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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | AI-Powered Energy Demand Forecasting in Smart Grids |
| Authors Name: | Jatin Dhingra , Sahil Pandhi , Lokesh Kumar Pandhi , Manik Lath |
| Download E-Certificate: | Download |
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IJRTI_207548
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| Published Paper Id: | IJRTI2511089 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | Smart grids have become a radical solution in order to deal with the constraints of conventional power systems especially as urbanization increases, renewable is integrated and consumer demands that are increasingly becoming dynamic [1]. The conventional forecast approaches like the regression and time series model cannot reflect the nonlinear consumption trends related to the variations of the weather, the socio-economic conditions and the decentralized energy generation [2]. To ad- dress this, this paper evaluates Artificial Intelligence (AI)-based forecasting models namely Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Transformer-based models to short and long-term electricity demand prediction [3]. The work aims at fulfilling two tasks: (1) create precise short-term (minutes ahead to hours ahead) and long-term (weeks ahead to months ahead) energy demand predictions through machine learning (ML) strategies, and (2) to eliminate grid overloading, enhance reliability, and prioritize a smoother way of distributing energy [19]. The suggested model is based on smart meter data, weather datasets, and socio-demographic characteristics to make the ML models training. Benchmark datasets are performed and the results are compared with the models of traditional prerequisites: ARIMA and Prophet [4]. The RMSE, MAE, and MAPE are the grounds of performance evaluation, which reveal that the deep learning models significantly surpass the classical approaches [25]. The observed results emphasize the idea that AI-based forecasting can assist in providing decreased blackouts, allowing the efficient response of demand, and integrating the renewable energy sources into the ecosystem of a smart grid [28]. This study helps to create more sustainable, enduring as well as productive infrastructures of energy in the future |
| Keywords: | Smart Grid, Artificial Intelligence, Machine Learning, Energy Demand Forecasting, LSTM, Grid Efficiency, Renewable Integration. |
| Cite Article: | "AI-Powered Energy Demand Forecasting in Smart Grids", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a764-a770, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511089.pdf |
| Downloads: | 000226 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511089
Registration ID:207548
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a764-a770 Country: Mohali, Punjab, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511089 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511089 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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