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A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

Maitanova, Nailya and Telle, Jan-Simon and Hanke, Benedikt and Grottke, Matthias and Schmidt, Thomas and Maydell, Karsten von and Agert, Carsten (2020) A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports. Energies, 13 (3). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en13030735. ISSN 1996-1073.

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Official URL: https://www.mdpi.com/635914


A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short‐term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee‐based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions.

Item URL in elib:https://elib.dlr.de/136405/
Document Type:Article
Additional Information:This paper is reprinted in a book "Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications". The reprint book will be listed in the Directory of Open Access Books (DOAB), Google Books and WorldCat. Link: https://www.mdpi.com/books/pdfview/book/2934 ISBN 978-3-03943-201-1 (PDF)
Title:A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Maitanova, NailyaUNSPECIFIEDhttps://orcid.org/0000-0003-1287-8139UNSPECIFIED
Telle, Jan-SimonUNSPECIFIEDhttps://orcid.org/0000-0001-6228-6815UNSPECIFIED
Hanke, BenediktUNSPECIFIEDhttps://orcid.org/0000-0001-7927-0123UNSPECIFIED
Agert, CarstenUNSPECIFIEDhttps://orcid.org/0000-0003-4733-5257UNSPECIFIED
Date:7 February 2020
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:photovoltaic power prediction; publicly available weather reports; machine learning; long short-term memory; integrated energy systems; smart energy management
HGF - Research field:Energy
HGF - Program:Technology, Innovation and Society
HGF - Program Themes:Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales
DLR - Research area:Energy
DLR - Program:E SY - Energy Systems Analysis
DLR - Research theme (Project):E - Energy Systems Technology (old)
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Maitanova, Nailya
Deposited On:20 Oct 2020 11:15
Last Modified:26 Oct 2020 15:20

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