| KEYWORD |
TEBE
Theses at Politecnico
Enhancing Energy Management in buildings through Data Analytics Based Methodologies
keywords ARTIFICIAL INTELLIGENCE, BUILDING ENERGY MANAGEMNT, DATA ANALYTICS
Reference persons ALFONSO CAPOZZOLI
External reference persons Eng. Marco Savino Piscitelli
Eng. Silvio Brandi
Research Groups BAEDA lab (www.baeda.polito.it), TEBE
Thesis type RESEARCH THESIS
Description The growing spread of building automation, energy management systems, and Information and Communication Technologies (ICT) in buildings, makes it possible a considerable amount of heterogeneous building-related data to be collected. These data are retrieved from different sources and can refer either to the energy consumption of a whole building, its HVAC system or a single component. As a consequence, complex building-related databases are more and more available. Their exploration provides the opportunity to effectively characterise the actual building energy behaviour and to optimise its performance during operation. The great opportunity offered by this data availability is leading to increased utilisation of data analytics techniques (e.g., data mining, machine learning) in building physics applications. Currently, the data analytics-based energy management of buildings provides powerful opportunities to enhance their energy efficiency. Furthermore, it can be used to reduce the mismatch be-tween the actual and expected energy demand, which is often due to an anomalous oper-ation of the equipment and control systems and occupant behaviour. The most promising applications of data analytics in building energy management are the prediction of energy demand required for the efficient operation of a building, the optimization of building operation, the detection and commissioning of operational failures of building equipment, the energy benchmarking analysis, the characterisation of energy demand profiles, and the assessment of the impact of user behaviour.
See also https://www.researchgate.net/lab/Building-Automation-and-Energy-Data-Analytics-Lab-Alfonso-Capozzoli
Required skills knowledge of building physics, heat transfer and HVAC systems. Rudiments of data mining and energy data analysis. Knowledge of Matlab, R or Python language programming is considered beneficial but not a prerequisite.
Deadline 19/10/2021
PROPONI LA TUA CANDIDATURA