Browsing by Author "Diarce, Gonzalo"

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    Corrigendum to “Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters” [Energy 239, Part D, 2022, 122318] (Energy (2022) 239(PD), (S0360544221025664), (10.1016/j.energy.2021.122318))
    (2022-08-15) Lumbreras, Mikel; Garay-Martinez, Roberto; Arregi, Beñat; Martin-Escudero, Koldobika; Diarce, Gonzalo; Raud, Margus; Hagu, Indrek; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA
    The authors regret to inform that, even after careful revisions in all stages of the manuscript, a relevant typographic error has been found in the published version of the paper. The error is found in the definition of the so-called Q-algorithm in Eq. (1), where the selection among two formulae is performed based on the actual heat load (Q) compared to a reference heat load (QREF). The correct formulation for equation can be found in Eq. (1) below. [Formula presented] The authors would like to apologise for any inconvenience caused.
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    Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters
    (2022-01-15) Lumbreras, Mikel; Garay-Martinez, Roberto; Arregi, Beñat; Martin-Escudero, Koldobika; Diarce, Gonzalo; Raud, Margus; Hagu, Indrek; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA
    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.
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    Unsupervised recognition and prediction of daily patterns in heating loads in buildings
    (2023-04-15) Lumbreras, Mikel; Diarce, Gonzalo; Martin, Koldobika; Garay-Martinez, Roberto; Arregi, Beñat; EDIFICACIÓN DE ENERGÍA POSITIVA
    This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.