Daily Load Forecasting of Electric Power Manufacturing Industry Considering Disaster Weather Recognition Under the Deep Learning

Abstract

At present, the power load of large power users such as electric power manufacturing enterprises is greatly affected by abnormal factors, among which the weather factor is one of the important influencing factors. How to accurately forecast the load level by considering weather factors is of great significance. This paper uses cluster analysis to screen out similar days that are severely affected by weather from the load data throughout the year. And a deep learning forecasting model that considers weather factors is built to realize the daily load forecast of electric power manufacturing enterprises. The realization of this research is helpful to provide accurate load forecasting methods for electric power manufacturing enterprises. The production plans according to weather conditions can be adjusted and the risks can be avoided, which can improve production efficiency.