Development of a low carbon heating and cooling system with storage and gender based temperature regulation for public and commercial buildings

**挑战与机遇：**新冠肺炎**全球大流行增加了患者危重医院病房的冷却需求，因为在疫情爆发的早期阶段，入院人数增加了。它还将增加未来几个月的冬季供暖需求。根据BEIS(2019年)，**热能是英国最大的能源消耗部门**，占44%，是英国排放量的最大贡献者。在降温方面，英国的需求主要由写字楼(65%)和零售商(30%)主导。由于预计气温上升，预计空调在未来将变得普遍。医院和零售场所的制冷和供暖需求必须通过可再生的低碳解决方案与能源储存相结合来满足，以便在2050年达到净零。这催生了英国**热能存储(TES)市场，每年约20GWh，直到2050年(BEIS，2019)，其价值为每年8亿GB**。这就产生了对可持续、可再生和高效的供热和制冷能量存储系统的需求，并对公共卫生建筑和零售空间进行了优化控制。**解决方案：**建议的解决方案是**低碳可再生供暖和制冷系统**，它利用**太阳能技术**，并结合**可持续的高度紧凑的存储模块**。该解决方案将**为建筑物夏季降温和冬季供暖提供热能储存**。该系统的关键部件由太阳能技术提供动力。电力是可再生的，存储模块由可持续材料制成。该系统还包括一个**利用人工智能(AI)技术**的控制系统**，它根据男性和女性的居住情况**有效地调节环境温度**。它使用机器学习(ML)来预测和预测能源需求。**该系统的关键方面是：*可再生太阳能技术，基于被动和主动设置，为加热和冷却建筑物的部件提供电力；*存储介质存储热能，用于夏季降温和冬季取暖；*控制系统，将优化输出，以确保基于性别占用的温度平衡；*控制系统确保需求是实时驱动的，具有预测和预测未来需求的能力。**该系统提供的好处是：*由于能源来自太阳能，二氧化碳排放量显著减少；*与公共卫生建筑、零售店和其他市场的供暖和制冷相关的燃料账单显著减少；*这种低碳供暖和制冷解决方案将降低对污染源的电力需求百分比，这将对英国的净零目标产生积极影响；*社会考虑性别的控制系统；*控制系统还能够根据准入学习和预测需求。这将新冠肺炎病房和公共场所的温度对病毒的影响降至最低，同时考虑了温度对关键工作人员认知表现的影响。

**The Challenge and Opportunity:**The **Covid-19** global pandemic increased demand for cooling in patient critical hospital wards, as admissions increased during the early stages of the outbreak. It is also set to increase the heating demand in the winter months to come. According to the BEIS (2019), **heat is the largest energy-consuming sector** in the UK at 44% and the single largest contributor to UK emissions. With regard to cooling, UK demand is dominated by offices (65%) and retailers (30%). Air conditioners are expected to become widespread in the future due to expected rising temperatures. The cooling and heating demand of hospitals and retail spaces must be met by a renewable low-carbon solution integrated with energy storage in order to reach net zero by 2050\. This has given rise to a UK **Thermal Energy Storage (TES) market of approximately 20GWh per year until 2050 (BEIS, 2019), which has a value of £800 million per year**. This creates the need for a sustainable, renewable and efficient heating and cooling energy storage system, with optimised controls for public health buildings and retail spaces.**The Solution:**The solution proposed is **a low-carbon renewable heating and cooling system** that utilises **solar technology**, combined with **a sustainably highly compact storage module**. The solution will **provide thermal energy storage for cooling of buildings in the summer, and heating in the winter**. The critical components of the system are powered by solar technology. The power is renewable and the storage module is made from sustainable materials. The system also includes a **control system utilising Artificial Intelligence (AI) technology**, which efficiently regulates the ambient temperature **based on the occupancy of men and women**. It uses Machine-Learning (ML) for forecasting and predicting energy demands.**The key aspects of the system are:*** Renewable solar technology that provides power for the components that heat and cool the buildings, based on passive and active setups;* Storage medium stores thermal energy for cooling in the summer and heating for the winter;* Control system that will optimise the output to ensure temperature balance based on the gender occupancy;* The control system ensures that demand is real-time driven, with the ability to forecast and predict future demands.**The benefits offered by the system are:*** Significant reduction in carbon dioxide emissions because the energy source is from the sun;* Significant reduction in the cost of fuel bills related to heating and cooling of public health buildings, retail outlets with scalability to other markets;* This low-carbon heating and cooling solution will reduce the percentage of power demanded from polluting sources, which will have a positive impact on the UK's net zero targets;* The control system that socially considers gender;* The control system is also able to learn and forecast demand based on admittance. This minimises the temperature effects on the virus in Covid-19 wards and public spaces, whilst considering the effect of temperature on the cognitive performance of key workers.