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Feeding Building forecasting models via hybrid simulated/measured database

keywords BUILDING AUTOMATION, BUILDING SIMULATION, MACHINE LEARNING

Reference persons GIACOMO CHIESA

External reference persons Possibile correlatore industriale

Research Groups ETD

Thesis type RESEARCH / EXPERIMENTAL

Description The application of forecasting algorithms in the building secotr is progressively growing, allowing estimating of building energy needs and temperatures. Nevertheless, the initial lack of historical monitored data is causing industries to struggle to apply their models - especially for free-running usages - missing the feeding data. Nevertheless, new building dynamic simulation platforms, including accurate weather data and the possibility to semi-automatically calibrate models, can produce needed data to feed ML instruments, allowing a few months/weeks of data for the first application. The thesis candidate will develop an approach to progressively substitute simulation data with monitored ones while they are stored. The thesis will be tested on actual buildings, monitored via cloud solutions, for which a sufficient amount of historical data is available to try the approach (2-3 years). The work is part of the EU H2020 projects EDYCE and PRELUDE

Required skills ML algorithm, building monitoring/variables


Deadline 18/10/2024      PROPONI LA TUA CANDIDATURA




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