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A Perfect Load Forecasting Strategy (PLFS) for Smart Grids Based on Artificial Intelligence
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: Received: 8 December 2023 / Approved: 8 December 2023 / Online: 8 December 2023 (12:42:36 CET)
A peer-reviewed article of this Preprint also exists.
Rabie, A.H.; I. Saleh, A.; Elkhalik, S.H.A.; Takieldeen, A.E. An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence. Technologies 2024 , 12 , 19.
Rabie, A.H.; I. Saleh, A.; Elkhalik, S.H.A.; Takieldeen, A.E. An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence. Technologies 2024, 12, 19.
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Rabie, A.H.; I. Saleh, A.; Elkhalik, S.H.A.; Takieldeen, A.E. An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence. Technologies 2024 , 12 , 19.
Rabie, A.H.; I. Saleh, A.; Elkhalik, S.H.A.; Takieldeen, A.E. An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence. Technologies 2024, 12, 19.
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Abstract
Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces a Perfect Load Forecasting Strategy (PLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed PLFS consists of two sequential phases, which are; Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selec-tion is done using Advanced Leopard Seal Optimization (ALSO) as a new natural inspired opti-mization technique,
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Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
While outlier rejection is accomplished through Interquartile Range (IQR) as a measure of statis-tical dispersion. On the other hand, actual load forecasting takes place in LFP using a new pre-dictor called; Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed PLFS has been tested through excessive experiments. Results have shown that PLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.
Keywords
Artificial Intelligence; Load Forecasting; Feature Selection; Outlier Rejection
Subject
Engineering, Other
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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