Multivariate statistical monitoring of buildings. Case study: Energy monitoring of a social housing building

Abstract

A complete methodology for energy building monitoring based on Principal Component Analysis (PCA) is proposed. The method extends the Unfolding or Multiway Principal Component Analysis (MPCA) used in statistical batch process control in terms of building and neighbourhood monitoring. Relationships between energy consumption and independent variables such as weather, occupancy or any other variables that are significant for monitoring can be gathered in a model using the proposed methodology. Historic data are used to obtain a reference model that will be used for monitoring. Two unfolding strategies are proposed (time-wise and entity-based) offering complementary views of the building or of the community under consideration. The first, time-wise unfolding, is suitable for detecting behavioural changes over time, whereas entity-wise unfolding allows the identification of entities, e.g. dwellings in a building, that behave substantially differently from others over a period of time. Two simple statistics, T² and SPE, are used to define two monitoring charts capable of detecting abnormal behaviours and, furthermore, the isolation of variables that mainly explain such a situation. The paper presents the theoretical background, followed by the methodological principles. The results are illustrated by a case study

This work has been developed within the project Plataforma para la monitorización y evaluación de la eficiencia de los sistemas de distribución en Smart Cities, ref. DPI2013-47450-C2-1-R and project ACCUS (Adaptive Cooperative Control in Urban (sub) Systems., ART-010000-2013-2 -333020-1), funded by the Spanish Ministry of Industry, Energy and Tourism and by the JTI ARTEMIS Joint Undertaking of the European Commission. Appreciation is given for the data provided by the Patronat de l’habitatge de Barcelona with the collaboration of AITEL. Data fromMeteocat were also used