Abstract
Forecasting the electricity demand is crucial for efficient planning, management, and conservation towards intelligent power systems. This study investigates the yearly electricity consumption of the Moroccan residential sector from 1990 to 2020. Two different time series forecasting methods are implemented and the best one is selected to predict the electricity consumption for the coming years. The performance of the Holts Linear Trend (HLT) method is compared to those of the Autoregressive Integrated Moving Average (ARIMA) Model. The outcome analysis shows that the similarities in prediction were high; meanwhile, the HLT performs better than ARIMA and predicts the data with more accuracy; lowest Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Square Error (MSE) compared to ARIMA model. The forecasted results based on HLT model show that the Moroccan residential electricity consumption will remain increasing by an average annual rate of 2.4% in the following 10 years. In 2030, Morocco will consume around 1253 Ktoe of electricity in the residential sector. This paper has policy implications that can be deployed for optimal planning in the electricity sector.
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Hammou Ou Ali, I., Jamii, M., Ouassaid, M., Maaroufi, M. (2022). Predicting the Residential Energy Consumption in Morocco Based on Time Series Forecasting Models. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_8
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