# Biclustering of Smart Building Electric Energy Consumption Data
## 1 Introduction
In a similar way, we can extract day profiles, i.e., set of days with a comparable energy consumption common to all the considered buildings. With biclustering, we can perform clustering in both dimensions. In this way, it is possible to detect groups of buildings that behave in an analogous way during a particular time interval, which does not have to be the whole interval considered. In fact, if data are organized as matrix, where, for instance, rows stand for buildings and columns for days, then a bicluster represents a submatrix, where only the data relative to a subset of buildings are considered over a specific subset of days. Biclustering based methods have been widely proposed in the literature due to their capacity of discovering groups of instances with a common pattern under a subset of features [14; 15]. These approaches have mainly been applied in bioinformatics, and in particular in the context of microarray data. Within this context, their main purpose is to find subsets of genes with a similar biological behaviour under a subset of experimental conditions [16]. To this aim, several approaches have been proposed to analyse gene expression datasets. For example, in [17], the authors proposed a scatter search approach based on linear correlations among genes to find biclusters. In [18], the authors proposed a multi-objective evolutionary algorithm (MOEA) for the detection of biclusters presenting interesting features. However, biclustering techniques are not applied only within bioinformatics. They have also been used in other fields, such as social network datasets [19] or text mining [20]. For example, in [21], a biclustering method is used to identify the most suitable group of friends of the user in social network datasets. Within biclustering of biological data, time series datasets have also been addressed (e.g., [22; 23; 24; 25]). An interesting work was presented by [PERSON] et al. [26], who proposed and tested a biclustering algorithm for time series, not only based on gene expression datasets, but also on artificial time series and apartment price datasets in metropolitan areas. In this paper, we propose the use of biclustering methods to analyse energy consumption data. The main goal is to identify groups of buildings that present the same behaviour during a specific period of time. In particular, we are interested in detecting behaviours presenting peaks of consumption, as this may indicate some out of the norm behaviour that should be further investigated. To do so, we use a biclustering approach based on a multi-objective evolutionary algorithm (MOEA) proposed in [18]. To the best of our knowledge, there are no other biclustering approaches to time series of electric energy consumption data. However, as stated above, biclustering methods have been successfully applied to other time series data, e.g., genetic expression data (e.g., [23; 24]). Clustering techniques have been applied to analyse energy consumption dataset (see, for instance [27; 28]). As mentioned above, in our proposal, we use an algorithm, called SMOB, that has been previously used on gene expression data [18]. To evaluate the usefulness of our approach, we used data produced by different buildings located in a university campus in Spain. The obtained results show that biclustering methods can help in gathering more information about the energy consumption behaviour of buildings and to detect anomalous situations, e.g., deficient management of the buildings or abnormal functioning of some sensors. The paper is organised in the following way. In Section 2, we provide a description of the data used, some basic notions of biclustering and of the particular biclustering strategy applied to analyse the data. In Section 3, we describe the experimental setup and provide the results. In Section 4, we draw the main conclusions and identify possible future developments.