College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Comput Intell Neurosci. 2022 Apr 30;2022:6909558. doi: 10.1155/2022/6909558. eCollection 2022.
With the consumption of new energy and the variability of user activity, accurate and fast demand forecasting plays a crucial role in modern power markets. This paper considers the correlation between temperature, wind speed, and real-time electricity demand and proposes a novel method for forecasting short-term demand in the power market. Kernel Support Vector Machine is first used to classify real-time demand in combination with temperature and wind speed, and then the temporal convolutional network (TCN) is used to extract the temporal relationships and implied information of day-ahead demand. Finally, the Gradient Boosting Regression Tree is used to forecast daily and weekly real-time demand based on electrical, meteorological, and data characteristics. The validity of the method was verified using a dataset from the ISO-NE (New England Electricity Market). Comparative experiments with existing methods showed that the method could provide more accurate demand forecasting results.
随着新能源的消耗和用户活动的可变性，准确快速的需求预测在现代电力市场中起着至关重要的作用。本文考虑了温度、风速和实时电力需求之间的相关性，并提出了一种用于预测电力市场短期需求的新方法。首先，使用核支持向量机结合温度和风速对实时需求进行分类，然后使用时间卷积网络（TCN）提取日前需求的时间关系和隐含信息。最后，基于电气、气象和数据特征，使用梯度提升回归树预测日度和周度实时需求。使用来自 ISO-NE（新英格兰电力市场）的数据集验证了该方法的有效性。与现有方法的对比实验表明，该方法可以提供更准确的需求预测结果。
