Version 1
: Received: 15 August 2019 / Approved: 17 August 2019 / Online: 17 August 2019 (04:11:44 CEST)
How to cite:
Faizollahzadeh ardabili, S.; Mosavi, A.; R. Várkonyi-Kóczy, A. Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. Preprints2019, 2019080180. https://doi.org/10.20944/preprints201908.0180.v1
Faizollahzadeh ardabili, S.; Mosavi, A.; R. Várkonyi-Kóczy, A. Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. Preprints 2019, 2019080180. https://doi.org/10.20944/preprints201908.0180.v1
Faizollahzadeh ardabili, S.; Mosavi, A.; R. Várkonyi-Kóczy, A. Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. Preprints2019, 2019080180. https://doi.org/10.20944/preprints201908.0180.v1
APA Style
Faizollahzadeh ardabili, S., Mosavi, A., & R. Várkonyi-Kóczy, A. (2019). Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities. Preprints. https://doi.org/10.20944/preprints201908.0180.v1
Chicago/Turabian Style
Faizollahzadeh ardabili, S., Amir Mosavi and Annamária R. Várkonyi-Kóczy. 2019 "Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities" Preprints. https://doi.org/10.20944/preprints201908.0180.v1
Abstract
Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.
Keywords
machine learning; smart cities; IoT; deep learning; big data; soft computing; sustainable urban development; building energy; energy demand and consumption; sustainable cities
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
