Data-driven control strategies to enhance energy performance of HVAC systems in buildings_By ENERTECH27/02/2023 - 15/07/2023
According to the IEA (International Energy Agency), Heating Ventilating and Air Conditioning (HVAC) systems account approximately for 50% of buildings energy demand. As a consequence, exploring energy management strategies to enhance the operation performance of HVAC systems can lead to significant benefits in terms of cost savings and greenhouse gas emissions reduction. In this context, the spread of Information and Communication Technologies (ICT) and IoT sensors is contributing to an unprecedent availability of long-term monitoring data related to energy performance and buildings indoor environmental quality. This vast amount of data can enable the development of calibrated building energy models and data-driven control strategies to simultaneously optimise both indoor environmental quality and energy demand, through multi-objectives optimisation and predictive data analytics. All data are exchanged with monitoring system using 5G (Industry 4.0).
The purpose of this challenge is to provide an analysis and a feasibility study of a data-driven advanced control system able to optimise the operation of HVAC systems in non-residential buildings. A critical investigation on the current trends of predictive control technologies and algorithms will be performed to define opportunities and potentialities, also in comparison to usual reactive controls. Different advanced control strategies will be designed based on the input variables collected by the monitoring system, the control outputs related to the available actuators and objective functions. The developed solutions will be tested in co-simulation environments combining various state-of-the-art software and tools. The most promising solutions will be identified according to both control performance with respect to the objectives and their applicability in a real world scenario.
This challenge is carried out in the context of the CTE Next initiative, funded by the MISE.
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