A Neural Network Based Approach for the Short-term Forecasting of Electricity Market Price

Gyan singh; Dr. Deena Lodwal Yadav

doi:10.36948/ijfmr.2023.v05i03.3394

A Neural Network Based Approach for the Short-term Forecasting of Electricity Market Price

Author(s)	Gyan singh, Dr. Deena Lodwal Yadav
Country	India
Abstract	In this study, an improved cascaded neural network is utilized to determine a Marketing Clearing Price (MCP) for energy in the wholesale market in Russian. Research on MCP prediction has attracted a lot of attention in recent years. This study recommends a unique approach based on a customized back propagation algorithm for retraining, testing, and testing on pricing over many months. This research contributed to the development of the methodology. MATLAB R 2015a (8.1.0.602) based software and simulation will be used to carry out the suggested action. As part of the strategy framework, we provide an improved approach that can reduce the load time while also reducing the MAPE, MSE, and RMSE. The proposed method's end goal is to forecast the energy market clearing price. The MAPE of our proposed technique is the smallest of all that have been reported in the literature. The calculated MAPE for the proposed strategy is 1.9%. The proposed method may be used in a number of different types of electric boards to provide clean, stable electricity
Keywords	Electricity Marketing; Machine Learning; Clearing Price Prediction.
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-09
Cite This	A Neural Network Based Approach for the Short-term Forecasting of Electricity Market Price - Gyan singh, Dr. Deena Lodwal Yadav - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.3394
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.3394
Short DOI	https://doi.org/gscdr3

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A Neural Network Based Approach for the Short-term Forecasting of Electricity Market Price

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