基于径向基函数神经网络的综合能源系统多元负荷短期预测

Multiple short-term load forecasting in integrated energy system based on RBF-NN model

准确的能源负荷预测对综合能源系统的经济调度和优化运行有着重要的影响。提出一种基于径向基函数神经网络(radial basis function neural network,RBF-NN)模型的综合能源系统电、气、热多元负荷短期预测方法。首先利用Copula理论对电、气、热负荷进行相关性分析,建立了电、气、热负荷和温度的时间序列;接着设计RBF.NN网络模型结构,采用K.means聚类算法对隐含层节点进行优化;最后通过国内某园区综合能源系统的实际数据对模型进行验证。通过3个案例结果的比较,验证了文中提出的方法可以有效地考虑电、气、热负荷之间的耦合关系,具有较高的预测精度。

Accurate energy load prediction has a consider. able impact on the economic scheduling and optimal operation of in. tegrated energy system. Radial basis function neural network(RBF. NN)model is introduced to predict the short.term electric, gas and heating loads of integrated energy system. Firstly, the Copula theory is used to analyze the correlation of electric, gas and heating loads, and the time series model of electric, gas, heating loads and tempera. ture is established. Then, the structure of RBF.NN network is de. signed and K.means clustering algorithm is adopted to optimize the hidden layer nodes. Finally, the proposed model is verified by the practical data of an integrated energy system in a park in China. Through the comparison of three cases, it is verified that the method proposed in this paper can effectively consider the coupling relation. ship among electric, gas and heating loads, and improve the predic. tion accuracy.