Thermostats are widely used in temperature regulation of indoor spaces and have a direct impact on energy use and occupant thermal comfort. Existing guidelines make recommendations for properly selecting set points to reduce energy use, but there is little or no information regarding the actual achieved thermal comfort of the occupants. While dry-bulb air temperature measured at the thermostat location is sometimes a good proxy, there is less understanding of whether thermal comfort targets are actually met. In this direction, we have defined an experimental simulation protocol involving two office buildings; the buildings have contrasting geometrical and construction characteristics, as well as different building services systems for meeting heating and cooling demands. A parametric analysis is performed for combinations of controlled variables and boundary conditions. In all cases, occupant thermal comfort is estimated using the Fanger index, as defined in ISO 7730. The results of the parametric study suggest that simple bounds on the dry-bulb air temperature are not sufficient to ensure comfort, and in many cases, more detailed considerations taking into account building characteristics, as well as the types of building heating and cooling services are required. The implication is that the calculation or estimation of detailed comfort indices, or even the use of personalised comfort models, is key towards a more human-centric approach to building design and operation.

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.

This paper presents work undertaken as part of the European H2020 project OptEEmAL (Optimized Energy Efficient Design Platform for Refurbishment at District Level), toward development of a decision-support platform for building and district refurbishment interventions. We describe a methodology for generation and evaluation of refurbishment scenarios for building and districts with particular emphasis on “active” energy conservation measures (i.e., installation or replacement of heating, ventilation, air conditioning (HVAC) systems) and related controls. The impact of HVAC and controls on energy and economic key performance indicators are usually neglected or very simplified in existing energy simulation tools. We apply a model-based approach to evaluate key-performance indicators related to energy consumption and energy costs in buildings and districts, such that possible refurbishment alternatives can be easily evaluated, thereby showing how a smart decision support tool will allow stakeholders to compare multiple alternatives quickly. By considering relevant case studies at building and district level, including refurbishment of heating and cooling plants, we highlight, in a simulation-based study, how the deployment of efficiency-based controls enable significant energy savings thanks to the exploitation of the model-based approach. This way, additional motivations for energy savings and ultimately for new investments in energy-related technologies are provided.