Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms

Abstract

With the growth of population, many countries face the challenge of supplying energy resources. One approach to managing and planning these resources is to predict energy demand. This study presented an integrated approach by applying six Machine Learning (ML) algorithms (ANN, AR, ARIMA, SARIMA, SARIMAX, and LSTM) and mathematical programming to predict energy demand in Iran up to 2040. The data relating to electricity generation and fuel consumption in power plants, electricity imports and exports, and seven major energy-consuming sectors in Iran (residential, commercial, industrial, transportation, public, agriculture, and others) are collected. The data employed to forecast energy demand in each sector with ML algorithms and prediction accuracy indices evaluated the algorithms' prediction accuracy in every sector. Then, the optimization model for prediction accuracy improvement is introduced. The ML algorithms results are employed as inputs to the integrated model and executed by two PSO and Grey-Wolf Optimizer algorithms for different sectors. The energy demand in these seven sectors until 2040 is predicted, and five prediction accuracy metrics are used to validate the integrated optimization results. The outcomes of the proposed method in all sectors reflect its more accurateness than ML algorithms, such that the MAPE index equals 0.002–0.012 and 0.004–0.013 for the proposed model executed by the PSO and Grey-Wolf Optimizer algorithms. In general, the PSO algorithm indicates a 75.65% growth in the total energy demand of all sectors, and the Grey-Wolf Optimizer algorithm forecasts a 82.94% growth.