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Recent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight-step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already-designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process.

Personalised Environmental Control Systems (PECS) are devices that cater to the individual needs by providing micro-climate heating, cooling, and ventilation. However, to ensure comfort, energy savings, and productivity, a comfort model based automatic control is required. For its development, thermal preference, physiological information, and data on the surrounding indoor climate were gathered from 24 subjects when using a newly developed PECS with heating, cooling, and ventilation functions. Since PECS should ensure a high level of comfort while providing energy savings through background temperature relaxation, multiple steady-state ambient temperature settings ranging from 18 to 28 °C were tested. The data were clustered according to the subject's self-assessed general thermal preference, namely neutral, warmer, and colder. Machine learning was used to generate a cluster-based personalised comfort model using environmental, physiological, and behavioural indicators. The prediction performance of the models was 11 to 18 percent points higher than that of current group comfort models, predicted mean vote (PMV), which is independent of occupant similarities. The advantage of the personalised approach was the increased performance of the thermal comfort prediction at no expense of occupant sensitive information. Although reliant on estimates of physiological indicators, the models' performance may be increased using real-time data acquisition.

Personalized Environmental Control Systems (PECS) allow control over indoor environmental quality (IEQ) parameters of the microclimate around individual occupants. The present study reports on the results of human subject experiments evaluating a prototype of PECS. The tested prototype had heating, cooling, and ventilation (air circulation through a filter and an ultraviolet germicidal irradiation component) functions. The objective of the present experiment was to obtain the occupants' subjective responses and physiological parameters such as skin temperature, with and without the use of PECS. The occupants' interaction with the PECS prototype was also observed. Experiments were conducted with 24 university students (12 male and 12 female subjects) over a 5-week period between February and March 2022. Different ambient temperature settings between 18 and 28 °C were tested each week. In each week, subjects participated in two 3-hour sessions, once with PECS and once without it. Subjects with PECS were able to adjust the PECS functions freely throughout the measurements, and the changes they made were recorded in an internal log of the PECS. Subjective responses such as thermal sensation and acceptability were compared with their PECS operation to evaluate the effect of PECS, together with each occupant’s interaction with PECS.

Radiant systems have been proven to be an energy-efficient and resource-effective heating and cooling solution for buildings. A key feature of a thermally active building system (TABS), one type of a radiant cooling system, is its ability to activate and control the thermal mass of the building structure. The advantage of this feature is the peak load shifting effect by the thermal mass, which leads to energy saving compared to a conventional system, e.g., an all-air system. This feature of the radiant cooling system could be particularly beneficial under a heat wave and power outage event. Dynamic building simulations were carried out to quantify the resilience of TABS to heat waves and power outages. An all-air system (i.e., airconditioning) was used as the reference cooling system. The simulations were carried out using EnergyPlus. Future weather files (typical meteorological years and years with heat waves) developed in IEA EBC Annex 80 were used for the simulations. In both HVAC systems. Simulation results for future weather data resulted in a decrease in heating demand and an increase in cooling demand.

The world is facing a rapid increase of air conditioning of buildings. It is the motivation of Annex 80 to develop, assess and communicate solutions of resilient cooling and overheating protection. Resilient Cooling is used to denote low energy and low carbon cooling solutions that strengthen the ability of individuals and our community to withstand, and prevent, thermal and other impacts of changes in global and local climates. It encompasses the assessment and Research & Development of both active and passive cooling technologies of the following four groups: Reduce heat loads to people and indoor environments.Remove sensible heat from indoor environments.Enhance personal comfort apart from space cooling.Remove latent heat from indoor environments.The present review sums up the state of the art in cooling solutions which may be regarded as resilient. Its main objective is to systematically describe the available cooling solutions, their physical basis, their benefits and limitations, their technology readiness level, their practical availability, and applicability. Doing so, the State-of-the-Art Review forms the basis for the work of Annex 80.

The objective of Annex 80 is to develop, assess and communicate solutions for resilient cooling. The systematic assessment of resilient cooling strategies is one of the main activities of Annex 80. As stated in Annex Text: Activity B.1 includes a systematic assessment of potential benefits, limitations and performance indicators of resilient cooling systems under a wide range of application scenarios and other boundary conditions. We generate Resilient Cooling ‘Technology Profiles’ to clearly summarize and promote the operational characteristics and benefits of each technology/system. Recommendations for good implementation, commissioning and operation are being developed. Barriers to application and further research opportunities are being identified, which will inform the research activities of Subtask B. The previous approach for assessing the resilience of cooling strategies is mainly based on qualitative comparison and based on results from individual research, which lacks common boundary conditions and universal indicators for resilience evaluation. This study aims to provide a consistent approach for assessing the resilience of different cooling strategies by dynamic simulation. Various cooling strategies will be tested on the reference buildings under present and future weather conditions in different climate zones, and proposed key performance indicators will be applied to evaluate summertime overheating risk and climate resistance of cooling strategies.