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• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment's operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization. [ABSTRACT FROM AUTHOR]