摘要
随着电力系统的飞速发展,以往基于小规模数据的传统电力负荷预测算法可能无法容纳大量数据集和效率上的要求。为改善预测模型的工程实用性、效率和准确度,将传统的时间序列预测方法与机器学习中的支持向量机算法相结合,应用于短时电力负荷预测。即使用某一时刻对应的以往时间点的电力数据作为属性值,使用支持向量回归构建模型进行预测。通过实践证明,模型可以快速有效地处理数据,并且具有较高的预测准确度。
With the rapid development of power systems,traditional power load forecasting algorithms based on small-scale data in the past may not be able to accommodate large data sets and efficiency requirements.In order to improve the engineering practicability,efficiency and accuracy of the forecasting model,the traditional time series forecasting method is combined with the support vector machine algorithm in machine learning and applied to short term power load forecasting.That is,the power data corresponding to a certain point in the past is used as the attribute value,and the support vector regression is used to construct a model for prediction.Practice has proved that the model can process data quickly and effectively,and has high prediction accuracy.
作者
叶远胜
张静
YE Yuansheng;ZHANG Jing(China University of Mining and Technology-Beijing,Beijing 100083,China)
出处
《现代信息科技》
2020年第24期17-19,共3页
Modern Information Technology
关键词
时间序列
支持向量机
电力负荷预测
机器学习
time series
support vector machine
power load forecasting
machine learning
