机器学习和套袋预测沙特阿拉伯中期电力消耗

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dhiaa Musleh, Maissa A. Al Metrik
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引用次数: 1

摘要

电被广泛认为是适应性最强的能源形式和主要的二次能源。然而,电力并不是经济上可储存的;因此,电力系统需要电力生产和消费的持续平衡才能保持稳定。准确可靠的电能消耗评估有助于规划未来的发电系统,以满足不断增长的电能需求。由于沙特阿拉伯是世界上最大的电力消费国之一,本文提出了沙特阿拉伯的电力消费预测模型。在这项工作中,作者获得了沙特阿拉伯十年来从未见过的电力消耗数据集。该数据集完全由作者从沙特电力公司(SEC)获得,它具有进一步的研究潜力,远远超过这项工作。该研究仔细检查了集成模型和K*模型的性能,作为预测沙特电力公司数据集中18个服务办事处每月用电量的新模型,并在新的用电量数据集上提供了实验。对K*算法的全局混合参数进行了调整,以达到预测电力消耗的最佳性能。K*模型具有较高的准确性,相关系数(CC)、平均绝对百分比误差(MAPE)、均方根百分比误差(RMSPE)、平均绝对误差(MAE)和均方根误差(RMSE)分别为0.9373、0.1569、0.5636、0.016和0.0488。所得结果表明,套袋集成模型优于独立的K*模型。它使用K*作为基本分类器的原始完整数据集,产生0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE和0.0484 RMSE。将本研究的结果与先前使用人工神经网络(ANN)对同一数据集的研究结果进行了比较,结果表明,与独立模型和套袋集合相比,本研究中使用的K*模型优于ANN模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
Electricity is widely regarded as the most adaptable form of energy and a major secondary energy source. However, electricity is not economically storable; therefore, the power system requires a continuous balance of electricity production and consumption to be stable. The accurate and reliable assessment of electrical energy consumption enables planning prospective power-producing systems to satisfy the expanding demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this paper proposed an electricity consumption prediction model in Saudia Arabia. In this work, the authors obtained a never-before-seen dataset of Saudi Arabia’s electricity consumption for a span of ten years. The dataset was acquired solely by the authors from the Saudi Electrical Company (SEC), and it has further research potential that far exceeds this work. The research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset. The global blend parameters for the K* algorithm were tuned to achieve the best performance for predicting electricity consumption. The K* model achieved a high accuracy, and the results of the correlation coefficient (CC), mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), mean absolute error (MAE), and root mean squared error (RMSE) were 0.9373, 0.1569, 0.5636, 0.016, and 0.0488, respectively. The obtained results showed that the bagging ensemble model outperformed the standalone K* model. It used the original full dataset with K* as the base classifier, which produced a 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The outcomes of this work were compared with a previous study on the same dataset using an artificial neural network (ANN), and the comparison showed that the K* model used in this study performed better than the ANN model when compared with the standalone models and the bagging ensemble.
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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