期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 86, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2020.106737
关键词
The organization of the petroleum exporting countries (opec); Electricity consumption; Energy Conservation; Cuckoo Search Algorithm; Machine Learning; Levy Flights
类别
资金
- Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, KPK, Pakistan
- Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program
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. (C) 2020 Elsevier Ltd. All rights reserved.
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