Examinando por Autor "Zarrella, Angelo"

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  • Miniatura
    Ítem
    A data-driven model for the analysis of energy consumption in buildings
    (EDP Sciences, 2024-05-07) Borgato, Nicola; Prataviera, Enrico; Bordignon, Sara; Garay Martínez, Roberto; Zarrella, Angelo
    Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregation algorithm is proposed to distinguish space heating from domestic hot water usage. The method also evaluates the building's thermal inertia, examining the correlation between the hourly global energy consumption and the outdoor air temperature moving average. It also presents a methodology for improving the DHW heat consumption model. The methodology is applied to a case study of 51 buildings in Tartu, Estonia, with complete yearly demand measurements from the district heating operator. Thanks to the hourly calibration approach, R2 is 0.05 higher on average than the yearly Energy Signature Curve approach. The difference between estimated and measured annual energy consumption is 8% on average, demonstrating the practicality and effectiveness of the proposed method.
  • Miniatura
    Ítem
    Enhanced methodology for disaggregating space heating and domestic hot water heat loads of buildings in district heating networks
    (Elsevier Ltd, 2025-03) Borgato, Nicola; Bordignon, Sara; Prataviera, Enrico; Garay Martínez, Roberto; Zarrella, Angelo
    This paper presents an innovative approach to disaggregate a building's global heat consumption into space heating and domestic hot water heat load components using Energy Signature Curve models. The study addresses the challenges associated with these models, which often fail to represent daily trends accurately and do not account for dynamic changes in building usage. Four approaches based on linear regression models are compared to determine the most accurate method for space heating and domestic hot water disaggregation. The state-of-the-art Energy Signature Curve is compared with three improved alternatives. A new algorithm for automatic season threshold identification is proposed. The comparison with consumption data indicates that the proposed methodology significantly improves the accuracy in heat load disaggregation, with the superior performance provided by the model based on a 24-hour energy threshold. This advancement can potentially optimize district heating network management and support retrofit interventions by providing detailed consumption profiles.