Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models

Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models

Abstract Accurate forecasting of renewable energy generation is critical for sustainable energy planning in rapidly urbanizing cities like Dhaka. This study conducts a comprehensive comparative analysis of classical time-series models ARIMA and SARIMA and a deep learning model LSTM for long-term com...

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Bibliographic Details
Main Authors: Mohammad Liton Hossain, S. M. Nasif Shams, Saeed Mahmud Ullah
Format: Article
Language:English
Published: Springer 2025-08-01
Series:Discover Sustainability
Subjects:
Renewable energy forecasting
Time-series analysis
ARIMA and SARIMA models
Long Short-Term Memory (LSTM)
Deep learning for energy systems
Urban energy planning
Online Access:https://doi.org/10.1007/s43621-025-01733-5
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https://doi.org/10.1007/s43621-025-01733-5

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