A novel integration of Hodrick-Prescott filter (Hp-filter) and wavelet transform (WT) with optimize support vector machine (PSO-SVM) in predicting solar radiation.

Previous research has shown that predicting solar radiation is a challenging issue due to highly nonlinear and noisy climate data. Various hybrid approaches have been applied earlier for solar radiation prediction, which integrates the Wavelet Transform with various Machine Learning models. This research, therefore, intends to further improve the performance of these existing hybrid models. To address the limitations in handling nonlinear and noisy climate patterns, this study proposes a multi-hybrid model for accurately predicting solar radiation that incorporates the Hodrick-Prescott Filter (HP-Filter), Discrete Wavelet Transform (DWT), and Support Vector Machine (SVM). The collected data from the Bangladesh Meteorological Department for two different geological locations in Bangladesh, namely Dhaka and Chittagong, is divided into three categories for modeling: 70% for training, 15% for validation, and 15% for testing, whereas the model hyper-parameters of the SVM were optimized using the Particle Swarm Optimization algorithm. The proposed approach applies the Hodrick-Prescott Filter before analyzing DWT to strengthen the SVM model's ability to capture complicated climate patterns in great detail and also make the model more precise and reliable. Several performance metrics, such as Mean Squared Error (MSE), Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Coefficient of Determination (R