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

• Published in: Designs 2021-06, Vol.5 (2), p.27
• Main Authors: Shirzadi, Navid, Nizami, Ameer, Khazen, Mohammadali, Nik-Bakht, Mazdak
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Climate change; Data mining; Decision trees; Deep learning; Distributors; Electric power demand; Electric power grids; Electrical loads; Electricity; Electricity consumption; Electricity distribution; electricity load prediction; Energy consumption; Energy management; Energy technology; Feasibility studies; Forecasting; Households; Machine learning; Mathematical models; Municipalities; Neural networks; power grids; random forest; Recurrent neural networks; recurrent neural networks (RNN); Renewable resources; Smart grid; smart grids; support vector machine (SVM) long short term memory (LSTM); Support vector machines; Wind speed
• ISSN: 2411-9660; 2411-9660
• DOI: 10.3390/designs5020027

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.