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Intra-Day Solar Irradiance Forecasting for PV Power Generation Utilising Machine Learning Models
S. Theocharides, G. Makrides, M. Theristis, G.E. Georghiou
Photovoltaic (PV), Artificial Neural Network, Forecasting, Power
PV Systems and Storage – Modelling, Design, Operation and Performance
Subtopic: Solar Resource and Forecasting
Event: 36th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5DO.2.6
1433 - 1438
ISBN: 3-936338-60-4
Paper DOI: 10.4229/EUPVSEC20192019-5DO.2.6
0,00 EUR
Document(s): paper


Accurate PV production forecasting is an important feature that can assist utilities and plant operators in the direction of energy management and dispatchability planning. In this work, intra-day (1 to 3 hour-ahead) solar irradiance forecasting utilising Support Vector Machines for Regression (SVR) is derived in order to feed to an Artificial Neural Network (ANN) trained for PV power generation forecasting (1 to 3 hours ahead). This study focused on the improvement of intra-day PV power forecasting through improved solar irradiance forecasting by leveraging data-driven machine learning models that could record the solar irradiance profile and the behaviour of the PV system. The bestperforming models comprised of 3 parameters for the solar irradiance forecasting in-plane global irradiance (GI), elevation angle () and azimuth angle (s)) and 4 parameter the PV power forecasting model. (GI, ambient temperature (Tamb), and s). In addition, the results obtained over the test set period demonstrated that the intra-day PV power forecasting demonstrated a daily-normalised root mean square error (nRMSE) of 3.52% to 7.84% (solar irradiance forecasting nRMSE was 2.93% to 6.52%) indicating that both models have recorded the behaviour of their respective parameters.