Energy Forecasting Information System of Optimal Electricity Generation using Fuzzy-based RERNN with GPC

• Elumalaivasan Poongavanam,
• Padmanathan Kasinathan,
• Karunanithi Kandasamy,
• S. P. Raja,

Vol. 17, No. 10, October 31, 2023
10.3837/tiis.2023.10.006, Download Paper (Free):

• Energy Demand
• Electricity Generation
• Fuzzy based RERNN
• GPC
• Information System
• Time Series

Abstract

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposedmethod was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

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