Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management

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Abstract

The electricity consumption profile of buildings are different from the typical load curves that represent the electricity consumption of large systems at the national or regional level. The electricity demand in buildings is many times lower than the region- or nation-wide demands. It is also much more volatile and stochastic, meaning that the conventional tools are not effective enough for straightforward application at a building demand level. In this paper, an integrated approach consisting of Hilbert-Huang Transform (HHT), Regrouping Particle Swarm Optimization (RegPSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is devised for 24h-ahead prediction of electric power consumption in buildings. The forecasts are used as input information for smart decisions of distributed energy management systems that control the optimal bidding and scheduling of energy resources for building energy communities. The effectiveness of the proposed forecasting approach is demonstrated using actual electricity demand data from various buildings in the Otaniemi area of Espoo, Finland. The prediction performance of the proposed approach for various building types (energy customer clusters), has been examined and statistical comparisons are presented. The prediction results are presented for future days with a one-hour time interval. The validation results demonstrate that the approach is able to forecast the buildings’ electricity demand with smaller error, outperforming five other approaches, and in reasonably short computation times.
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
Pages1103-1110
Number of pages8
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

  • AI
  • ANFIS
  • Building
  • Electricity demand
  • Energy management
  • Feature extraction
  • Forecasting
  • HHT
  • Machine learning
  • Parameter optimization
  • RegPSO

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  • Cite this

    Eseye, A. T., Lehtonen, M., Tukia, T., Uimonen, S., & Millar, R. J. (2019). Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management. In Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019: Industrial Applications of Artificial Intelligence (pp. 1103-1110). (IEEE International Conference on Industrial Informatics). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972188