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  • 1.
    Sandberg, Alexander
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, p. 3784-3790Article in journal (Refereed)
    Abstract [en]

    With the building sector standing for a major part of the world's energy usage it of utmost importance to develop new ways of reduce the consumption in the sector. This paper discusses the evolution of the regulations and policies of the Swedish electric and district heating metering markets followed by the development of a nonlinear autoregressive neural network with external input (NARX), with the purpose of performing heat demand forecasts for a commercial building in Sweden. The model contains 13 input parameters including; calendar, weather, energy and social behavior parameters. The result revealed that these input parameters can predict the building heat demand to 96% accuracy on an hourly basis for the period of a whole year. Further analysis of the result indicates that the current data resolution of the district heat measuring system limits the future possibilities for services compared to the electric metering system. This is something to consider when new regulation and policies is formulated in the future.

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