big data analysis in smart grids was of particular interest to researchers during the period 2014-2018.
By processing the identities of journals and conferences, the bibliographic references are categorized as either journal articles or conference papers. From this classification, we found that the number of journal articles was 169, while the number of conference papers was 51, respectively. A percentage ratio depiction of these findings can be found in Figure 3. The flagship in the productivity list of journal articles is the “IEEE Transactions on Smart Grid” journal with 37 publications, followed by the “IEEE Transactions on Power Systems” journal with 12 publications and the “IEEE Access” journal with 9 publications, respectively. Table 1 presents the five most productive journals in terms of number of publications, while Figure 4 shows the percentage distribution for the total of the 169 journal articles.
The journals with less than three articles are as follows: “Energy Conversion and Management”, “IEEE Communications Magazine”, “IEEE Transactions on Big Data”, “IEEE Transactions on Power Delivery”, “Solar Energy”, “Applied Soft Computing”, “IEEE Transactions on Industry Applications”, “Big Data Research”, “IET Smart Grid”, “IEEE Transactions on Emerging Topics in Computing”, “Procedia Computer Science”, “IEEE Control Systems Letters”, “Computer Networks”, “Energy Policy”, “IEEE Power and Energy Magazine”, “IET Generation, Transmission & Distribution”, “Neural Computing and Applications”, “Lecture Notes of the Institute for Computer Sciences”, “Social Informatics and Telecommunications Engineering”, “Energy and Buildings”, “Mathematics”, “IEEE Transactions on Cloud Computing”, “IEEE Transactions on Parallel and Distributed Systems”, “Energy Procedia”, “Sustainability”, “Journal on Selected Areas in Communications”, “IEEJ Transactions on Electrical and Electronic Engineering”, “IEEE Transactions on Engineering Management”, “Journal of Cleaner Production”, “Computers & Electrical Engineering”, “Computing in Science & Engineering”, “Procedia Technology”, “Advanced Engineering Informatics”, “Machine Learning”, “Energy Economics”, “Advanced Data Mining and Applications”, “IEEE Transactions on Energy Conversion”, “Computer Science Review”, “Future Generation Computer Systems”, “Journal of Big Data’, “Neurocomputing”, “Wireless Personal Communications”, “Journal of Parallel and Distributed Computing”, “International Journal of Communication Systems”, “Machine Learning with Applications”, and “Wireless Communications and Mobile Computing”.
The impact of conference papers is summarized in Figure 5. The “ IEEE Power and Energy Society General Meeting” is the conference with the biggest number of publications, followed by the “IEEE PES Innovative Smart Grid Technologies” conference and the ‘IEEE International Conference on Big Data” conference with 6, 5, and 3 publications, respectively. Table 2 lists the four most prolific conferences in terms of quantity of publications.
Note that the “IEEE PES Innovative Smart Grid Technologies” conference includes conferences organized in Europe, Asia, and Latin America. The following conferences have less than two papers: “IEEE PES Power Systems Conference and Exposition”, “Brunei International Conference on Engineering and Technology”, “Chinese Automation Congress”, “IEEE Sensor Array and Multichannel Signal Processing Workshop”, “IEEE Canadian Conference on Electrical and Computer Engineering”, “IEEE Conference on Decision and Control”, “IEEE Conference on Technologies for Sustainability”, “IEEE Energy Conference”, “IEEE Industry Applications Society Annual Meeting”, “IEEE INFOCOM”, “IEEE International Conference on Agents”, “IEEE International Conference on Computational Intelligence and Computing Research”, “IEEE International Conference on Control Science and Systems Engineering”, “IEEE International Conference on Industrial Technology”, “IEEE International Conference on Smart City/SocialCom/SustainCom”, “IEEE International Congress on Big Data”, “IEEE PowerTech”, “IEEE/PES Power Systems Conference and Exposition”, “International Advanced Research Workshop on Transformers”, “International Conference and Exhibition on Electricity Distribution”, “International Conference on Electric Utility Deregulation and Restructuring and Power Technologies”, “International Conference on Fuzzy Systems and Knowledge Discovery”, “International Conference on Intelligent System Applications to Power Systems”, “International Conference on Smart Electric Grid”, “International Conference on Smart Grid and Clean Energy Technologies”, “IREP Symposium Bulk Power System Dynamics and Control-Optimization, Security and Control of the Emerging Power Grid”, “International Conference on Machine Learning and Cybernetics”, “PES T&D”, “Power Systems Computation Conference”, and “SoutheastCon”.

3. Contribution of Published Works

The contribution for each one of the 220 reviewed publications is presented in the following Table 3.

4. Classification of Publications Considering Big Data Analysis Methods or Techniques and Big Data Topics in Smart Grids

Considering the reviewed papers [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230], the big data analysis methods and techniques in smart grids can be classified in 13 categories as presented in Figure 6. Moreover, Table 4 presents a classification of the reviewed papers per aforementioned category.
The big data topics related with smart grids can be also classified in 25 different topics. Figure 7 presents these topics, while Table 5 provides a taxonomy of the reviewed papers per topic.
Despite the fact that artificial intelligence and machine learning are frequently used interchangeably, based on the analysis presented in Table 4, it can be concluded that the dominant method used for the big data analysis is the machine learning. Other methods and tools adopted for the big data analysis and related with the concept of artificial intelligence are cloud computing, clustering, data mining, deep learning and neural networks, while a specific category is the support vector machine models used for classifying and regressing data.
Additionally, signal processing, mathematical programming and statistical techniques are other alternatives to address big data analysis problems related with smart grids. Regarding big data-related topics in smart grids, the demand side management, the energy theft, the load forecasting, renewable energy sources, the smart metering as well as the synchrophasor technology are the most popular topics in the literature. Among them, the smart metering is the flagship topic in smart grids literature, followed by synchrophasor technology and renewable energy sources. There are a number of reasons for adopting these strategies for resolving challenges with smart grids. The most essential characteristic of a smart network is the incorporation of cutting-edge communication and information technology. In order to monitor and operate smart grids and manage their generation and demand, metering devices and gadgets using these technologies have been developed. The size and unpredictability of the data extracted from smart grids necessitate the use of specialized procedures and techniques for its management and processing.
For the purposes of comparison, the similarities and differences of this work with other published literary works are presented in Table 6. It is evident from the comparison, that none of the twenty-seven previously published publications includes a chronological distribution analysis or classification of the works according to journals or conferences. In addition to the present work, only eighteen of them included future works.

5. Future Research Works and Challenges of Big Data Analytics Applications and Topics in Smart Grids

The following is a summary of significant research works and issues that need to be addressed:
  • Study of electromagnetic disturbances, which affect the electronically based equipment of smart grids.
  • Innovative strategies and fresh points of view to effectively solve the problem of power quality in smart grids.
  • Determination of the fundamental components of smart meter analytics, which will allow the vast variety of smart metering solutions to be linked together and the primary analytical endeavors to be identified.
  • New processes and workflows for diagnostics in real time.
  • Customer engagement in initiatives utilizing demand response.
  • Study of the interconnected system’s economic and financial growth.
  • Techniques that are focused on the decrease of losses in various smart grid’s communication technologies.
  • Analysis of the energy sustainability based on consumers effectively and cooperatively control the amount of energy they use and then communicate relevant information with one another.
  • Addressing issues with traffic engineering, economic models, energy portfolio management, and conceptualizing process-level energy use.
  • Sustainability to the big data industry in terms of smart grid.
  • Techniques for protection of smart grids to guarantee their reliability and stability.
  • Load control and demand response analysis as well as real-time pricing applications.
  • Analysis of electrical load patterns and forecasting techniques for demand and generation (renewable or not).
  • Techniques reducing security risks and improving the disaster recovery ability of smart grids.
  • Techniques that incorporate renewable energy sources to smart grids.
  • Efficient cryptographic schemes.
  • Penalty and incentive mechanisms.
  • Optimal allocation of smart metering devices.
  • Advanced security and data communication techniques for integration, control, and monitoring of smart grid.
  • A unified and complete standard for smart grids.
  • Defending mechanisms against cyber attacks.
  • Predictive models for different climatic and topographic conditions.

6. Conclusions

This paper provides a summary of the big data methodologies that are implemented in smart grids nowadays. The evaluation takes into account a total of 220 works that have been published in the field of literary study throughout the course of the last two decades. The papers have been organized in a chronological order to reflect the research focus over the past 20 years and they are classified into journal articles and conference papers. On the basis of the total number of articles submitted to each journal or conference, an estimate of the effect of the publications and conferences that have the most influence is derived. The contribution of each publication is summarized, while a classification that takes into account the several approaches or strategies that are used when working with big data is carried out. Moreover, a categorization is made taking into consideration the large data issues raised by smart grids. Finally, the paper focuses on upcoming issues posed by big data analytics applications and subjects in smart grids. It is obvious that as the popularity of big data integration and data lake storage increases, so does the trend toward using smart meter data for purposes beyond billing and cost reductions. Utilities must focus on the harness of smart meters and big data to maximize consumer value, meet sustainable energy targets, enhance service quality, and generate cost savings. By using the next generation of smart meters, utilities will be able to analyze millions of data points in real time and distill them into actionable insights that help more than just their bottom line. Advanced visibility throughout the grid will provide both operations and maintenance with actionable information into asset health to prevent problems before they develop. Possessing the ability to identify abnormalities before outages or other damaging system problems occur will give substantial cost savings and proactive customer service capabilities required in the future competitive environment. In terms of a greener smart grid, renewable energy sources, such as solar, wind, and hydrogen, may be effectively managed and distributed due to smart grid technologies. The smart grid integrates diverse distributed energy resource assets to the electrical grid. Data collection is crucial to the interaction between the smart grid and renewable energy. Wind farms, for instance, use mechanical gears that need each connection to handle numerous sensors. Each sensor can detect the present climatic and ambient conditions. This information can be swiftly sent across the grid to notify the utility of any problems, so enhancing both service quality and safety.

Author Contributions

Conceptualization, N.M.; methodology, N.M.; validation, N.M. and C.P.; formal analysis, N.M. and C.P.; investigation, N.M.; resources, N.M. and C.P.; data curation, N.M. and C.P.; writing—original draft preparation, N.M.; writing—review and editing, N.M. and C.P. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Data sources and equipment in smart grids.
Figure 1. Data sources and equipment in smart grids.
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Figure 2. Chronological distribution of the published works.
Figure 2. Chronological distribution of the published works.
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Figure 3. Percentage of journal articles and conference papers.
Figure 3. Percentage of journal articles and conference papers.
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Figure 4. Impact of journal articles in terms of publications per journal.
Figure 4. Impact of journal articles in terms of publications per journal.
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Figure 5. Impact of conference papers in terms of publications per conference.
Figure 5. Impact of conference papers in terms of publications per conference.
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Figure 6. Classification of publications considering big data analysis methods and techniques in smart grids.
Figure 6. Classification of publications considering big data analysis methods and techniques in smart grids.
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Figure 7. Classification of publications considering big data topics in smart grids.
Figure 7. Classification of publications considering big data topics in smart grids.
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Table 1. The five most “productive” journals.
Table 1. The five most “productive” journals.
Rank Journal Publisher Number of publications
1 IEEE Transactions on Smart Grid IEEE 37
2 IEEE Transactions on Power Systems IEEE 12
3 IEEE Access IEEE 9
4 Renewable and Sustainable Energy Reviews ELSEVIER 8
5 Energies MDPI 7
Table 2. The four most “productive” conferences.
Table 2. The four most “productive” conferences.
Rank Conference Number of publications
1 IEEE Power and Energy Society General Meeting 6
2 IEEE PES Innovative Smart Grid Technologies 5
3 IEEE International Conference on Big Data 3
4 IEEE International Conference on Smart Grid Communications 2
4 International Conference on Renewable Energy Research and Applications 2
4 International Power and Energy Conference 2
Table 3. Contribution of reviewed works.
Table 3. Contribution of reviewed works.
Reference Contribution
[11] A price elasticity and customer benefit function-based extended responsive load economic model.
[12] A deep long short-term memory recurrent neural network is used to forecast photovoltaic output power.
[13] A hybrid algorithm for detecting power quality disturbances in electrical power systems that includes simulation of power quality events, feature extraction, selection of dominant features, and classification of selected features.
[14] A multiple linear regression analysis model to make probabilistic forecasts of solar energy
[15] Single and mixed power quality disturbances in distributed generation systems connected to the grid can be detected and categorized automatically with the help of a variational mode decomposition and a decision tree algorithm.
[16] Description of the Real Time Dynamics Monitoring System (RTDMS®) for SynchroPhasor System Technology (SPST).
[17] A review of the different levels of power quality problems in smart grids, as well as their structure, importance, and requirements.
[18] Comparison of the efficiency of a random forest and an artificial neural network in predicting the amount of electricity used by the Heating, Venting, and Air Conditioning system in a hotel in Madrid, Spain.
[19] The Bayesian probability theory is applied to solve the multiple line outage detection problem in linear time.
[20] A data analytics-based technique for monitoring and detecting malicious activity on a cyber-physical transmission protection system.
[21] A probabilistic health state estimation framework for smart grid power transformer lifetime management that integrates probabilistic forecasting models with Monte Carlo-based Bayesian filtering methods.
[22] Based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded multilevel inverter, a novel fault location technique is presented, which results in a significant reduction in fault location detection time.
[23] A thorough examination of smart electricity meters and their application, focusing on key aspects of the metering process, the various stakeholder interests, and the technologies used to satisfy stakeholder interests.
[24] Development of a general computing grid framework for probabilistic-based reliability and security assessment of power systems.
[25] A survey for wide area smart grid’s architectural model and control.
[26] Using fuzzy logic to detect abnormal operating conditions and to generate fault signatures for various fault types, a novel health monitoring system for a variable air volume unit is created. An artificial neural network classification technique is then applied to fault signatures to classify the fault type.
[27] Hybrid deep learning methods to enhance the outcomes in Saudi smart grid load forecasting.
[28] A review of current energy management systems, applications, and frameworks.
[29] A strategic vision of a self-healing smart grid.
[30] A hybrid method for short-term bus load forecasting of power systems based on a forecast-aided state estimator and a multilayer perceptron neural network.
[31] A neural network approach for hort-term load forecast having a novel learning algorithm based on a new modified harmony search technique.
[32] Detection of fraudulent consumers by only observing electricity consumption records using a Tomek Link Borderline Synthetic Minority Oversampling Technique with Support Vector Machine and a Temporal Convolutional Network with Enhanced Multi-Layer Perceptron.
[33] Description of the role of the big data enterprise in envisioning the smart grid, based on a six-plane model that explains the factors necessary to transform the “brown-tussle” between the smart grid and the big data enterprise into a “greener-win-win” resolution.
[34] The clustering yields consistent results across multiple iterations of the algorithm, and it is combined with a time-series analysis, a novel cluster selection algorithm, and a multilayer perceptron neural network to create a hybrid solar radiation forecasting method for varying time horizons.
[35] An efficient dynamic solution for online smart grid topology identification and monitoring by combining concepts from compressive sensing and graph theory.
[36] A cloud-computing-based universal framework for big data information management in smart grids.
[37] An approach for transient stability assessment incorporating diversity by modifying it explicitly.
[38] A semantic Extract-Transform-Load system that integrates and publishes data from many sources as open linked data using semantic technologies.
[39] An event comprehension framework that analyses PMU data and develops high-level interpretations of the data with minimal latency to offer grid operators with greater situational awareness.
[40] An integer programming based formulation for solution of the PMU placement problem.
[41] An additive quantile regression model for a set of future distribution quantiles using a boosting approach.
[42] Three major components for a public utilities demand response system are discussed.
[43] A summary of current efforts to integrate cloud computing into the existing smart grid architecture in order to ensure the reliability, efficiency, and security of energy distribution.
[44] A linear programming optimization algorithm based on the average hourly voltage and load data from the smart meters as well as the distribution topology data as given by the geographic information system.
[45] A framework for very short-term probabilistic solar power estimates utilizing data acquired by a smart grid infrastructure.
[46] A literature review of smart infrastructure and smart management techniques.
[47] An algorithm that searches for the global optimum mainly through migration and mutation.
[48] A differential evolution with biogeography-based optimization method for solving different convex and non-convex economic load dispatch problems.
[49] A biogeography-based optimization algorithm for solving both convex and nonconvex economic load dispatch problems of thermal generators.
[50] A review of the current state of big data analytics and its applications in power grids, identifying problems and potential from utility, industry, and research standpoints.
[51] The “telegestore” project in ENEL metering system.
[52] Instance-based transfer learning algorithm for wind power quantile regression.
[53] Integration and analysis of data from many sources based on the geographic information system that provides a framework for integrating these data via spatial and temporal relationships.
[54] A mobile edge computing-based system enabled by big data analytics for the electric vehicle charging use.
[55] An artificial neural network approach for forecasting short-term wind power in Portugal.
[56] Active sensing paradigm for inverter-enabled grid topology identification.
[57] Smart metering electric load disaggregation using a novel feature extraction method and supervised classification.
[58] A prediction of the intraday load curve at the residential level.
[59] An opposition-based harmony search algorithm for the solution of combined economic and emission dispatch issues in power systems.
[60] A kernel-based support vector regression combination model for solving the problem of short-term load forecasting.
[61] Security risk modeling in critical smart grid infrastructures in the era of big data and artificial intelligence.
[62] A clustering approach for identifying natural consumer segmentation and temporal consumption patterns.
[63] An online forecasting model for a photovoltaic power system that uses a radial basis function network.
[64] A framework for systematically incorporating renewable energy sources, distributed storage units, cooling facilities, and dynamic pricing into the workload and energy management activities of a data center network.
[65] A method for rapidly identifying power system events using online synchrophasor measurements based on the reduction of the dimensionality of emergent ambient phasor measurement unit data.
[66] A measurement-based methodology to compute the power flow Jacobian matrix and infer important information about the system’s topology in almost real time.
[67] An integrated solution for the real-time power quality disturbance analysis based on an optimized deep belief network taking raw data of signals directly and an embedded parallel computing platform.
[68] A summary of the clustering approaches used to construct appropriate customer grouping, as part of a larger system for analyzing data on electrical demand patterns.
[69] The huge streaming PMU data were represented by utilizing the fluctuations in the covariance matrix of the large streaming PMU data and by developing a unique power state assessment technique based on the numerous high-dimensional covariance matrix tests.
[70] The interrelated generation planning projects used to derive a unique -branch lattice process and approximate interrelated geometric Brownian motion processes that describes the evolution of market values of the completed projects.
[71] Development and analysis of a computational intelligence algorithm for smart condition monitoring based on the ability to predict if a particular machine is in working condition or not and considering a dataset provided.
[72] The performance of a metaheuristic-based n-steps-ahead time series forecasting model was evaluated using a multithreaded method.
[73] A stream mining algorithm in order for the synchrophasor data to fulfill the speedy decision-making requirements of power system situational awareness.
[74] A method for reducing the dimensionality of synchrophasor data in real-time by finding correlations between synchrophasor measurements and representing the data with their primary components without losing too much information.
[75] Actual disturbance files acquired by PMUs were exhaustively grouped and mapped to a variety of disturbance occurrences using agglomerative hierarchical clustering.
[76] A summary of the prospects, ideas, and problems of data management in smart grids, as well as a summary of the Big Data technologies and methods that may be utilized to manage smart grid requirements, including as processing, storage, and even visualization.
[77] The importance of big data for smart grid implementation.
[78] A model-free technique that uses high-resolution phasor measurement unit data to deliver accurate and fast information on the stability of the system.
[79] Modelling of localized faults in the smart grid of a multi-utility by means of several heterogeneous features using an one-class classifier based on an interaction among clustering and dissimilarity measure learning techniques where specialized measures have been designed to deal with each feature type (e.g., categorical, metric, and time series).
[80] A method for compressing data in smart distribution systems utilizing singular value decomposition.
[81] Detection of electricity theft by comparing the energy consumption patterns of customers based on the historical data and support vector machines trained with the data collected from smart meters to represent all possible forms of theft.
[82] Utilization of big data techniques for dynamic energy management on smart grid systems.
[83] The application of random projections in order to tackle the problem of the huge quantity of data generated in smart grids by using a data sketching procedure to obtain a summarized version of original data.
[84] State-of-the-art computationally intelligent approaches used in load forecasting in terms of their categorization and assessment for the sustainable functioning of the entire energy management system.
[85] An approach for short-term load forecasting that utilizes an adaptive two-stage hybrid network with self-organized map and support vector machine.
[86] A bootstrap approach that incorporates randomness from the model and external factors for generating forecasting distributions.
[87] A review of cloud technologies to smart grids.
[88] Diagnostics of a power transformer are performed using a support vector machine with genetic algorithm.
[89] A technique for recognizing patterns in load profiles to aid in the management of the electric sector.
[90] A dual-scale similarity function algorithm based on the combination of Euclidean distance and the shape of the curve to describe the similarity between the power load curves and a clustering of load curves according to the principle of spectral clustering to make the algorithm insensitive to the data distribution and data dimension and to ensure convergence to the global optimal solution.
[91] Clusters determination of the typical load profiles based on the fuzzy c-means and hierarchical clustering algorithms along with probability neural networks used as a classification tool for assign consumers’ type of activity to the particular cluster.
[92] A two-case optimization-based profit maximizing technique for data centers without and with renewable generators behind the meter.
[93] A technique using wavelet neural networks and data pre-filtering to predict extremely short-term loads 1 hour into the future in 5-minute increments using a moving window.
[94] An efficient and secure data acquisition scheme based on ciphertext policy attribute based encryption.
[95] Three metrics, namely, the building occupant energy-use efficiency, entropy, and intensity enable the design of more targeted energy conservation campaigns introduced for the classification of building occupants according to their energy-use patterns.
[96] Security issues, challenges, and recommendations for block chain technology in smart grid, energy trading, and big data.
[97] A rank reduction optimization approach for singular value decomposition-based data compression.
[98] A method for the intelligent recognition of transient instability pattern based on power grid topology.
[99] A data aggregation approach that protects privacy against internal attackers using Boneh-Goh-Nissim public key encryption.
[100] Various timeframe dispatch and scheduling, such as day-ahead and real-time scheduling, to address the difficulty of integrating variable wind energy into the bulk electricity system.
[101] A method that performs a high-dimensional analysis and compares the results to random matrix theory predictions in order to discover anomalies.
[102] A technique for short-term load forecasting in microgrids based on pattern recognition using a self-organizing map, clustering of the prior partition using the k-means algorithm, and demand forecasting for each cluster using a multilayer perceptron.
[103] A Mycielski - Markov hybrid model for the the prediction of solar radiation.
[104] Big data analytics for the enhancement of the smart grid.
[105] A support vector regression-based electric load forecasting model based on a chaotic ant swarm optimization technique to enhance the forecasting performance by looking for the optimal combination of parameters.
[106] A load forecasting model that improves forecasting performance by combining the seasonal recurrent support vector regression model with the chaotic artificial bee colony method.
[107] Analysis, protection and security concerns associated with IoT generated big data of smart grids.
[108] A multi-label support vector machine to identify distribution lines outages in response to extreme weather events by leveraging advanced metering infrastructure data.
[109] A framework for temporal, functional, and geographical big data processing for large-scale smart grids.
[110] A review of the most recent advancements in energy big data analytics and security/privacy.
[111] A study of the applicability and efficacy of many data mining techniques for energy market price classification, as well as a data model for generating the initial data set for price classification.
[112] An artificial neural network method for energy theft detection in distribution systems.
[113] A redundancy architectural design for measuring network deployment in a power system including PMUs.
[114] A mathematical framework for a typical fog-based smart grid design that describes the placement and planning of fog computing in smart grid.
[115] A technique for forecasting city-wide power gains from solar panels using precise 3D urban massing models in conjunction with Daysim-based hourly irradiance simulations, average meteorological year climatic data, and hourly estimated rooftop temperatures.
[116] A study of the applications of Internet of Things technology in the power grid, as well as the integration of renewable energy sources, in order to achieve sustainable energy and avert climate change.
[117] An approach for detecting theft that combines a convolutional neural network, long-short term memory, and a deep siamese network.
[118] A strategy based on both fuzzy theory and game theory for presenting a system that can effectively avoid a prisoner’s dilemma in an environment where electricity is created autonomously via the use of new and renewable energy and then exchanged for consumption.
[119] A two-layer dynamic optimum synchrophasor measuring devices selection technique for the spatial characterization of the synchrophasor measurement system coupled with a matching pursuit decomposition for characterization of the signals in the time domain. To study defect detection and identification, a hidden Markov model-based SG situational awareness technique is developed.
[120] A survey on big data and the power grid that provides background information on relevant studies, approaches, and tools for big data in energy.
[121] A technique that uses PMU measurement data to predict the inter-area dominating oscillation modes in a bulk power system and to handle both ringdown data and ambient data with robust performance.
[122] An approach for outage management in distribution systems based on the multiple-hypothesis method and the extended protection tree that handles complicated outage situations involving numerous failures, missing outage notifications from fault indicators and smart meters, and protection miscoordination.
[123] Adopting a top-down approach with decision tree and support vector machine classifiers for the detection of consumers that actively steal power.
[124] A tensor-based large data processing approach in the Internet of Everything context of a smart city using support vector machine.
[125] A consumption pattern-based energy theft-detecting algorithm and a support vector machine utilized to forecast the normal and fraudulent usage patterns of clients.
[126] A method for synthesizing load curves and estimating the equation of the typical load curve in a building.
[127] A study of damping/firstswing stability control and wide-area secondary voltage regulation.
[128] Utilization of extraordinarily big data sets that are difficult to analyze with conventional database tools in power system operation, control, and protection.
[129] A two-module strategy that combines data preprocessing and categorization into a single framework. The first module includes data imputation, outliers management, standardization, and class balance stages to provide quality data for classifier training, while the second module separates honest and dishonest users using a support vector machine classifier.
[130] A parallel-detrended fluctuation analysis technique leveraging a computer cluster for the rapid identification of transient events on enormous PMU data.
[131] A method for distributed Prony analysis utilizing consensus and subgradient updating.
[132] Utilization of prediction interval creation techniques in the forecasting of power prices.
[133] A method for the lossless compression of voltage magnitude and phase data in which power system features are employed to improve upon conventional compression techniques.
[134] A procedure for solving the short-term load forecasting problem for residential households.
[135] The new industrial revolution and the significance of Artificial Intelligence are reviewed and addressed.
[136] Data mining methods were used to create four time series models with varying prediction horizons.
[137] A scalable technique expressed as a stochastic knapsack problem including projected customer reactions for demand response program targeting using unique data obtainable from smart meters at the individual level.
[138] A formulation based on a variant of the conventional support vector data description for multi-class classification with novelty detection that yields precise results for the classification of actual waveforms.
[139] A bibliographical review of the broad background of wind speed and wind power forecasting research and development.
[140] The smart home idea was expanded to general consumer units with immediate and distributed decision-making capabilities.
[141] Analysis of the requirements for protecting the privacy of big data in smart grids and proposal of associated approaches.
[142] A Lambda-based big data architecture for smart grids, including a Real-Time CEP engine incorporated in the Speed layer and utilizing Spark Streaming.
[143] A privacy-preserving authentication technique based on aggregated proofs (AP3A) for achieving simultaneous identification and safe identification for varied working mode battery vehicles.
[144] Micro-grid load forecasting using a hybrid model built of empirical mode decomposition (EMD), extended Kalman filter (EKF), and extreme learning machine with Kernel (KELM).
[145] A cost-effective situational awareness tool (FNET/GridEye) for the development of wide-area monitoring system in electric power grids.
[146] A precise categorization of electric consumers enabling electric utilities to set individualized tariffs for each customer category.
[147] A classification strategy for the detection and categorization of complex power quality disturbances (PQDs) utilizing a three-level Bayesian-Network with multiple connections.
[148] Development of a cloud computing platform including the system architecture design and a cyber security scheme.
[149] A power security system based on stream data mining that consists primarily of an intelligent electric outlet, a coordinator, and a server, with a ZigBee module serving as a link between the traditional power grid and the coordinator.
[150] An artificial neural network tool for the rating of the load curves of all consumers of a power utility in data centres and from
various consumer profiles, together with the registration data and analysis of each load feeder.
[151] Cloud computing model can be used for developing smart grid solutions
[152] Establishment of a cloud-based smart grid for evaluating big data and making decisions to balance consumer demand.
[153] A method for analyzing synchrophasor data that use statistical correlation techniques to uncover data inconsistencies inconsistencies and power system contingencies.
[154] A protection strategy for microgrids with inverter-interfaced distributed generating units based on the wavelet transform and a data-mining algorithm.
[155] A platform for real-time anomaly detection based on smart meter data obtained at the users’ premises.
[156] A review on big data management and decision making in smart grids.
[157] Detection of customers with anomalous drops in their consumed energy by means of a windowed analysis with the use of the Pearson coefficient, Bayesian networks and decision trees.
[158] A comprehensive analysis of the reliability impacts of major smart grid resources, including renewables, demand response, and storage.
[159] A framework for the prospective deployment of big data analytics for smart grids and renewable energy power utilities based on five distinct machine learning techniques for forecasting the stability of the smart grid.
[160] The implementation of a large data framework for smart grids on a secure Google Service cloud platform.
[161] A big data ecosystem for the smart grid that is built on the cutting-edge Lambda architecture and is capable of executing parallel batch and real-time operations on distributed data.
[162] A study of the elements that impact a customer’s decision to subscribe to a certain demand response management system, as well as a classification of consumer types to non-green comfort seeking behavior (NCSB) and green incentive seeking behavior (GISB).
[163] Support vector machine (SVM)-based methodology for non-technical loss analysis for electric utilities.
[164] A technique for capturing geographic variation in wind production alongside transmission restrictions.
[165] A system for power load forecasting using support vector machine and ant colony optimization.
[166] A review of non-technical losses, load profiles, and data mining strategies utilized to decrease non-technical loss activities.
[167] The K-means clustering approach is used to categorize data on power load into five groups: super-peak, peak, cycling, intermediate, and base.
[168] A consumer automated energy management system (CAES) used to reduce residential energy costs and smooth energy usage, which is based on an online learning application that implicitly estimates the impact of future energy prices and consumer decisions on long-term costs and schedules residential device usage.
[169] A method to reduce peak electricity demand in building climate control by using real-time electricity pricing and applying model predictive control (MPC) along with
least-squares support vector machine for electricity tariff price forecasting.
[170] Comparison of classification techniques, namely Linear Discriminant Analysis (LDA), Nearest Neighbor Method (kNN), Learning Vector Quantization (LVQ), and Support Vector Machine (SVM), to identify several power quality disturbances in the context of smart metering design for smart grids with high penetration of photovoltaic (PV) systems.
[171] A systematic and automated approach to build a hybrid intrusion detection system that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks based on a data mining technique called common path mining.
[172] A process that employs a collection of unsupervised machine learning algorithms and selects the Fuzzy C-Means (FCM) algorithm for clustering load curves.
[173] A data-driven method for determining the underlying network architecture for low voltage (LV) distribution networks, including load phase connection, based on energy measurement time series.
[174] Intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as methods for processing the data for various applications, and a method for parameter estimation based on the voltage drop equation and regression analysis to improve the accuracy of distribution system models.
[175] A methodology to extract electric energy consumption patterns in big data time series, based on the study of four clustering validity indices in their parallelized versions along with the application of a distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library.
[176] A class of autonomous broker agents for retail power trading that can operate in a broad variety of smart electricity markets utilizing reinforcement learning with function approximation and can derive long-term, profit-maximizing strategies.
[177] A bad data pre-estimation filtering algorithm of PMU-based linear state estimators coupled with a Discrete Kalman Filter (DKF) state estimator that uses only PMU measurements and an AutoRegressive Integrated Moving Average (ARIMA) process combined with the outcome of the adopted DKF state estimator.
[178] A hybrid method for probabilistic electrical load forecasting that employs a generalized extreme learning machine (GELM) for training an enhanced wavelet neural network (IWNN), wavelet preprocessing, and bootstrapping.
[179] Tools for online transient stability assessment that use classification and regression trees (CART) and multivariate adaptive regression splines (MARS) models in combination with high-speed synchronized phasor data derived from phasor measurement units.
[180] A self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering) methodology, capable of handling large amounts of time-series data in the context of electricity load management research.
[181] A literature review of short-term load forecasting strategies based on artificial intelligence.
[182] A load forecasting strategy by employing
a distance based outlier rejection (DBOR) methodology and a hybrid technique combining evidence from Genetic Based Feature Selector (GBFS) and Rough set Base Feature Selector (RBFS).
[183] The deployment of an online version of the eXtended Classifier System for Clustering (XCSc) on top of the smart grid using a multi-agent system architecture with the goal of generating a dynamic data partitioning scheme for the smart grid storage repository.
[184] An assessment of both the concerns of big data for electric utilities and the use of data analytics to determine if data is relevant to regulatory and policy considerations regarding electric utilities.
[185] A hybrid model based on particle swarm optimization (PSO) and support vector machine (SVM) for anticipating short term electricity load.
[186] Deep neural network (DNN)-based load forecasting models for demand side empirical load database, trained by pre-training limited Boltzmann machine and rectified linear unit without pre-training, respectively.
[187] A data-driven methodology for the estimation the power generation of invisible solar power sites by using the measured values from a small number of representative sites including a data dimension reduction engine and a mapping function and additionally proposing a hybrid method based on k-means clustering and principal component analysis.
[188] A data-driven technique of association rule mining for transformer state parameters that combines the Apriori algorithm with a probabilistic graphical model.
[189] A pooling-based deep recurrent neural network that groups a collection of customers’ load profiles into a pool of inputs in order to construct a customized deep learning application for residential load forecasting.
[190] A technique for identifying the phasing of transformers and single-phase taps as well as creating or verifying meter-to-transformer mappings utilizing voltage and kilowatt-hour readings from advanced metering infrastructure (AMI) based on linear regression and fundamental voltage drop relationships.
[191] Apache spark is presented as a unified cluster computing platform appropriate for storing and analyzing large data on smart grid data for applications such as autonomous demand response and real-time pricing.
[192] A combination of neural networks and fuzzy logic approaches is used to accurately estimate the production of a wind farm.
[193] A cloud-based software platform for big data analytics in smart grids.
[194] A proposal for mining patterns of residential energy consumption behavior in smart grids.
[195] A framework and methods for estimating the various states of electric cars with respect to their demand, location, and grid connection durations, based on Monte Carlo simulation and fuzzy logic probability.
[196] A technique for detecting and localizing power outages based on social media data (Twitter).
[197] A review of big data standards and maturity models for smart grid distributed generation.
[198] A hybrid model comprised of k-Medoids clustering and deep learning models for hourly load forecasting one day in advance at the level of distribution transformers.
[199] Various approaches of machine learning have been utilized for load forecasting.
[200] In-depth analysis of the various big data technologies and big data analytics in the context of the smart grid, as well as a discussion of the difficulties and opportunities presented by the arrival of machine learning and big data from smart grids.
[201] A high-level overview of some of the Matlab tools that allow the user to extract relevant information from big data sources.
[202] A smart metering load data compression approach based on the detection of load features using the general extreme value (GEV) distribution characteristic of residential load data.
[203] A review of big data issues in smart grid.
[204] K-Means methods to improve clustering by harvesting inherent structure from the smart meter data.
[205] A fault location identification method for smart distribution network based on a state estimation algorithm which uses real-time data from simulated phasor
measurement units placed in the distribution network.
[206] A self-organizing map methodology to achieve the segmentation and demand patterns classification for electrical customers on the basis of database measurements.
[207] The online power system Transient Stability Assessment (TSA) problem is represented as a two-class classification problem, and a novel data mining approach, Core Vector Machine (CVM), based on PMU large data is proposed to tackle the problem.
[208] A review of deep learning for renewable energy forecasting.
[209] Intelligent agent and big data-based smart grid architecture.
[210] Based on a deep convolutional neural network, a unique full closed-loop strategy to identify and categorize power quality problems is presented. A unit structure consisting of 1-D convolutional, pooling, and batch-normalization layers is meant to capture multi-scale features and reduce overfitting in the context of the power quality disturbances problem.
[211] A K-SVD sparse representation approach is utilized to breakdown load profiles into linear combinations of various partial use patterns (PUPs), enabling the compression of smart meter data and the extraction of concealed power consumption patterns. On the basis of the recovered patterns, a linear support vector machine (SVM)-based technique is utilized to categorize the load profiles into two groups: residential consumers and small and medium-sized companies (SMEs).
[212] A hybrid neural network model for load forecasting that combines information entropy theory with ant colony clustering.
[213] A system for cleaning historical data, doing supervised learning, and accelerating the learning process in order to enable online state estimates.
[214] A security situational awareness mechanism based on the analysis of big data in the smart grid is seamlessly combined with a Fuzzy cluster-based analytical approach, game theory, and reinforcement learning to provide a security situational analysis for the smart grid.
[215] An online application for early event detection using the reduced dimensionality and phasor measurement unit data.
[216] By utilizing the Gaussian-Mixture model to cluster the historical data and learning minimum and maximum values or distance values to each center of individual clusters, we can obtain a narrower range of normal data compared to the scheme based on the Min-Max model, resulting in more accurate detection of false data.
[217] An energy theft detection technique for the energy privacy protection in the smart grid based on convolutional neural networks (CNN) to identify abnormal behavior of the metering data from a long-period pattern observation and a Paillier algorithm to safeguard the energy privacy.
[218] A review on cloud computing for smart grid applications
[219] A microgrid fault detection strategy based on the wavelet transform and deep neural networks.
[220] Applications as well as a comprehensive overview of the technological and regulatory obstacles and hazards associated with big data analytics in electricity distribution networks.
[221] A study of large data management and management processes to assist in managing grid data.
[222] A data-driven method for identifying and classifying faults in an electrical power system is comprised of an efficient analysis of the data using feature vectors including the region or zone of the bus and machine learning models to classify and identify the fault’s location.
[223] Analysis of the use of the Mamdani-model and the Sugeno-model in a fuzzy expert system for defect diagnostics based on the current condition of the power transformer.
[224] A survey of the uses of SVM modeling in the solar and wind energy industries.
[225] Extreme learning machine method for power system transient stability assessment problem.
[226] A phasor measurement unit-based robust state estimation method (PRSEM) for real-time monitoring of power systems under different operation conditions.
[227] A method for detecting power theft based on the Wide & Deep Convolutional Neural Networks (CNN) model.
[228] Application of micro-phasor measurement unit (PMU) data to power distribution network event detection using an unique data-driven event detection technique, namely Hidden Structure SemiSupervised Machine (HS3M).
[229] A short-term wind power prediction approach based on a support vector regression (SVR)-based local predictor (LP) with false neighbours filtered (FNF-SVRLP).
[230] Small sample-based classification of power quality disturbances using a hybrid method based on k-nearest neighbor and fully-convolutional Siamese network.
Table 4. Classification of the reviewed papers per category of Figure 6.
Table 4. Classification of the reviewed papers per category of Figure 6.
Big data analysis’ methods and techniques Definition References
Artificial intelligence Artificial intelligence refers to a collection of computing technologies that, when combined, give computers the ability to carry out a wide range of complex tasks such as the ability to read, comprehend, and translate both spoken and written language, as well as to analyze data and provide recommendations. [18,23,55,57,61,82,99,102,104,112,118,135,139,150,158,181,192,223]
Cloud computing Cloud computing refers to the on-demand access, via the internet, to computing resources that are hosted at a remote data center that is managed by a cloud services provider. These computing resources include applications, servers (both physical and virtual servers), data storage, development tools, and networking capabilities, among other things. [36,43,54,87,94,114,148,151,152,160,161,193,203]
Clustering Clustering, often known as cluster analysis, is a method of machine learning that organizes the dataset without labeling it. It is a method for organizing the data points into distinct clusters, each of which is made up of data points that are quite similar to one another. [34,58,62,68,75,79,89,90,91,95,109,111,118,121,126,130,146,153,161,166,167,172,175,180,183,184,187,191,204,212,214,216]
Data mining The technique of using big data sets to search for anomalies, patterns, and correlations in order to make accurate predictions is known as data mining. This information may be utilized to enhance revenues, lower expenses, improve customer connections, reduce risks, and a variety of other things by employing a wide variety of ways. [18,38,73,74,111,120,136,141,149,154,157,161,166,171,174,182,183,184,185,194,203,206,209,220,221]
Decision making The process of selecting choices by recognizing a decision to be made, collecting relevant information, and analyzing potential solutions is referred to as decision making. [11,155,156]
Deep learning Deep learning is a subclass of machine learning that comprises neural networks with three or more layers. These neural networks seek to imitate the activity of the human brain, although inadequately, allowing them to “learn” from vast quantities of data. [12,20,27,32,67,71,72,73,117,134,135,198,199,208,210,217,218,219,227,229]
Machine learning Machine learning is a subfield of artificial intelligence and computer science that focuses on the use of data and algorithms to emulate how people learn, while continually enhancing its accuracy. [23,28,32,33,37,45,52,57,59,60,78,81,84,85,88,97,99,104,105,106,107,108,110,115,122,123,124,125,128,129,135,136,138,140,142,144,159,163,165,168,169,170,172,175,176,178,183,185,191,196,198,200,201,202,207,208,209,213,214,222,224,225,227,228,229,230]
Mathematical programming Mathematical programming refers to the use of mathematical models to the solution of issues such as decision problems. The concepts are supposed to contrast with computer programming, which handles similar issues by creating methods that may be tailored to a particular situation. [17,29,35,40,44,47,48,49,56,64,70,83,92,100,103,113,119,137,140,143,164,205]
Neural networks Neural networks, commonly referred to as artificial neural networks or simulated neural networks, are a subset of machine learning and the foundation of deep learning methods. Their name and structure are derived from the human brain, emulating how organic neurons communicate with one another. [12,25,26,27,30,31,55,63,71,91,93,102,112,132,134,146,150,178,181,186,198,199,219,227,229]
Pattern recognition Pattern recognition is a mature yet interesting and rapidly expanding area that underlies advancements in related fields such computer vision, image processing, text and document analysis, and neural networks. It is similar to machine learning and has applications in rapidly developing fields. [13,89,91,98,102,153,175,194,206,211,212]
Signal processing Signal processing is the technology that enables the production, transformation, and interpretation of data. [15,16,22,33,39,46,51,127,131,133,145]
Statistical technique Statistical procedures include planning, designing, collecting data, analyzing, making relevant interpretations, and publishing the results of the research. The statistical analysis offers meaning to meaningless numbers and hence gives life to dead facts. [14,17,19,21,24,41,53,65,66,69,80,86,101,147,153,162,169,170,173,177,179,184,188,189,190,192,195,202,215,226]
Support vector machine It is a supervised machine learning model for two-group classification problems that employs classification techniques. Support vector machine models can classify fresh text after receiving labeled training data. [13,32,81,85,88,108,123,124,125,129,163,165,169,170,185,211,224]
Table 5. Classification of the reviewed papers per topic in smart grid.
Table 5. Classification of the reviewed papers per topic in smart grid.
Big data topics in smart grids References
Demand response [62,72,137,142,162,191]
Demand side management [11,76,84,111,146,150,167,168,169,172,194,204,206,207]
Economic load dispatch [47,48,49]
Electric vehicles [54,114,116,143,195]
Energy management [28,33,64,82,84,159]
Energy price [59,132,176,184]
Energy theft / Non-technical losses [32,81,112,117,123,125,129,157,163,166,217,227]
Generation planning [70]
Load forecasting [27,30,31,58,60,84,85,89,90,91,93,95,102,105,106,134,144,165,178,181,182,185,189,193,198,199,212]
Load pattern [68,180]
Microgrids [43,102,144,154,201,219]
Monitoring and control [25,28,38,39,40,42,50,66,69,71,80,82,138,184,203,213,226]
Power quality [13,15,17,67,147,170,210,230]
Power transformer [21,44,88,188,190,223]
Power system protection [154]
Power system stability [37,78,98,121,127,128,179,203,225]
Power system reliability / resilience [19,22,24,29,53,79,87,100,122,156,158,196,219,222,228]
Renewable energy sources [12,14,34,45,52,55,63,64,86,92,101,103,118,136,139,151,152,159,186,187,192,201,208,224,229]
Security / Cyber security [20,24,29,36,38,50,61,87,94,96,97,99,107,110,135,141,143,148,149,171,214,216]
Smart cities / buildings / home [18,26,32,95,115,124,126,140,175,181]
Smart meters / Smart metering [23,41,42,43,44,46,51,57,77,83,96,97,104,108,110,116,122,137,142,155,160,161,170,173,174,183,197,200,202,203,209,211,218,220,221]
Synchrophasor technology [16,39,40,65,66,69,73,74,75,78,104,109,113,119,120,121,128,130,131,133,145,153,164,177,205,215,226]
Topology identification [35,56,173]
Table 6. Comparison of the review works published in the literature with the present work.
Table 6. Comparison of the review works published in the literature with the present work.
Reference Chronological distribution Classification per journal or conference Methods and techniques Topics in smart grids Future works
[17] ‒ ‒ ✓ ✓ ✓
[23] ‒ ‒ ✓ ✓ ‒
[25] ‒ ‒ ✓ ✓ ✓
[28] ‒ ‒ ✓ ✓ ✓
[29] ‒ ‒ ✓ ✓ ‒
[33] ‒ ‒ ✓ ✓ ‒
[43] ‒ ‒ ✓ ✓ ✓
[46] ‒ ‒ ✓ ✓ ✓
[50] ‒ ‒ ✓ ✓ ✓
[68] ‒ ‒ ✓ ✓ ‒
[76] ‒ ‒ ✓ ✓ ‒
[77] ‒ ‒ ✓ ✓ ✓
[82] ‒ ‒ ✓ ✓ ✓
[87] ‒ ‒ ✓ ✓ ‒
[96] ‒ ‒ ✓ ✓ ✓
[104] ‒ ‒ ✓ ✓ ‒
[107] ‒ ‒ ✓ ✓ ✓
[116] ‒ ‒ ✓ ✓ ‒
[120] ‒ ‒ ✓ ✓ ✓
[135] ‒ ‒ ✓ ✓ ✓
[139] ‒ ‒ ✓ ✓ ‒
[156] ‒ ‒ ✓ ✓ ✓
[184] ‒ ‒ ✓ ✓ ✓
[197] ‒ ‒ ✓ ✓ ✓
[200] ‒ ‒ ✓ ✓ ✓
[208] ‒ ‒ ✓ ✓ ✓
[224] ‒ ‒ ✓ ✓ ✓
This work ✓ ✓ ✓ ✓ ✓
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