Part2: Building Management linking Energy Demand, Distributed Conversion and Storage using Dynamic Modelling and a Pervasive Sensor Infrastructure

无论是在英国还是在全球，商业和住宅建筑的二氧化碳排放量都占了很大比例。2000年，英国40%的非交通能源用于采暖，采暖和热水占家庭能源的82%，占商业能源的%。因此，商业建筑减少能源需求可以大大有助于实现英国更广泛的能源消耗目标。与直接提出商业办公空间用户行为改变干预措施的提案不同，该项目提议解决我们在了解商业环境中能源消耗的数量和性质方面的一个关键缺陷，以期开发新的整体解决方案，包括优化共享资源使用和能源储存设施。拟议的研究计划通过设计和开发一个传感基础设施来应对这一挑战，该基础设施由联网的物理传感器(例如在线状态传感器、能耗传感器)和虚拟传感器(例如日历和房间预订传感器、应用程序使用情况传感器)组成，将提供关于正在使用多少能源、用于什么目的以及由谁使用的细粒度信息。通过应用知识工程、活动识别和机器学习(例如，贝叶斯分类器)的技术，我们方法的第一阶段将获得更高级别的信息(例如，在特定房间举行的会议)，并将使用模式(如能耗峰值)与真实世界的活动和工作流(例如，打印一系列会议报告)联系起来。在第二阶段，这些资料将用于将大厦管理中使用的建筑物模型参数化，以便更准确地预测能源使用情况，并优化(分散的)能源消耗、发电和储存。基于这些模型，我们将开发一个决策支持工具，将收集到的数据以及节能策略的预期影响可视化，例如组织和政策的变化或活动的重新安排。这将使决策者能够找出能源浪费的地方(例如，尽管只安排了几次会议，但仍有几个会议室在供暖)，并制定和评估了减少能源消耗的战略。收集的数据还有利于使用新的和新兴的ISO标准的其他建筑系统，这些标准用于使用互联网协议的建筑物中的设备和系统的互操作性。此外，这些数据将使人们更好地了解建筑的使用方式以及热量是如何浪费的。通过物理和虚拟传感器的组合，将建立更准确的建筑物居住者热舒适性的测量，从而帮助解决与建筑物对居住者舒适的不同需求有关的不断出现的投诉和潜在冲突，这也会导致不必要的过热。

Commercial and residential buildings are responsible for a large proportion of carbon dioxide emissions both in the UK and globally. In 2000, 40% of the UK's total non-transport energy use was for space heating, and space heating and hot water accounted for 82% of domestic and 64% of commercial use of energy. Energy demand reduction by commercial buildings can therefore significantly contribute towards achieving the UK's broader energy consumption goals. In contrast to proposals that directly propose behaviour change interventions for the users of commercial office space, this project proposes to address a key deficit in our understanding of the quantity and nature of energy consumption in commercial settings with a view to developing novel holistic solutions including the optimisation of shared resource usage and energy storage facilities. The proposed research plans to tackle this challenge by designing and developing a sensing infrastructure that consists of networked physical (e.g. presence sensors, power consumption sensors) and virtual sensors (e.g. calendar and room booking sensors, application usage sensors) that will provide fine-grained information about how much energy is being used, for what purpose and by whom. By applying techniques from knowledge engineering, activity recognition and machine learning (e.g. Bayesian classifiers) the first stage of our approach will derive higher-level information (e.g. a meeting taking place in a particular room) and will link usage patterns (such as spikes in power consumption) to real-world activities and workflows (e.g. printing off a series of reports for a meeting). In the second stage, this information will be used to parameterise building models used in building management to more accurately predict energy usage and to optimise (decentralised) energy consumption, generation and storage. Based on these models, we will develop a decision support tool that visualises the collected data as well as the expected impact of energy saving strategies such as organisational changes and policies or the rescheduling of activities. This will enable decision makers to identify where energy is being wasted (e.g. several meeting rooms being heated despite only a few meetings being scheduled) and to formulate and evaluate strategies to reduce energy consumption. The data collected also benefits other building systems using new and emerging ISO standards for inter-operability of appliances and systems in buildings using Internet Protocols. In addition, the data will enable a better understanding of the way the building is used and how heat wasted. Through a combination of physical and virtual sensors a more accurate measurement of thermal comfort of the building's occupants will be established and thus assist in resolving ever occurring complaints and potential conflicts associated with the diverse needs for occupant comfort in buildings which also results in unnecessary overheating.