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Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

COMPLECCiTY Lab, Department of Building, Civil and Environmental Engineering (BCEE), Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montréal, QC H3G 1M8, Canada
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Academic Editor: Hamid Reza Karimi
Received: 1 February 2021 / Revised: 30 March 2021 / Accepted: 2 April 2021 / Published: 6 April 2021
(This article belongs to the Section Aerospace, Vehicles, and Civil Engineering Design Automation)
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies. View Full-Text
Keywords: electricity load prediction; power grids; smart grids; recurrent neural networks (RNN); random forest; support vector machine (SVM) long short term memory (LSTM); deep learning; machine learning; non-linear auto-regressive exogenous (NARX) electricity load prediction; power grids; smart grids; recurrent neural networks (RNN); random forest; support vector machine (SVM) long short term memory (LSTM); deep learning; machine learning; non-linear auto-regressive exogenous (NARX)
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MDPI and ACS Style

Shirzadi, N.; Nizami, A.; Khazen, M.; Nik-Bakht, M. Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning. Designs 2021, 5, 27. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020027

AMA Style

Shirzadi N, Nizami A, Khazen M, Nik-Bakht M. Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning. Designs. 2021; 5(2):27. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020027

Chicago/Turabian Style

Shirzadi, Navid; Nizami, Ameer; Khazen, Mohammadali; Nik-Bakht, Mazdak. 2021. "Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning" Designs 5, no. 2: 27. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5020027

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