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Title:
 
Photovoltaic Energy Yield Prediction Using an Irradiance Forecast Model Based on Machine Learning for Decentralized Energy Systems
 
Author(s):
 
S. Wendlandt, F. Popescu
 
Keywords:
 
Energy Yield, Energy Management, Forecast
 
Topic:
 
PV Applications and Integration
Subtopic: PV Driven Energy Management and System Integration
Event: 36th European Photovoltaic Solar Energy Conference and Exhibition
Session: 6CV.1.6
 
Pages:
 
1860 - 1864
ISBN: 3-936338-60-4
Paper DOI: 10.4229/EUPVSEC20192019-6CV.1.6
 
Price:
 
 
0,00 EUR
 
Document(s): paper
 

Abstract/Summary:


Over the past few years electricity generation costs for PV technology have dropped massively. Since, at the same time, PV module efficiencies have increased significantly, the market for building-applied PV systems has dramatically changed and in many countries it has become a de facto standard to use PV as the main source for the building´s energy needs. Because the power output of PV systems is fluctuating along with solar irradiation, advanced energy storage and management systems are necessary to cover the building energy demand on a stable basis. This paper presents a novel ‘gray-model’ approach to the estimation the forecast of PV energy systems. It is based on machine learning for solar irradiance forecasting and physical-mathematical models to simulate the PV system itself. The paper presents a comparison between simulated and real-life energy production data of a sample PV system.