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.

中文翻译：

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基于机器学习预测电力消耗以提高性能：石油输出国组织 (OPEC) 的案例研究

摘要 预测用电量有助于决策者合理规划经济发展。这可以通过加强运营策略避免过度消耗电力来实现节能。电力利用和财政改善与石油输出国组织（欧佩克）的所有成员国有着长期的关系。为了提高电力消耗预测性能，本文提出了一种替代机器学习方法来预测具有改进性能的欧佩克电力消耗。欧佩克电力利用预测的建模依赖于通过 Levy 飞行的布谷鸟搜索算法。发现所提出的方法是有效的、可操作的、一致的、与文献中研究人员已经讨论过的电力消耗预测方法相比，它是稳健的。反过来，可以在 12 个欧佩克成员国推动节能。