Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC)

Abstract Forecasting electricity consumption can help policymakers to properly plan for economic development. This is possible through energy conservation by avoiding excessive consumption of electricity through enhanced operational strategy. Power utilization and financial improvement are in long term relationship with all member nations of the Organization of Petroleum Exporting Countries (OPEC). In order to improve electricity consumption forecasting performance, this paper proposes an alternate machine learning method for forecasting OPEC electricity consumption with improved performance. The modeling of the OPEC electricity utilization forecast depends on the Cuckoo Search Algorithm by means of Levy flights. The proposed method is found to be efficient, operative, consistent, and robust compared to the electricity consumption forecasting methods that have already been discussed by researchers in the literature. In turn, energy conservation can be motivated in the twelve OPEC member countries.