基于数据挖掘的楼宇短期负荷预测方法研究

楼宇短期负荷预测是楼宇能效管理系统中对用能子系统进行评估诊断、优化控制以及调度规划的重要基础。针对智能楼宇参与需求响应所需高精度、实时负荷信息的要求，提出一种基于数据挖掘支持向量机的楼宇短期负荷预测方法。选择与待预测时点相似相近的样本数据集，采用K-means算法对样本数据集中的温度、湿度、气压等气象数据进行聚类，根据聚类结果提取训练样本，最后采用支持向量机(SVM)算法建立负荷预测模型。实际应用结果表明，该方法预测结果平均相对误差为1.34%，相对误差在1%以内的概率达到67.5%，优于现有的时间序列法、同结构SVM法、不考虑气象因素的DMSVM法等方法。

Study of short-term load forecasting method based on data mining for buildings

The load forecasting is the important basis of the energy management systems for the evaluation and diagnosis, optimized control, and scheduling of the energy subsystems in buildings. In order to obtain real-time and high accuracy load information, this paper proposes a short-term load forecasting method based on data mining for buildings. It firstly finds the sample datasets that are similar to the forecasted time points from the historical data, and then performs the K-means cluster analysis on the meteorological data, such as temperature, humidity, barometric pressure, etc., and finally adopts the support vector machine (SVM) for short term forecasting. The practical application results prove that the eMAPE of the proposed method is 1.34%, and the probability of the relative error less than 1% is 67.5%, which are obviously better than that of the ARIMA, SVM and DMSVM without meteorological data.