Day-ahead Prediction of Building District Heat Demand for Smart Energy Management and Automation in Decentralized Energy Systems

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Abstract

This paper proposes an Artificial Intelligence (AI) based data-driven approach to forecast heat demand for various customer types in a District Heating System (DHS). The proposed day-ahead forecasting approach is based on a hybrid model consisting of Imperialistic Competitive Algorithm (ICA) and Support Vector Machine (SVM). The model is built using two years (2015 - 2016) of hourly data from various buildings in the Otaniemi area of Espoo, Finland. Day-ahead forecast models are also developed using Persistence and four other AI based techniques. Comparative forecasting performance analysis among these techniques was performed. The proposed ICA-SVM heat demand forecasting model is tested and validated using an out-of-sample one-year (2017) hourly data of the buildings’ district heat consumption. The prediction results are presented for the out-of-sample testing days in a one-hour time interval. The validation results demonstrate that the devised model is able to predict the buildings’ heat demand with an improved accuracy and short computation time. Moreover, the proposed model demonstrates outperformed prediction accuracy improvement, compared to the other five evaluated models.
Original languageEnglish
Title of host publicationProceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Subtitle of host publicationIndustrial Applications of Artificial Intelligence
PublisherIEEE
Pages1694-1699
Number of pages6
ISBN (Electronic)978-1-7281-2927-3
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Industrial Informatics - Aalto University, Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17
https://www.indin2019.org/

Publication series

NameIEEE International Conference on Industrial Informatics
PublisherIEEE
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X

Conference

ConferenceIEEE International Conference on Industrial Informatics
Abbreviated titleINDIN
CountryFinland
CityHelsinki-Espoo
Period22/07/201925/07/2019
Internet address

Keywords

  • SVM
  • ICA
  • District heating
  • Prediction
  • Energy efficiency
  • Energy management
  • AI
  • Machine learning
  • Building
  • Decentralized energy systems
  • Smart cities
  • Smart grid

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  • Cite this

    Eseye, A. T., Lehtonen, M., Tukia, T., Uimonen, S., & Millar, R. J. (2019). Day-ahead Prediction of Building District Heat Demand for Smart Energy Management and Automation in Decentralized Energy Systems. In Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019: Industrial Applications of Artificial Intelligence (pp. 1694-1699). (IEEE International Conference on Industrial Informatics). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972297