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Machine Learning Approach to a Low-Cost Day-Ahead Photovoltaic Power Prediction Based on Publicly Available Weather Reports for Automated Energy Management Systems

Maitanova, Nailya and Telle, Jan-Simon and Hanke, Benedikt and Schmidt, Thomas and Grottke, Matthias and von Maydell, Karsten and Agert, Carsten (2019) Machine Learning Approach to a Low-Cost Day-Ahead Photovoltaic Power Prediction Based on Publicly Available Weather Reports for Automated Energy Management Systems. In: EU PVSEC 2019. EU PVSEC 2019, 9.-13. Sep. 2019, Marseille, Frankreich.

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Abstract

The fully automated and transferable predictive approach based on the long short-term memory machine learning algorithm is developed for the forecasting of the photovoltaic (PV) power output. The main challenge of this approach is using publicly available weather reports and measured PV power without any technical information about the PV system. Nevertheless, the developed model is able to predict the power output of the various PV systems for all seasons with a reasonably good accuracy. This transferability of the approach is proven by the prediction of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The mean absolute scaled error of the predictions decreases with increasing the size of training set and reaches its minimum by the training with 90 days. The PV power prediction made with the publicly available weather data is compared to the predictions made with the fee based solar radiation data. The usage of the solar radiation data leads to more accurate predictions even with small training sets. Although the model with the publicly available data needs a greater training set, it still can make reasonably good predictions. Therefore, it can be applied in forecast-based energy management systems.

Item URL in elib:https://elib.dlr.de/129281/
Document Type:Conference or Workshop Item (Speech)
Title:Machine Learning Approach to a Low-Cost Day-Ahead Photovoltaic Power Prediction Based on Publicly Available Weather Reports for Automated Energy Management Systems
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Maitanova, NailyaNailya.Maitanova (at) dlr.deUNSPECIFIED
Telle, Jan-SimonUNSPECIFIEDUNSPECIFIED
Hanke, BenediktUNSPECIFIEDUNSPECIFIED
Schmidt, ThomasUNSPECIFIEDUNSPECIFIED
Grottke, MatthiasUNSPECIFIEDUNSPECIFIED
von Maydell, KarstenKarsten.Maydell (at) dlr.deUNSPECIFIED
Agert, Carstencarsten.agert (at) dlr.dehttps://orcid.org/0000-0003-4733-5257
Date:2019
Journal or Publication Title:EU PVSEC 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:PV power prediction, publicly available weather reports, machine learning, long short-term memory, integrated energy systems, smart energy management
Event Title:EU PVSEC 2019
Event Location:Marseille, Frankreich
Event Type:international Conference
Event Dates:9.-13. Sep. 2019
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), E - Systems Analysis and Technology Assessment (old)
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Maitanova, Nailya
Deposited On:28 Oct 2019 17:47
Last Modified:07 Apr 2020 15:24

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