Frey, Ulrich J. und Klein, Martin und Deissenroth, Marc (2019) Modelling complex investment decisions in Germany for Renewables with different machine learning algorithms. Environmental Modelling & Software. Elsevier. doi: 10.1016/j.envsoft.2019.03.006. ISSN 1364-8152.
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Kurzfassung
Investment decisions in renewable energies are known to be influenced by many diverse drivers, e.g. social, political, geographic, economic and psychological. Non-comprehensive models are problematic since missed interactions might introduce bias. We implement a robust modelling approach by (1) using a large data set with 1.4 million solar installations and (2) three different machine learning algorithms (deep neural networks, gradient boosting, random forests). Generalized linear models serve as baseline and comparison. A high prediction accuracy can be achieved on the county level with deep neural networks (adjusted R2 = 0.86) and gradient boosting (adjusted R2 = 0.87). The most important drivers are population per county, followed by type of urbanisation and social variables like unemployment, with varying degree of importance for the different machine-learning algorithms. Our approach points out both differences and agreements across methods and therefore a higher confidence in their interpretation
elib-URL des Eintrags: | https://elib.dlr.de/127150/ | ||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||
Titel: | Modelling complex investment decisions in Germany for Renewables with different machine learning algorithms | ||||||||
Autoren: |
*DLR corresponding author | ||||||||
Datum: | 2019 | ||||||||
Erschienen in: | Environmental Modelling & Software | ||||||||
Open Access: | Nein | ||||||||
In SCOPUS: | Ja | ||||||||
In ISI Web of Science: | Ja | ||||||||
DOI: | 10.1016/j.envsoft.2019.03.006 | ||||||||
Verlag: | Elsevier | ||||||||
ISSN: | 1364-8152 | ||||||||
Stichwörter: | investment decisions; solar installation; renewable energy; deep learning; machine learning; Germany | ||||||||
DLR - Schwerpunkt: | Energie | ||||||||
DLR - Forschungsgebiet: | E SY - Energiesystemanalyse | ||||||||
Standort: | Stuttgart | ||||||||
Institute & Einrichtungen: | Institut für Technische Thermodynamik > Systemanalyse und Technikbewertung |
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