Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model

Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model

This study explores the implementation of advanced machine learning techniques to enhance the integration of renewable energy into smart grids, focusing specifically on predicting solar power generation for the upcoming year. Three distinct machine learning models are employed: the Long Short-Term M...

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Bibliographic Details
Main Authors: Ahsan Zafar, Yanbo Che, Muneer Ahmed, Muhammad Sarfraz, Ashfaq Ahmad, Mohammad Alibakhshikenari
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Renewable energy
smart grids
power forecasting
bi-LSTM model
hybrid autoencoder LSTM model
Online Access:https://ieeexplore.ieee.org/document/10288460/
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