English Abstract
Abstract:
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 shortterm 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
