• 人工智能 •

### 基于LSTM神经网络的区域微网短期负荷预测

1. (山东建筑大学信息与电气工程学院,山东济南250101)
• 出版日期:2022-05-07 发布日期:2022-05-07
• 作者简介:尹春杰(1970—),男,山东潍坊人,副教授,博士,研究方向:智能微电网控制技术,电力电子与电力传动新技术,E-mail: yinchunjie@126.com; 通信作者:肖发达(1997—),男,山东临沂人,硕士研究生,研究方向:智能微电网控制技术,E-mail: 1004184668@qq.com; 李鹏飞(1995—),男,江苏南通人,硕士研究生,研究方向:智能微电网控制技术,E-mail: 2993061929@qq.com; 赵钦(1998—),男,山东菏泽人,硕士研究生,研究方向:智能微电网控制技术,E-mail: 2071440750@qq.com。
• 基金资助:
山东省自然科学基金面上项目(ZR2020MF070)

### Short-term Load Forecasting of Regional Microgrid Based on LSTM Neural Network

1. (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)
• Online:2022-05-07 Published:2022-05-07

Abstract: There are many studies on the load forecasting of large power grids and relatively few studies on microgrids. Therefore, it is very important to establish a suitable microgrid load forecasting model to improve the accuracy of forecasting. This paper analyzes and selects temperature, daily type, and multiple historical loads as the input variables of the model for the case of fewer input variables, selects the LSTM neural network based on the recurrent neural network for modeling, and constructs the load forecasting model of microgrid based on LSTM neural network. Finally, in order to enhance the reliability of the results, two sets of load data in different time periods are used to predict separately, and the prediction results of the LSTM neural network are compared with those of BP neural network, RBF neural network and Elman neural network. The experimental results show that the prediction results of LSTM neural network are better than BP neural network, RBF neural network and Elman neural network. The LSTM neural network load forecasting model has good promotion prospect under the background of microgrid.