The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were primarily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and economic parameters, 8.33% on household lifestyle, 38.33% on historical energy consumption, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally.

中文翻译：

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电力负荷预测：系统回顾

每个国家的经济增长都与其电力基础设施、网络和可用性高度相关，因为电力已成为现代世界日常生活的核心部分。因此，全球住宅和商业用电需求出现了惊人的增长。另一方面，过去几年电价一直在波动，更不用说发电量不足以满足全球需求。作为对此的解决方案，许多研究旨在估计未来住宅和商业用途的电能需求，以使发电商、分销商和供应商能够提前有效规划并促进用户节能。虽然，负荷预测是电力行业自电力诞生以来面临的主要问题之一。当前的研究试图对九年来（2010-2020 年）在电力需求预测方面的学术期刊上报道的大约七十七 (77) 项相关的先前工作进行系统和批判性审查。具体而言，关注以下主题：（i）所使用的预测算法及其在该领域的拟合能力，（ii）影响电力消耗的理论和因素以及研究工作的起源，（iii）相关的准确性和误差应用于电力负荷预测的指标，以及 (iv) 预测期。结果显示，在电力预测中使用的前九个模型中，90% 是基于人工智能的，其中人工神经网络 (ANN) 占 28%。在此范围内，ANN 模型主要用于电能消耗模式复杂的短期电力预测。关于使用的准确度指标，据观察，均方根误差 (RMSE) (38%) 是电力预测人员中最常用的误差指标，其次是平均绝对百分比误差 MAPE (35%)。研究进一步显示，50% 的电力需求预测是基于天气和经济参数，8.33% 是基于家庭生活方式，38.33% 是基于历史能源消耗，3.33% 是基于股票指数。最后，我们回顾了进一步研究本地和全球电力负荷预测的挑战和机遇。关于使用的准确度指标，据观察，均方根误差 (RMSE) (38%) 是电力预测人员中最常用的误差指标，其次是平均绝对百分比误差 MAPE (35%)。研究进一步显示，50% 的电力需求预测是基于天气和经济参数，8.33% 是基于家庭生活方式，38.33% 是基于历史能源消耗，3.33% 是基于股票指数。最后，我们回顾了进一步研究本地和全球电力负荷预测的挑战和机遇。关于使用的准确度指标，据观察，均方根误差 (RMSE) (38%) 是电力预测人员中最常用的误差指标，其次是平均绝对百分比误差 MAPE (35%)。研究进一步显示，50% 的电力需求预测是基于天气和经济参数，8.33% 是基于家庭生活方式，38.33% 是基于历史能源消耗，3.33% 是基于股票指数。最后，我们回顾了进一步研究本地和全球电力负荷预测的挑战和机遇。研究进一步显示，50% 的电力需求预测是基于天气和经济参数，8.33% 是基于家庭生活方式，38.33% 是基于历史能源消耗，3.33% 是基于股票指数。最后，我们回顾了进一步研究本地和全球电力负荷预测的挑战和机遇。研究进一步显示，50% 的电力需求预测是基于天气和经济参数，8.33% 是基于家庭生活方式，38.33% 是基于历史能源消耗，3.33% 是基于股票指数。最后，我们回顾了进一步研究本地和全球电力负荷预测的挑战和机遇。