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Novel Intraday Photovoltaic Production Forecasting Algorithm Using Deep Learning Ensemble Models
S. Theocharides, G. Makrides, M. Theristis, G.E. Georghiou
Performance, Photovoltaic (PV), Artificial Neural Network, Forecasting, Power
PV Systems – Modelling, Design, Operation and Performance
Subtopic: Solar Resource and Forecasting
Event: 38th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5BV.4.7
1134 - 1137
ISBN: 3-936338-78-7
Paper DOI: 10.4229/EUPVSEC20212021-5BV.4.7
0,00 EUR
Document(s): paper


The high penetration level of photovoltaic (PV) systems to the utility grid and the intermittent nature of the generated power, introduces new challenges for the stability of electricity grids. The scope of this study is to present a novel intraday (up to 5-hours ahead forecasting) PV power production forecasting algorithm that is fully data-driven and based on machine learning ensemble principles. The methodology followed to develop the forecasting ensemble comprised of a cluster of Bayesian neural networks (BNN) that were trained with different exogenous variables, lag structures and estimation window duration. Preliminary obtained results demonstrated that the resulting hour-ahead forecasting ensemble model showed accuracies beyond the state-of-the-art (<3.3%). Finally, the implemented ensemble was able to forecast 5 points in time (5-hours ahead) with an accuracy <8%.