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Title:
 
Optimising Own PV Consumption with PV Energy Yield Predictions from Machine Learning Algorithms and Weather Data
 
Author(s):
 
H. Heck, A. Schmidt, E. Schüpbach, F. Kuonen, S. Bacha, U. Muntwyler
 
Keywords:
 
Prediction, Machine Learning, Weather Data, PV Energy Yield
 
Topic:
 
PV Applications and Integration
Subtopic: PV Driven Energy Management and System Integration
Event: 37th European Photovoltaic Solar Energy Conference and Exhibition
Session: 6BV.5.4
 
Pages:
 
1796 - 1801
ISBN: 3-936338-73-6
Paper DOI: 10.4229/EUPVSEC20202020-6BV.5.4
 
Price:
 
 
0,00 EUR
 
Document(s): paper
 

Abstract/Summary:


Weather data are evaluated in view of their influence on high-quality PV energy yield predictions based on machine learning algorithms (MLAs). Optimisation experiments evidence that the prediction quality can be increased to over 30% by incorporating specific weather parameters in the ML-training. The results will feed into a planning tool for optimising the own consumption (including in wintertime) of PV plant owners. The outcome of this study also illustrates evolving best practice in using meteorological data to produce PV energy yield predictions with specific MLAs.