Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian...

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Vydáno v:Energies (Basel) Ročník 15; číslo 9; s. 3425
Hlavní autoři: Sultana, Nahid, Hossain, S. M. Zakir, Almuhaini, Salma Hamad, Düştegör, Dilek
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2022
Témata:
Accuracy
Artificial intelligence
Bayesian optimization algorithm
Electricity
electricity demand
Energy consumption
Forecasting
GDP
Genetic algorithms
Gross Domestic Product
Literature reviews
Machine learning
NARX
Neural networks
Optimization algorithms
SARIMAX
short-term forecast
Statistical analysis
Teaching
Canada
Ontario Canada
Pakistan
ISSN:1996-1073, 1996-1073
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Shrnutí:This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15093425