Enhance the Performance of Solar Irradiance Deep Learning Forecasting Model Using Recursive Estimation Method with Grid Search Algorithm

Authors

  • Gautam Kumar Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India
  • Sandip Kumar Goyal Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.4134

Keywords:

Synergistic, intrinsic mode function, solar irradiation, time step ahead, renewable energy

Abstract

This study presents a better way to predict solar irradiance by combining the Recursive Estimation Method for Signal Decomposition with Bidirectional Long Short-Term Memory (BiLSTM) networks that are made for predictive modeling. As solar power plays a bigger and bigger role in India’s green energy strategy, it is very important to be able to accurately predict solar irradiance in order to make the best use of resources. The suggested RE-BiLSTM framework does better than standalone models like LSTM, GRU, and BiLSTM, as well as the CEEMDAN-BiLSTM model hybrid, at different times of day (15 minutes, 30 minutes, and 60 minutes) and in different seasons (summer, monsoon, autumn, and winter). RMSE, MAE, and R2 are some of the evaluation metrics that show the proposed model consistently has lower error rates and higher predictive accuracy, especially at shorter time scales. Comparative analysis shows that the forecasting errors are more than 50% lower than those of the other models, which shows how strong the method is. These results suggest that the RE-BiLSTM model is a promising way to improve solar irradiance prediction and help India adapt solar power into its energy infrastructure.

 

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Author Biographies

Gautam Kumar, Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India

Gautam Kumar is a Research Scholar in the Department of Computer Science and Engineering at Maharishi Markandeshwar Engineering College, affiliated with Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India. His research focuses on solar irradiance forecasting using artificial intelligence and deep learning techniques. His areas of interest include machine learning, deep learning, time series analysis, and renewable energy systems.

Sandip Kumar Goyal, Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India

Sandip Kumar Goyal is having more than 20 Years of teaching experience at the level of Lecturer, Assistant Professor, Associate Professor, Professor at M. M. Engineering College, M. M. (Deemed To Be University), Mullana (Ambala) and currently working as Professor in CSE Department, MMEC, MM(DU), Mullana. He did Ph.D., M. Tech. & B. Tech. in the field of Computer Science & Engineering. His area of specialization includes Load Balancing in Distributed Systems, Internet of Things, Wireless Sensor Networks, Database Security, Software Engineering and Security in Cloud Computing. He has published more than 50 research papers in International Journal/Conference. More than 6 PhDs have been awarded under his supervision.

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Published

2026-06-04

How to Cite

Kumar, G. ., & Goyal, S. K. . (2026). Enhance the Performance of Solar Irradiance Deep Learning Forecasting Model Using Recursive Estimation Method with Grid Search Algorithm. Distributed Generation &Amp; Alternative Energy Journal, 41(03), 575–614. https://doi.org/10.13052/dgaej2156-3306.4134

Issue

2026: Vol 41 Iss 3

Section

Articles