Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction.

Accurate forecasting of photovoltaic (PV) generated electricity is essential for efficiently managing and integrating Renewable Energy (RE) into electricity distribution systems. This research investigation optimizes Feature Selection (FS) and prediction results for PV energy prediction by applying Bayesian Density Estimation (BDE) with Elastic Net (ELNET) regression analysis. This phenomenon and unacceptable outcomes are prevalent when applying conventional regression algorithms on datasets with significant results and addressing predictor multicollinearity. Improved FS and multicollinearity control has been rendered feasible by ELNET, which integrates the best features of Ridge and Lasso regression. ELNET eliminates these challenges through the implementation of L1 and L2 penalties. Non-parametric prediction Bayesian Density Estimation (BDE) is comprehensive data regarding residual distributions and predictor impacts. By incorporating ELNET's regularisation and FS abilities with BDE's statistical prediction and adaptability, the recommended ELNET-BDE is proposed to attain more accurate and reliable predictions. This technique has been used to assess massive data sets developing from Visakhapatnam, India, incorporating historical PV energy generation combined with definite Meteorological Factors (MF). Considering comprehensive data preliminary processing, FS, and validation, ELNET-BDE outperforms existing methods. Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). Compared with distinct FS techniques, RMSE can be minimized by up to 15% and MAE by up to 20%. The findings specify a substantial improvement in accuracy in prediction, emphasizing how the model can be used for improving solar power grid integration and energy for improved RE management.