A Hybrid Bayesian Ridge Regression-CWT-Catboost Model For PV Power Forecasting

Abstract

The forecasting of the high intermittency of Photovoltaic (PV) energy in smart grid is a persisting challenge. The proposed paper takes this challenge by presenting accurate forecasting techniques. PV power forecasting contributes to energy sector stability, controllability, and utilization through systematic monitoring for proper energy operation and optimization of grid-load balance. This paper addresses a novel paradigm that effectively copes with unpredictable extreme meteorological conditions. The proposed technique combines the Bayesian Ridge Regression (BRR) model, Continuous Wavelet Transform (CWT), and Gradient boosting with categorical features (Catboost). The architecture of the proposed model is based on the acquisition of features inputs, which sorts those features according to their importance. This ranking deploys a Bayesian Ridge Regression model to select the most relevant features. Then, the CWT decomposition technique converts the features chosen into a time-frequency domain. Catboost model generates the forecast output for one day ahead. The final results are deduced using inverse CWT. The Australian weather data have been used to evaluate the performance of the proposed technique on short short-term power forecasting for large-scale PV plants. The evaluation has been conducted using score metrics, visualization curves, and to-fold cross-validation. Simulation results are conducted to confirm the performance of the proposed technique.