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Benchmarking of adaptive control strategies of HVAC systems in a dynamic simulation environment

Riferimenti ALFONSO CAPOZZOLI

Tipo tesi IN COLLABORAZIONE CON AZIEDA, RICERCA

Descrizione In the last few years, many research activities are aimed at exploring strategies for simultaneously optimizing indoor environmental quality and energy demand through multi-objectives and quasi-real time control procedures based on forecasting and online analytics. Adaptive and predictive optimal control provides powerful opportunities for leveraging building properties (e.g. thermal mass, storage, renewable energy sources) to enhance energy flexibility during operation. However, a robust benchmarking of these control strategies against other known techniques remain an open issue to address.
The student is expected to obtain the following skills:
HVAC modelling in EnergyPlus.
Knowledge of Python language.
Basic control strategies of HVAC systems (e.g. PID control).
Advanced control strategies of HVAC systems (e.g. Fuzzy-PID, Model Predictive Control, Reinforcement Learning).


Scadenza validita proposta 23/05/2020      PROPONI LA TUA CANDIDATURA




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