## 2 Anomaly Detection in Building's Consumption: A Brief Overview of the State of the Art
The experiments use a dataset with multivariate time series about water consumption. [PERSON] et al. [35] proposed a data mining-based framework that extracts electricity load patterns and discovers hidden information on them. A clustering method was also proposed to identify the typical electricity load patterns. The proposed framework was applied to analyze the electricity consumption data of office buildings. [PERSON] et al. [36] presented a big data platform for the detection of anomalies in power consumption. They demonstrate the application of the system to a scheme of power consumption anomaly detection, benchmarking different alternatives. For a whole building, [PERSON] et al. [37] proposed an anomaly detection and diagnosis methodology to detect meter-level anomalous energy consumption and then perform a diagnosis on the loads responsible for anomalous patterns. The process consists of several steps combining data analytics procedures. The methodology was developed and tested on the monitored data of a medium/low voltage transformation unit of a university campus. Some other works can be found that perform surveys on several methods. For instance, the work by [PERSON] et al. [38] presented a review of ML and DL techniques for anomaly detection in IoT data. The work includes an analysis of the nature of the data, anomaly types, learning modes, window models, datasets, and evaluation criteria. Furthermore, research challenges related to data evolution, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameter selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data were studied. Furthermore, for the energy consumption of buildings, a review of anomaly detection systems based on artificial intelligence was presented by [PERSON] et al. [24]. Their work includes a taxonomy to classify existing algorithms based on different modules and parameters, such as ML algorithms, computational platforms, anomaly detection degrees, feature extraction procedures, and usage scenarios. Domain-specific issues, difficulties, and challenges that remain open are also analyzed. Those challenges include the deficiency of having definitions of anomalous power consumption, annotated datasets for supervised and semi-supervised learning, standard metrics to evaluate the models' performance, and the reproducibility of scenarios, and privacy preservation. In general, the studies are made with single sources of data, and most use well-known datasets. The controlled quality datasets are used to evaluate the performance of the models, providing different conditions than the ones that are proposed in this case study. In our case, we are looking for a semi-automated process, adaptable to different sources of data and combinations, to be implemented without major tuning, and without major knowledge by the deployers. In this context, the methodology for this work is presented next, including the two unsupervised algorithms which were analyzed (namely the IF/ML approach and the VAE/DL approach) and a new performance metric.