Heat demand forecasting algorithm for a Warsaw district heating network

Teresa Kurek, Artur Bielecki, Konrad Świrski, Konrad Wojdan, Michał Guzek, Jakub Białek, Rafał Brzozowski and Rafał Serafin

Energy, 2021, vol. 217, issue C

Abstract: This paper presents a complex analysis of heat demand forecasting methods for the Warsaw District Heating Network, which is owned by Veolia Energia Warszawa, the largest district heating network (DHN) in the European Union. The analyzed network supplies heat for both domestic and heating purposes. Therefore, summer, intermediate, and winter seasons were delineated and separately evaluated. Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic). A 72 h forecast horizon is evaluated for total heat demand (the sum of all substations), as well as for groups of buildings (local models for specific city areas), and individually for the majority of substations. Models of areas use an additional input variable, namely, the results of the total heat demand forecast, and are proposed to be developed as an auxiliary information variable offered to grid operators. An artificial neural network based model achieves the best accuracy for all analyzed seasons. The intermediate seasons prove to be the most difficult to accurately forecast for and only the combination of summer and winter linear autoregresive models with utilization of a fuzzy logic reached comparable accuracy.

Keywords: Heat demand forecast; District heating; Fuzzy logic; Artificial neural networks; On-line forecast (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:217:y:2021:i:c:s0360544220324543

DOI: 10.1016/j.energy.2020.119347

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