Development of a data analytics-based EIS tool for the automatic recognition of anomalous energy consumption patterns in buildings
Reference persons ALFONSO CAPOZZOLI
Description Differently from other kinds of energy management DSS tools, EISs read data at meter-level, analyse them and provide informative outputs to a human user (e.g., energy manager, building owner, energy service company). Advanced EISs not only allow new forms of building energy management to be pursued but at the same time significantly reduce the complexity of performance commissioning in existing buildings. According to the Building Commissioning Association (BCA) Existing Building Commissioning (EBCx) is defined as a systematic process aimed at improving the performance of buildings and energy systems by means of low/no cost and capital-intensive measures and ensuring their effect persists over time. Advanced EISs are today capable to enhance such process (i.e., building commissioning) and it is mainly due to the exploitation of data analytics methods.
The main objective of this Thesis project is to conceive a methodological framework of analysis that allows the final user to gain insights into energy consumption time series at whole building level and then to enable the identification of incorrect energy management procedures that are responsible of energy wasting during operation. The methodology will exploit time series analytics techniques and automatic pattern recognition methods for developing a framework that can be embedded in a EIS. The case study that will be considered is the campus of Politecnico di Torino.
Deadline 24/02/2024 PROPONI LA TUA CANDIDATURA