基于隐含数据信息挖掘的贝叶斯电采暖负荷预测

冬季电采暖负荷的准确预测对电网安全稳定运行以及电力系统调峰和调频具有重要意义。为充分挖掘冬季电采暖负荷数据中隐含信息,提出基于贝叶斯网络的多变量冬季电采暖负荷预测方法。首先将隐含信息数据中的多变量数据分为可观测数据和隐含数据,依据变量之间的影响机制搭建贝叶斯网络结构,并通过EM(Expectation Maximization Algorithm)算法训练可观测数据信息,获取隐含数据分布,进而基于可观测数据和隐含数据实现冬季电采暖负荷预测。采用北京电力公司提供的冬季电采暖负荷实测数据进行验证,结果表明,采用贝叶斯网络进行电采暖负荷预测具有较高的预测精度。

Bayes Heating Load Forecasting Based on Implicit Data Message Mining

The accurate prediction of heating load in winter is of great significance to the safe and stable operation of the power grid and the peaking and frequency regulation of the power system. In order to fully exploit the implicit information in winter heating load data, a multivariable winter heating load forecasting method based on Bayesian network is proposed. Firstly, the multivariable data in implicit information data is divided into observable data and implicit data. Bayesian network structure is built based on the influence mechanism between variables, and observable data information is trained by EM (Expectation Maximization Algorithm) to obtain hidden data distribution, and then realize heating load forecasting based on observable data and implicit data. Using the measured data of winter heating load in an area provided by Beijing Power Grid to verify, the results show that the use of Bayesian network for heating load forecasting has higher prediction accuracy.