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Thermochemical energy storage (TCES) presents a promising method for energy storage due to its high storage density and capacity for long-term storage. A combination of TCES and district heating networks exhibits an appealing alternative to natural gas boilers, particularly through the utilisation of industrial waste heat to achieve the UK government’s target of Net Zero by 2050. The most pivotal aspects of TCES design are the selected materials, reactor configuration, and heat transfer efficiency. Among the array of potential reactors, the fluidised bed emerges as a novel solution due to its ability to bypass traditional design limitations; the fluidised nature of these reactors provides high heat transfer coefficients, improved mixing and uniformity, and greater fluid-particle contact. This research endeavours to assess the enhancement of thermochemical fluidised bed systems through material characterisation and development techniques, alongside the optimisation of heat transfer. The analysis underscores the appeal of calcium and magnesium hydroxides for TCES, particularly when providing a buffer between medium-grade waste heat supply and district heat demand. Enhancement techniques such as doping and nanomaterial/composite coating are also explored, which are found to improve agglomeration, flowability, and operating conditions of the hydroxide systems. Furthermore, the optimisation of heat transfer prompted an evaluation of heat exchanger configurations and heat transfer fluids. Helical coil heat exchangers are predominantly favoured over alternative configurations, while various heat transfer fluids are considered advantageous depending on TCES material selection. In particular, water and synthetic liquids are compared according to their thermal efficiencies and performances at elevated operating temperatures.

The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems.

The aim of this work is to analyse the thermal comfort and energy performance of a thermal heating chair for open plan office using field experiments, thermal comfort survey and energy simulations. A comprehensive review on the development of thermal chairs was carried out to highlight the present research gaps. The study developed a thermal chair prototype with controllable heating pads, incorporated into the back of the seat and back rest fabric. The field test was carried out in an office building in the UK during the winter. The study showed that the users set the thermal chair temperature between 29-45 °C. The field survey results of the thermal satisfaction survey showed that 19 out of 44 participants felt satisfied before using the device. While after using the thermal chair, the number of satisfied respondents increased to 34. The work also utilised Building Energy Simulation to further assess the thermal comfort and energy performance of the thermal chair. Three cases were simulated: non heated office chair with the zone thermostat maintained at 22 °C, non heated office chair with the zone thermostat at 16-20 °C and thermal chair with the zone thermostat at 16-20 °C.

Thermochemical energy storage materials and reactors have been reviewed for a range of temperature applications. For low-temperature applications, magnesium chloride is found to be a suitable candidate at temperatures up to 100 °C, whereas calcium hydroxide is identified to be appropriate for medium-temperature storage applications, ranging from 400 °C up to 650 °C. For the high-temperature range (750–1050 °C), oxides of cobalt, manganese, and copper are found to have the redox behaviour required for thermochemical heat storage. However, some of these materials suffer from low thermal conductivities, agglomeration, and low cyclability and, therefore, require further improvements. The concept of enhancing thermal conductivities through additives such as nanomaterials has been encouraging. From an operational point of view, fluidized-bed reactors perform better than fixed- and moving-bed reactors due to better particle interactions. There is, however, a need for the reaction bed to be further developed toward achieving optimum heat and mass transfers. Agitated fluidized-bed reactors have shown encouraging results and are suggested for further exploration. A combination of appropriate computational tools can facilitate an in-depth understanding of bed dynamics.

The Urban Heat Island (UHI) effect is a phenomenon whereby urban areas become warmer than their surrounding rural areas, due to the replacement of vegetation and soil with surfaces such as asphalt and concrete. The asphalt pavement surfaces tend to absorb a large amount of heat through solar radiation and increase the air temperature, which affects the operation of building heating and cooling systems, causing environmental problems and thermal discomfort. However, this energy can be collected by water circulated through buried copper pipes to cool down temperatures and be stored for other usages. This work aims to develop a method for determining the optimum areas to locate pavement solar collector (PSC) systems and simulate the reduction of ambient air and surface temperature by using a coupled computational modelling approach. Discrete ordinate model and solar-ray tracing were utilised for solar radiation effect modelling in the 3D simulation. Furthermore, the PSC prototype was developed, and lab-scale experiments were carried out for validation. Based on the simulated conditions, in the unshaded area, the asphalt slab’s near-surface temperature was reduced by up to 10℃ and the outlet water temperature increased by about 5℃. At the pedestrian height level, the air temperature was reduced up to 4.6℃. This study further expands the investigation of the variation of outdoor conditions such as air temperature and solar radiation. The results showed that the proposed method could be used to optimise the pavement solar collector’s positioning to reduce urban surface and air temperature.

Research into photovoltaic thermal systems is important in solar technologies as photovoltaic thermal systems are designed to produce both electrical and thermal energy, this can lead to improved performance of the overall system. The performance of photovoltaic thermal systems is based on several factors that include photovoltaic thermal materials, design, ambient temperature, inlet and outlet fluid temperature and photovoltaic cell temperature. The aim of this study is to investigate the effect of photovoltaic thermal outlet water temperatures and solar cell temperature on both electrical and thermal efficiency for different range of inlet water temperature. To achieve this, a mathematical model of a photovoltaic thermal system was developed to calculate the anticipated system performance. The factors that affect the efficiency of photovoltaic thermal collectors were discussed and the outlet fluid temperature from the photovoltaic thermal is investigated in order to reach the highest overall efficiency for the solar cooling system. An average thermal and electrical efficiency of 65% and 13.7%, respectively, was achieved and the photovoltaic thermal mathematical model was validated with experimental data from literature.

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

A windcatcher is a wind-driven natural ventilation system that catches the prevailing wind to bring fresh airflow into the building and remove existing stale air. This technology recently regained attention and is increasingly being employed in buildings for passive ventilation and cooling. The combination of windcatchers and evaporative cooling has the potential to reduce the amount of energy required to ventilate and cool a greenhouse in warm and hot climates. This study examined a greenhouse incorporated with a passive downdraught evaporative cooling windcatcher (PDEC-WC) system using Computational Fluid Dynamics (CFD), validated with experimental data. Different hot ambient conditions of temperature (30–45 °C) and relative humidity (15–45%) were considered. The study explored the influence of different spray heights, layouts, cone angles and mass flow rates on indoor temperature and humidity. The average error between measurements and simulated results was 5.4% for the greenhouse model and 4.6% for the evaporative spray model. Based on the results and set conditions, the system was able to reduce the air temperature by up to 13.3 °C and to increase relative humidity by 54%. The study also assessed the influence of neighbouring structures or other greenhouses that influence the flow distribution at the ventilation openings. The study showed that the windcatcher ventilation system provided higher airflow rates as compared to cross-flow ventilation when other structures surrounded the greenhouse.

The 2022 FIFA World Cup brings Qatar great challenges in terms of minimizing the cooling energy consumption and providing thermal comfort for both spectators and players. This paper presents comparisons among the results of thermal and wind environment modelling of a semi-outdoor stadium under three different cooling configurations and a baseline configuration without cooling using the Computational Fluid Dynamics (CFD) tool ANSYS Fluent 18.2. The three cooling configurations are: (1) vertical jets only above upper tiers, (2) vertical jets above upper tiers and horizontal jets at the back of lower tiers and around the pitch, (3) integrated vertical jets above upper tiers, horizontal jets at the back of lower tiers and air curtains at gates. De-coupled solar radiation simulations are implemented using the solar irradiance data in Doha under fair weather conditions method in Fluent in order to capture realistic thermal boundary conditions for the ground, stadium and surrounding buildings. On the basis of the set conditions, the results show that air curtains, employed in configuration 3 are effective in preventing the penetration of hot outside air through the gates of the stadium, which is an existing issue for stadiums in hot climates, and also contribute to lower energy consumption per match than the other configurations of cooling jets. The results presented in this study are useful not only for future design and retrofits of stadiums in hot climates but also for stadiums that incorporate mechanical cooling.