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Load Forecasting in an Office Building with Different Data Structure and Learning Parameters

Author

Listed:
  • Daniel Ramos

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
    Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal)

  • Mahsa Khorram

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
    Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal)

  • Pedro Faria

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
    Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal)

Abstract

Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns.

Suggested Citation

  • Daniel Ramos & Mahsa Khorram & Pedro Faria & Zita Vale, 2021. "Load Forecasting in an Office Building with Different Data Structure and Learning Parameters," Forecasting, MDPI, vol. 3(1), pages 1-14, March.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:15-255:d:521101
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    References listed on IDEAS

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    1. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
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    7. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
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    Cited by:

    1. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    2. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).
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    4. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

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