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Sadheesh Kumar, S. J., K, Navin. (1404). Next-Gen Solar Forecasting: PSO-Optimized Bayesian LSTM for Enhanced Accuracy. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2025.16360.2266
S. J. Sadheesh Kumar; Navin sam K. "Next-Gen Solar Forecasting: PSO-Optimized Bayesian LSTM for Enhanced Accuracy". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22098/joape.2025.16360.2266
Sadheesh Kumar, S. J., K, Navin. (1404). 'Next-Gen Solar Forecasting: PSO-Optimized Bayesian LSTM for Enhanced Accuracy', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2025.16360.2266
Sadheesh Kumar, S. J., K, Navin. Next-Gen Solar Forecasting: PSO-Optimized Bayesian LSTM for Enhanced Accuracy. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22098/joape.2025.16360.2266

Next-Gen Solar Forecasting: PSO-Optimized Bayesian LSTM for Enhanced Accuracy

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 06 آذر 1404    اصل مقاله (852.48 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.16360.2266
نویسندگان
S. J. Sadheesh Kumar* ؛ Navin sam K
Department of Electrical and Electronics Engineering, National Institute of Technology Puducherry, Karaikal, India.
چکیده
Accurate solar photovoltaic power (SPVP) generation forecasting is vital for integrating solar energy into the power grid. This paper presents an advanced forecasting model using a Bayesian enhanced Long Short-Term Memory neural network (BLSTM NN) model optimized by the Particle Swarm Optimization (PSO) algorithm to elevate the accuracy and reliability of SPVP generation forecasting. The hyperparameters of the BLSTM NN are optimized using the PSO algorithm, resulting in improved forecasting performance. The model is evaluated using a comprehensive dataset comprising five years of historical data from a 1 MW SPVP plant in South India, sampled at 15-minute intervals. Key performance indicators, including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared, are used for various analyses. The performance of the proposed model is evaluated through accuracy, uncertainty, scalability, and sensitivity analyses. Experimental results highlight a 16.02% reduction in RMSE, a 22.84% reduction in MAPE, and a 24\% improvement in R² over the conventional baseline DB model. The outcomes underscore the capability of the proposed model to deliver superior forecasting accuracy. The approach helps integrate reliable and efficient solar power into grid planning.        
کلیدواژه‌ها
Bayesian LSTM NN model؛ forecasting؛ hyperparameter optimization؛ particle swarm optimization؛ solar photovoltaic power؛ uncertainty quantification
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