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

Study on load monitoring and demand side management strategy based on Elman neural network... 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/approachThe 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.FindingsThe 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/implicationsBecause of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.Practical implicationsThe paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.Originality/valueThis paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Circuit World Emerald Publishing

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

Circuit World , Volume 49 (1): 11 – Feb 6, 2023

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References (22)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0305-6120
eISSN
0305-6120
DOI
10.1108/cw-07-2021-0199
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsThe 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/implicationsBecause of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.Practical implicationsThe paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.Originality/valueThis paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

Journal

Circuit WorldEmerald Publishing

Published: Feb 6, 2023

Keywords: Elman neural network; Demand side management; Load monitoring; Sparrow search algorithm

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