Abstract
An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network.This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model.The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.
Highlights
Energy consumption in buildings accounts for up to 40% of the total energy consumption in the European Union (EU) [1]
B) Wide range of applicability: the multi variable model presented in this study aims to be valid for any type of building, regardless of the heating profile or final use, since the building stock connected to a District Heating (DH) network is usually made up of all kinds of building types
For the application assessed in this study, the high thermal inertia of the DH network could assume these fluctuations and, the analysis focuses on global energy results
Summary
Energy consumption in buildings accounts for up to 40% of the total energy consumption in the European Union (EU) [1]. The evolution of DH networks over the years has been reducing supply temperatures, originally in the range of 80 C and over, with the progressive implementation of the so-called 4th Generation District Heating (4GDH) ([5,6]) or Ultra Low Temperature (ULT) DH networks, which supply heat at temperatures around 45 C. This has enabled an increased integration of low grade energy sources such as solar thermal (ST) systems [7] or waste heat (WH) streams ([8e10]) in the heat network.
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