基于小波去噪和决策树的个性化大用户负荷预测

Wavelet De-noising and Decision Tree Based Load Forecasting of Large Consumers

把握用户的用电规律并对用户未来的用电进行精准的预测对于开展需求响应、提高电网运行效率等具有重要意义。首先对大用户负荷特性进行了分析,指出大用户负荷具有量大面广、个性不一、近大远小、波动显著以及周期失灵等特性。然后针对这些特性,提出了一种基于小波去噪和决策树的个性化模式挖掘预测方法,能够挖掘大用户历史负荷数据进行模式提取,对不同用电模式的大用户分别进行个性化负荷预测。对某省50个典型大用户的算例分析结果表明该方法的准确性较其他常用预测方法更高。

Having a better understanding of the customs of consumers＇ electricity consumption and the accurate load forecasting of individual consumer are of great significance to demand response implement and high efficient power system operation. Firstly the load characteristics of large consumers are analyzed,and it is pointed out that the load profiles of large consumers have characteristics such as large in volume and wide in coverage,variant in load features,highly short-term correlative with historic load,notable in fluctuation,and without clear periodic characteristic. According to these characteristics,a wavelet de-nosing and decision tree based method for pattern extraction and load forecasting is proposed. The method mines data of consumer＇s historical load and extracts their utilization patterns,then personalizes loads forecasting of consumers based on different utilization patterns. Case studies on 50 typical large consumers in a province of China showthat the proposed method is superior to other forecasting methods in accuracy.