电价预测
电力市场
电
支持向量机
计量经济学
背景(考古学)
平均绝对百分比误差
计算机科学
回归
人工神经网络
人工智能
经济
统计
工程类
数学
生物
电气工程
古生物学
作者
Mahmood Hosseini Imani,Ettore Francesco Bompard,Pietro Colella,Tao Huang
出处
期刊:IEEE Transactions on Industry Applications
[Institute of Electrical and Electronics Engineers]
日期:2021-09-21
卷期号:57 (6): 5726-5736
被引量:4
标识
DOI:10.1109/tia.2021.3114129
摘要
Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices plays a crucial role. The more accurate the prediction is, the lower the market risk is. In this article, several machine learning algorithms (support vector machine, Gaussian processes regression, regression trees, and multilayer perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including mean absolute error, R-index, mean absolute percentage error, and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and tree-based models outperform other models at different time horizons.
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