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Title:
 
Intra-Day Solar Irradiance Forecasting for PV Power Generation Utilising Machine Learning Models
 
Author(s):
 
S. Theocharides, G. Makrides, M. Theristis, G.E. Georghiou
 
Keywords:
 
Photovoltaic (PV), Artificial Neural Network, Forecasting, Power
 
Topic:
 
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
 
Pages:
 
1433 - 1438
ISBN: 3-936338-60-4
Paper DOI: 10.4229/EUPVSEC20192019-5DO.2.6
 
Price:
 
 
0,00 EUR
 
Document(s): paper
 

Abstract/Summary:


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.