NDLI: Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

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Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

Content Provider	Tech Science Press
Author	Kanagasabai, Kulothungan Kasinathan, Padmanathan Poongavanam, Elumalaivasan
Abstract	The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy policy. This energy demand will have to be fulfilled by an adequate mix of existing energy sources, considering the constraints imposed by future economic and social changes in the direction of a more sustainable world. Forecasting energy demand, on the other hand, is a tricky task because it is influenced by numerous micro-variables. As a result, an macro model with only a few factors that may be predicted globally, rather than a detailed analysis for each of these variables, is required. In this work, a hybrid approach is proposed for identifying the optimal generation-based mix of electricity systems in India. This approach is developed by combing Recalling-Enhanced Recurrent Neural Network (RERNN) and Giza Pyramids Construction (GPC). RERNN possesses selective memory features whereas GPC is a meta-heuristic algorithm that deals with different sets of problems. The goal of this approach is to assess the present load requirements and production profile to understand the current requirement and its production methods. Data on regards to electricity load profile involving the generation of all power plants, capacity factors and transmission limits were gathered from different sources including the Indian National Load Dispatch Centre, Central Electricity Authority database, annual reports of India and regional load dispatching centers. The proposal has introduced the RERNN technique to analyze the optimal time series whereas the GPC technique is involved in the optimization of RERNN parameters. The present work is simulated in MATLAB and the performance assessed with different parameters. The present technique gives an effective results in terms of accuracy, computational efficiency and feasibility.
Related Links	https://www.techscience.com/iasc/v36n1/49973
Starting Page	249
Ending Page	265
ISSN	10798587
DOI	10.32604/iasc.2023.030101
Issue Number	1
Journal	Intelligent Automation & Soft Computing (IASC)
Volume Number	36
e-ISSN	2326005X
Language	English
Publisher Date	2022-09-29
Access Restriction	Open
Subject Keyword	RERNN time series prediction Electricity generation demand GPC
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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