基于LSTM网络的分散式电采暖负荷仿真分析

Simulation analysis of distributed electric heating load based on LSTM network

中文关键词: 长短时记忆网络  分散式电采暖负荷  负荷建模  误差评价

英文关键词: long short-term memory network  distributed electric heating load  load modeling  error evaluation

中文摘要:

      基于等效热参数模型的分散式电采暖负荷存在参数识别困难、仿真误差大，不能满足电网调节需求的问题，为此提出基于长短时记忆网络的分散式电采暖负荷仿真模型。首先，根据分散式电采暖负荷与建筑物之间的传热过程，确定模型参数。然后，结合模型输入变量与输出变量确定长短时记忆网络参数，建立负荷模型。并通过对测试集输入数据中的室内温度进行动态更新，实现了长时间尺度的温度仿真。接着，为衡量模型的准确性，提出了纵向与横向两种维度上的模型误差评价指标。最后，算例分析结果表明，相比于分散式电采暖负荷二阶等效热参数模型，基于长短时记忆网络的分散式电采暖负荷模型仿真结果的纵向误差与横向误差更小，模型精度更高。

英文摘要:

      Decentralized electric heating load based on equivalent thermal parameter model faces difficulties in parameter identification and large simulation errors,which cannot meet the needs of power grid regulation. Therefore,a simulation model for decentralized electric heating load is proposed based on long short term memory（LSTM）network. Firstly,the model parameters are determined according to the heat transfer process between the distributed electric heating load and the building. Then the LSTM network parameters are determined by combining the model input variables and output variables to establish the load model. By dynamically updating the indoor temperature in the input data of the test set,the long-term temperature prediction is realized. In order to measure the accuracy of the model,model error evaluation indexes in both vertical and horizontal dimensions are proposed. Finally,the example analysis results show that compared with the second-order equivalent thermal parameter model of distributed electric heating load,the longitudinal error and lateral error of the prediction results of the distributed electric heating load model based on LSTM network are smaller and the model accuracy is higher.