Short-Term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm

Abstract

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) was proposed to reduce the probability that the Fruit Fly Optimization Algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models.

Keywords:

• short-term load forecasting
• convolutional neural network
• extreme learning machine
• adaptive mutation
• fruit fly optimization algorithm

Additional information

Funding

This work was supported by the National Key Research and Development Program of China (No. 2017YFB0902800).

Notes on contributors

Kai Sun

Kai Sun, received the B.Eng. from Faculty of Electrical Engineering, Taishan College of Science and Technology, Shandong University of Science and Technology in 2018, He received M.Sc. degree from Faculty of Electrical Engineering, Shandong University of Technology in 2021. His research interests include power system load forecasting and grid day ahead scheduling.

Zhenhai Dou

Zhenhai Dou, received M.Sc. degree from Faculty of Electrical Power Engineering, Shandong University of Technology. He received the Ph.D degree from Faculty of Electrical Engineering, China Agricultural University in 2002. His research interests include power system load forecasting and power system automation technology.

Bo Zhang

Zhang Bo received the M.Sc. degree from Faculty of Electrical Engineering, Shandong University of Technology in 2020. His research interests include power system load forecasting.

Hao Zou

Hao Zou received the M.Sc. degree from Faculty of Electrical Engineering, Shandong University of Technology in 2020. His research interests include power system load forecasting.

Shengtao Li

Shengtao Li received the M.Sc. He worked in Zibo Power Supply Bureau of State Grid Corporation of China and obtained the title of senior engineer. His research interest is mainly in power system load forecasting and dispatching technology.

Yaling Zhu

Yaling Zhu received the M.Sc. degree from Faculty of Electrical Engineering, Shandong University of Technology in 2021. Her research interests include power system load forecasting.

Qingling Liao

Qingling Liao received the M.Sc. degree from Faculty of Electrical Engineering, Shandong University of Technology in 2021. His research interests include power system load forecasting.