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PV self-consumption prediction methods using supervised machine learning
KTH.
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0002-2603-7595
Number of Authors: 22022 (English)In: 2022 BuildSim Nordic, BSN 2022, EDP Sciences , 2022, article id 02003Conference paper, Published paper (Refereed)
Abstract [en]

The increased prevalence of photovoltaic (PV) self-consumption policies across Europe and the world place an increased importance on accurate predictions for life-cycle costing during the planning phase. This study presents several machine learning and regression models for predicting self-consumption, trained on a variety of datasets from Sweden. The results show that advanced ML models have an improved performance over simpler regressions, where the highest performing model, Random Forest, has a mean average error of 1.5 percentage points and an R2 of 0.977. Training models using widely available typical meteorological year (TMY) climate data is also shown to introduce small, acceptable errors when tested against spatially and temporally matched climate and load data. The ability to train the ML models with TMY climate data makes their adoption easier and builds on previous work by demonstrating the robustness of the methodology as a self-consumption prediction tool. The low error and high R2 are a notable improvement over previous estimation models and the minimal input data requirements make them easy to adopt and apply in a wide array of applications.

Place, publisher, year, edition, pages
EDP Sciences , 2022. article id 02003
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-333443DOI: 10.1051/e3sconf/202236202003Scopus ID: 2-s2.0-85146889141OAI: oai:DiVA.org:kth-333443DiVA, id: diva2:1785271
Conference
2022 BuildSim Nordic, BSN 2022, Copenhagen, Denmark, Aug 22 2022 - Aug 23 2022
Note

QC 20230802

Available from: 2023-08-02 Created: 2023-08-02 Last updated: 2023-08-02Bibliographically approved

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Tóth, MartosSommerfeldt, Nelson

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf