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There has been an increasing concern on thermal comfort in sleeping environments and its associated energy use in the past few years. To improve the thermal environment and to reduce energy use of air conditioning in bedrooms, applications of task/ambient air conditioning (TAC) systems were proposed and studied previously. It’s indicated that the TAC system can be well integrated with a bed. Due to the variation of the envelope heat gain in a bedroom during night, it is necessary to study the thermal environment inside a bedroom and the energy use of a TAC system for the bedroom at varying envelope thermal loads. Therefore, this paper reports on a numerical study on a TAC system applied to a bedroom with different envelope heat gains. The influences of envelope heat gain on energy consumption of the TAC system and the indoor thermal comfort were studied in this paper. The research results show that at supply air flow rate ( Q s ) of 50 l/s, energy consumption was increased from 47.78 W to 213.11 W, and the PMV value was increased from −1.69 to −1.29 with the increase in envelope heat gain from 3.11 W to 155.6 W. To make PMV equal to zero under different envelope heat gains, linear regression models between supply air parameters and PMV were built and solved. The obtained values form curves where PMV = 0 and give a guide for operation of the TAC system. It was found that the calculation of energy consumption on these curves depends only on envelope heat gain. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous hierarchical control method for a direct expansion air conditioning system. The control objective is to maintain both thermal comfort and indoor air quality at required levels while reducing energy consumption and cost. This control method consists of two layers. The upper layer is an open loop controller that allows obtaining tradeoff steady states by optimizing the energy cost of the direct expansion air conditioning system and the value of predicted mean vote under the time-of-use price structure of electricity. On the other hand, the lower layer designs a model predictive controller, which is in charge of tracking the tradeoff steady states calculated by the upper layer. Control performance of the proposed control method is compared to a conventional control strategy. The results show that the proposed control strategy reduces the energy consumption and energy cost of the direct expansion air conditioning system by 31.38% and 33.85%, respectively, while maintaining both the thermal comfort and indoor air quality within acceptable ranges, which validate the proposed methodology in terms of both comfort and energy efficiency. [ABSTRACT FROM AUTHOR]

Generally, conventional controllers for comfort are designed by using on/off control or proportional-integral (PI) control, with little consideration of energy consumption of the system. This paper presents a multi-input-multi-output (MIMO) model predictive control (MPC) for a direct expansion (DX) air conditioning (A/C) system to improve both indoor thermal comfort and air quality, whereas the energy consumption is minimised. The DX A/C system is modelled into a nonlinear system, with a varying speed of compressor and varying speed of supply fan and volume flow rate of supply air being regarded as inputs. We first propose an open loop controller based on an optimisation of energy consumption with the advantage of a unique set of steady states. The MPC controller is proposed to optimise the transient processes reaching the steady state. To facilitate the MPC design, the nonlinear model is linearised around its steady state. MPC is designed for the linearised model. The advantages of the proposed energy-optimised open loop controller and the closed-loop regulation of the MIMO MPC scheme are verified by simulation results. [ABSTRACT FROM AUTHOR]

• 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]

In this paper we explore the influence of personal control over space conditioning systems on occupants’ thermal comfort perception. It aims to identify the psychological effects of perceived control as well as the physical impacts when the controllable approaches are utilized. A chamber experiment was conducted, the results from which indicate that subjects with perceived control tended to report more positive comfort perception. The severer the thermal conditions were, the more subjects wanted to use personal control approaches, thus corresponding to stronger psychological effects. When personal control approaches were utilized appropriately, even a slight improvement in thermal conditions could significantly alleviate subjects’ thermal discomfort complaints. These findings provide support to the adaptive comfort theory and can serve as reference for the design of personal environmental control systems. [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]