Electric load forecasting based on Long-Short-Term-Memory network via simplex optimizer during COVID-19
Xiaole Li, Yiqin Wang, Guibo Ma, Xin Chen, Qianxiang Shen, B. Yang
Abstract
Electric load forecasting is a challenging research, which is of great significance to the safe and stable operation of power grid in epidemic period. In this paper, Long-Short-Term-Memory (LSTM) model with simplex optimizer is proposed to forecast the electric load for an enterprise during the COVID-19 pandemic. The forecasting process consists of data processing, LSTM network construction and optimization. Firstly, some data processing steps includes information quantifying, electric load data cleaning, correlation-coefficient-based medical data filtering, clustering-based medical data and electric load data filling. Then LSTM-based electric load forecasting model of enterprise is established during the COVID-19 pandemic. On this basis, LSTM network is trained and parameters are optimized via simplex optimizer. Finally, an example of the electric load forecasting of an enterprise during the COVID-19 pandemic is investigated. The forecasting results show that the reduced number of iterations is about 25% and the improved forecasting accuracy is about 5.6%. These results can be used as a reference for resuming production of enterprises and planning of electric grid.