Study on load monitoring and demand side management strategy based on Elman neural network optimized by sparrow search algorithm

Abstract

Purpose This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal. Design/methodology/approach The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved. Findings The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM. Research limitations/implications Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further. Practical implications The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user. Originality/value This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.