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...

وصف كامل

التفاصيل البيبلوغرافية

الحاوية / القاعدة:	Discover Sustainability
المؤلفون الرئيسيون:	Mohammad Liton Hossain, S. M. Nasif Shams, Saeed Mahmud Ullah
التنسيق:	مقال
اللغة:	الإنجليزية
منشور في:	Springer 2025-08-01
الموضوعات:	Renewable energy forecasting Time-series analysis ARIMA and SARIMA models Long Short-Term Memory (LSTM) Deep learning for energy systems Urban energy planning
الوصول للمادة أونلاين:	https://doi.org/10.1007/s43621-025-01733-5