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Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems

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  • Danica Maljkovic

    (Energy Institute Hrvoje Požar, 10000 Zagreb, Croatia)

Abstract

Assessing the influential factors on measured (or allocated) heat consumption in district heating systems is often limited by the available data. Within a project of modelling consumption in district heating systems in Croatia for the Ministry of Environmental Protection and Environment, an access to a complete billing database of the largest Croatian district heating company was granted. The company supplies approximately 126,400 final consumers (both households and businesses) over 375 km of distribution network. The billing database has 40 vectors in a few million single inputs. In addition to these data, a questionnaire is distributed to the final consumers in several buildings labelled as “model buildings”, gathering behavioural and demographic data of final consumers (such as occupancy, mode of space usage, heat comfort level, age of occupants, etc.). The two sets of data are then merged, and a correlation analysis is performed. Furthermore, a two-step regression analysis is performed based on variables from billing database in the first step, with added behavioural and demographic variables obtained from the questionnaires in the second step. The models from two steps are compared, tested and interpreted. Results of the most influential factors on heat consumption in district heating systems are given and the influence of the behavioural/demographic variables on the prediction accuracy of heating consumption is interpreted.

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  • Danica Maljkovic, 2019. "Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems," Energies, MDPI, vol. 12(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:586-:d:205403
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    References listed on IDEAS

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    1. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    2. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    3. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    4. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
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