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2022

  • in Thesis
  • 15 min read

A cloud-based Energy Information System (EIS) for innovative energy management in buildings: the case of Politecnico di Torino.

Abstract

The building sector accounts for more than a third of global energy consumption, and nearly 90 percent of total energy consumption in the building lifecycle depends on its operation. In recent years, the spread of IoT technologies in buildings and the adoption of pervasive smart-metering systems have enabled the acquisition of a massive amount of high-frequency energy-related data. Leveraging this data to extract and formalize knowledge is essential to characterize the actual performance of buildings during operation and take action to reduce energy consumption, prevent energy waste, and promote a more efficient way of managing buildings. A valuable tool employed to monitor, analyze and control building energy systems by taking advantage of advanced data analysis technologies are so-called energy management and information systems (EMIS). EMISs are often designed as monolithic software deployed on physical servers and thus are unable to scale properly to support computationally demanding real-time applications. In addition, EMISs are usually tailored to the building-specific monitoring system, which leads to a lack of interoperability and raises challenges when integrating different advanced services based on data-driven techniques. This work presents the design of microservice-based Energy Management and Information System cloud architecture and the implementation of a forecast and anomaly detection application. The architecture, based on the Kubernetes container cluster, enabled fine-grained system decoupling, reliability, scalability, and ease of system maintenance while optimizing resource utilization, interoperability, and integration, creating a robust environment to analyze cross-domain data and developing innovative data-driven EIS services. The forecast and anomaly detection application was tested and deployed online on the photovoltaic plant of the Politecnico di Torino campus, leading to the development of a tool useful to support energy management through an effective prediction of energy demand at a daily scale and anomaly alerting.

References

  • Davide Taddei A cloud-based Energy Information System (EIS) for innovative energy management in buildings: the case of Politecnico di Torino. Rel. Alfonso Capozzoli, Fulvio Giovanni Ottavio Risso, Roberto Chiosa, Marco Savino Piscitelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022

Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries

Abstract

Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads.

References

  • Chiosa, R., Piscitelli, M. S., Fan, C., & Capozzoli, A. (2022). Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries. Energy and Buildings, 270, 112302.
  • in Thesis
  • 15 min read

Automated Anomaly Detection In Energy Consumption Time Series Of Buildings Through Pattern Recognition Techniques

Abstract

Commercial buildings are significant consumers of electrical and thermal energy, therefore energy savings, improving energy efficiency, and reducing greenhouse gas emission are the purposes for building owners, operators, and stakeholders. On the other hand, energy analysts have to understand the energy consumption behavior by looking for changes in energy patterns that may imply device failures or anomalous behavior. This master’s thesis deals with an energy data-mining approach that performs automated Anomaly Detection, through data analytics techniques called Matrix Profile (MP) and its extension Contextual Matrix Profile (CMP). This work aims to extract from large energy time-series data generated by sub-meters and smart sensors installed in Politecnico di Torino buildings, anomalous energy consumption patterns and to understand the root causes of the detected anomaly. The framework built up combines a hierarchical cluster algorithm, which helps to aggregate power consumption daily patterns, with MP and a final descriptive statistics outliers’ analysis.

References

  • Simone Vitale. AUTOMATED ANOMALY DETECTION IN ENERGY CONSUMPTION TIME SERIES OF BUILDINGS THROUGH PATTERN RECOGNITION TECHNIQUES. Rel. Alfonso Capozzoli, Marco Savino Piscitelli, Roberto Chiosa. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2022
  • in Thesis
  • 15 min read

Application of Data Analytics techniques for the analysis of building energy performance during operation : the case of Politecnico di Torino.

Abstract

In 2019, building operations were responsible for about the 28% of the global CO2 emissions, taking into account not only the share directly due to the daily activity, but also the indirect part produced by the generation of power that supplies the building. The buildings consumption is strongly affected by the Energy Performance Gap, which is the deviation of the actual energy performance of the building with respect to the expected and designed one. As a consequence, the scope of improvement is relevant for the buildings and, in this context, an effective energy management has a key role. The aim of this thesis work is to provide a data analytics methodology whose results can be helpful to increase the knowledge about the system and that can be a tool to implement for the energy management. In particular, the methodology is applied to an educational building, the Polytechnic of Turin, focusing on a defined subsection of the system that includes energy-intensive loads and a photovoltaic production plant. The analysis follows two parallel paths, taking into account first, the load-side and then the production-side of the domain. The load-level analysis identifies typical profiles of consumption - with correspondent external conditions - of a chiller unit and an independent building, by means of an hierarchical clustering technique and a classification tree. Then, the focus is on the baseload of each profile, intended as the minimum value of demand that is always present, in order to find reference power ranges that are used to define a Key Performance Indicator, that ranks the daily energy-related behaviour. At this point, the energy waste of the loads is detected with a comparison between the actual consumption and a simulated one, considering improved values of baseload power. The production-level analysis, instead, consists in the development of an Artificial Neural Network for the forecast of the power production of the photovoltaic plant; the results of the neural network are then used to develop an anomaly detection algorithm in order to automatically found faulty operating conditions of the system, providing a daily warning that distinguishes between strong and possible anomalies. Finally, a predictive maintenance procedure is proposed with the aim to recommend extraordinary maintenance actions if a series of anomalous day are consequently reported.

References

  • Maria Teresa Zitelli Application of Data Analytics techniques for the analysis of building energy performance during operation : the case of Politecnico di Torino. Rel. Alfonso Capozzoli, Marco Savino Piscitelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2022