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To date, the assessment of the energy flexibility to be delivered by existing buildings and by their legacy HVAC systems is hindered by a lack of commonly agreed-upon methodologies. There are many research works in the field; however, many of them are focused on the design stage or, in case of addressing building operation, they are based on controlled experimental set-ups. The novelty of this paper lies in the fact that it develops and validates an original methodology for the Flexibility Function estimation to evaluate the delivered energy flexibility of several Automated Demand Response services applied on different heat pump systems working under real operations. The active interaction with several electricity markets, ranging from the Spanish day-ahead market to the German and Swiss ancillary services markets, have also been evaluated during the winter and spring seasons. The method results showed that heat pumps could offer a significant potential of flexibility in the analysed countries. Nevertheless, it has also been envisaged that some restrictions concerning reaction times and reliability may affect its readiness for certain ancillary services markets.

© 2018 IEEE. Nowadays, all aspects of a production process are continuously monitored and visualized in a dashboard. Equipment is monitored using a variety of sensors, natural resource usage is tracked, and interventions are recorded. In this context, a common task is to identify anomalous behavior from the time series data generated by sensors. As manually analyzing such data is laborious and expensive, automated approaches have the potential to be much more efficient as well as cost effective. While anomaly detection could be posed as a supervised learning problem, typically this is not possible as few or no labeled examples of anomalous behavior are available and it is oftentimes infeasible or undesirable to collect them. Therefore, unsupervised approaches are commonly employed which typically identify anomalies as deviations from normal (i.e., common or frequent) behavior. However, in many real-world settings several types of normal behavior exist that occur less frequently than some anomalous behaviors. In this paper, we propose a novel constrained-clustering-based approach for anomaly detection that works in both an unsupervised and semi-supervised setting. Starting from an unlabeled data set, the approach is able to gradually incorporate expert-provided feedback to improve its performance. We evaluated our approach on real-world water monitoring time series data from supermarkets in collaboration with Colruyt Group, one of Belgiums largest retail companies. Empirically, we found that our approach outperforms the current detection system as well as several other baselines. Our system is currently deployed and used by the company to analyze water usage for 20 stores on a daily basis. ispartof: pages:527-536 ispartof: 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) vol:2018-November pages:527-536 ispartof: 18th IEEE International Conference on Data Mining (ICDM) location:Singapore, SINGAPORE date:17 Nov - 20 Nov 2018 status: published