Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids

Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids

Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LS...

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Bibliographic Details
Main Authors: Muhammad Asghar Majeed, Sotdhipong Phichaisawat, Furqan Asghar, Umair Hussan
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Recurrent neural networks
load forecasting
temporal dependency modeling
long short-term memory
gated recurrent units
convolutional neural networks
Online Access:https://ieeexplore.ieee.org/document/11021601/
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https://ieeexplore.ieee.org/document/11021601/

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