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The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final users ́ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating & Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R values above 0.9 in some of them.

Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques