A Kernel-Based Approach For Photovoltaic Power Prediction in South Africa

Music City Center 

Photovoltaic (PV) solar power generation represents a viable option for meeting increased electricity demand. Accurate solar power predictions are crucial for feasibility studies of new installations and successful integration of PV systems into existing power grids. This need is particularly acute in South Africa, where the expansion of renewable energy capacity must be balanced against grid stability. This study applies kernel-based approaches to PV power prediction, focusing on capturing multi-scale temporal patterns in solar power generation. Using data from a large-scale PV installation in South Africa's Northern Cape region, the study investigates how kernel ridge regression can model both the inherent periodicity of solar power generation and its weather-dependent variations. The methodology addresses the complex interplay between weather patterns, seasonal variations, and power generation. The research contributes to the practical advancement of PV power prediction in renewable energy applications, with direct implications for grid integration and operational planning in regions with significant solar power installations.

Photovoltaic power prediction

kernel methods

machine learning

renewable energy

applied statistics 

Section on Statistics and the Environment