Examinando por Autor "Diarce Belloso, Gonzalo"

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    Improving building heat load forecasting models with automated identification and attribution of day types
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-10-08) Lumbreras Mugaguren, Mikel; Garay Martínez, Roberto ; Diarce Belloso, Gonzalo ; Martín Escudero, Koldobika ; Arregi, Beñat
    This paper introduces a comprehensive methodology for predicting hourly heat loads in buildings. The approach employs unsupervised learning to identify distinct day types based on daily load profiles. A classification process then assigns each day to one of these day types, followed by the application of various supervised learning techniques to forecast heat loads. The methodology is both simple and robust, facilitating its use in load prediction across a wide range of buildings. The process is validated using data from three distinct building types (Residential, Educational, and Commercial) located in Tartu, Estonia. The results indicate that the day type identification and attribution process significantly reduce model complexity and computational time while achieving high prediction accuracy (MAPE ~<2%) with minimal computational requirements.
  • Miniatura
    Ítem
    Unsupervised recognition and prediction of daily patterns in heating loads in buildings
    (Elsevier Ltd, 2023-04-15) Lumbreras Mugaguren, Mikel; Diarce Belloso, Gonzalo; Martín Escudero, Koldobika; Garay Martínez, Roberto ; Arregi, Beñat
    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.