|Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions|
|Lennard R. Visser, Tarek A. AlSkaif, Wilfried G.J.H.M van Sark
Utrecht University, Utrecht, Netherlands
The increasing penetration of distributed renewable energy resources like PV-systems form a threat to reliable grid operation. PV-systems impede load balancing due to the intermittent and uncontrollable power production. The development of highly accurate forecasting techniques is essential to support a high PV penetration rate in the local electricity grid. This research examines the performance of different machine learning models that autonomously predict day-ahead power production of individual PV-systems in the electricity grid. The forecasting models are developed by considering historic power production and regional predictions of weather metrics. The method allows to generate site specific forecasting algorithms that inherently account for site characteristics including size, orientation and shading and is independent of such input. Initial results show that Gradient Boosting, Artificial Neural Network and Random Forest outperform the other machine learning models.