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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]

[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]

This paper presents figures for the small power equipment loads encountered by the Welsh School of Architecture in UK offices while undertaking a programme of research and monitoring into the energy efficiency of air-conditioning systems in use. The findings are based on surveys undertaken in 30 air-conditioned offices between April 2000 and October 2002. Peak small power equipment loads were calculated using the CIBSE nameplate-ratio method. The results show that the small power loads averaged 17.5 W/m2 with a range between 6 and 34 W/m2 of treated floor area. When normalised for occupancy the calculated peak small power equipment loads averaged 158 W per person, with a range between 124 and 229 W per person. Current industry guidance could lead to the overestimation of small power equipment loads by as much as 650%, ultimately resulting in increased capital and running costs of air-conditioning plant and reduced thermal comfort. A more accurate method of estimating peak small power equipment loads in UK office buildings is suggested based on occupant density. [Copyright &y& Elsevier]