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Volume 13 | Issue 7 | Year 2026 | Article Id. IJEEE-V13I7P106 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I7P106

Solar Power Forecasting Using Deep Learning Model Based Improved Particle Swarm Optimization Method

Dantuluru Venkata Satya Ravi Varma, Ajaya Kumar Parida, Manas Ranjan Nayak, Raj Kumar Parida

Received Revised Accepted Published
02 Apr 2026 18 May 2026 19 Jun 2026 27 Jul 2026

Citation :

Dantuluru Venkata Satya Ravi Varma, Ajaya Kumar Parida, Manas Ranjan Nayak, Raj Kumar Parida, "Solar Power Forecasting Using Deep Learning Model Based Improved Particle Swarm Optimization Method," International Journal of Electrical and Electronics Engineering, vol. 13, no. 7, pp. 113-124, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I7P106

Abstract

Accurately forecasting solar power is essential for secure and efficient operations of contemporary power systems, particularly in the smart grid and renewable energy integration context. In this paper, we present a new hybrid deep learning model, the BiLSTM Model using Improved PSO (BiLSTM-IPSO), for improved short-term prediction of solar power, which incorporates the Bidirectional Long Short-Term Memory (BiLSTM) model with an Improved Particle Swarm Optimization (IPSO) algorithm. The IPSO algorithm successively arranges the BiLSTM hyperparameters with chaotic initialization, adaptive inertia weights, and velocity clamping, to converge globally and avoid stagnating locally. The proposed BiLSTM-IPSO model performance is compared with various state-of-the-art techniques, including TLBO-DL, CNN-LBO, BiLSTM-AADC, and HCLN, using standard indicators, such as MSE, RMSE, MAE, MBE, and R². Results show that our model consistently performs better than all the baselines, and the lowest RMSE and the highest R² are 0.0565 and 0.955, respectively, on the 0.5-hour horizon. The excellent generalization performance of the framework in all of its forecast horizons indicates its feasibility for applications in real-time deployment in solar energy management systems and smart-grid operations.

Keywords

Solar Power Prediction, Deep Learning Methods, MAE, BiLSTM, IPSO.

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