Unlocking the Potential of Model-Predictive Control in Non-domestic Building Energy Management: Automated Configuration and Optimisation of Control

非住宅建筑目前产生了英国18%的碳排放，其中40%是由于空间供暖/制冷。控制方面的创新预计将节省这一数字的25%，为英国的碳减排目标做出了相当大的贡献。然而，这种创新还必须解决建筑物翻新和新建的问题，因为建筑物的更新率相当低。本提案旨在通过使模型预测控制（MPC）成为一种经济可行的建筑控制技术，从而实现非住宅建筑能源管理的阶段性变化。MPC非常适合于控制建筑物，通常在输入应用和可观察响应之间具有较大的时间延迟。重要的是，MPC明确基于优化，与当前使用经验制定规则的建筑管理系统不同。目前，预测动力学模型的生成和更新是MPC的核心，必须由高技能的控制工程师手动完成。尽管在一项研究中，MPC已经被证明可以在非住宅建筑中使用手工制作的预测模型实现节能25%，但手工调整这些模型的成本目前难以用于商业部署。我们将使用先进的机器学习技术，根据从运行中的建筑物获取的数据自动生成预测动态模型；因此，我们将捕捉实际建筑物的关键特征，例如居住者对建筑物能源性能的深刻影响。更重要的是，我们的方法将能够定期更新预测模型，以适应建筑结构、使用和当地环境的不可避免的变化，所有这些都会影响建筑的热动力学，从而影响其能源效率。为了经济有效地展示研究理念的潜力，我们将广泛使用计算机模拟建筑，探索各种不同的建筑形式、天气条件和改造。我们亦会发展一套适用于模拟非住宅楼宇的随机模型，以便更好地考虑楼宇使用者对能源表现的影响。我们将在不同的真实建筑上进行实际演示，以补充这些广泛的模拟研究，其中一个将是被动设计学校；控制被动式设计建筑的峰值温度是一个众所周知的挑战，特别是在夏季。该项目的总体结果将是促进建筑控制的逐步变化，以最小限度地使用主要的、不可再生的能源，产生一个可接受的内部环境。这源于MPC明确基于优化的事实。除了有助于实现碳减排目标和提高工作场所的福祉，这也将为建筑运营商节省大量成本。我们将通过各种渠道，包括项目结束研讨会，向英国建筑服务社区传播关于MPC及其对碳减排的潜在贡献的知识。

Non-domestic buildings currently generate 18% of the UK's carbon emissions, >40% of which is due to space heating/cooling. Innovations in control are predicted to save > 25% of this figure making a sizeable contribution to the UK's carbon reduction target. Such innovations, however, must also address building refurbishment as well as new build since the rate of building replacement is rather low.This proposal aims to effect a step change in the building energy management of non-domestic buildings by making model-predictive control (MPC) an economically-viable control technology for buildings. MPC is well-suited to controlling buildings, which typically have a large time delay between the application of an input and observable response. Importantly, MPC is based explicitly on optimisation as distinct from current building management systems, which use empirically-crafted rules. Currently, the generation and updating of the predictive dynamical model, which lies at the heart of MPC, has to be done manually by highly-skilled control engineers. Although MPC has been demonstrated to achieve energy savings >25% in non-domestic buildings in a research setting with hand-crafted predictive models, the cost of hand tuning these models is currently prohibitive for commercial deployment. We will use advanced machine learning techniques to automatically produce the predictive dynamical model from data acquired from the building-in-operation; hence we will capture key characteristics of the actual building, such as the profound influence of occupants on the building's energy performance. More than this, our approach will be able to periodically update the predictive model to accommodate the inevitable changes in building fabric, use and local environment, all of which will affect the building's thermal dynamics and hence its energy efficiency.To cost-effectively demonstrate the potential of the research idea, we will make extensive use of building simulation by computer to explore a diverse range of different building forms, weather conditions, and modifications. We will also develop a stochastic model suitable for use in the simulation of non-domestic buildings allowing us to to better account for the impact of building occupants on energy performance. We will supplement these extensive simulation studies with a practical demonstration on different real buildings, one of which will be a passive design school; controlling peak temperatures in passive design buildings is a known challenge particularly in the summer months.The overall outcome of this project will be to facilitate a step change in building control, producing an acceptable internal environment with the minimum use of primary, non-renewable energy. This derives from the fact that MPC is explicitly based on optimisation. As well as contributing to carbon reduction targets and greater workplace well-being, this will also produce significant cost savings for building operators.We will disseminate knowledge about MPC and its potential contributions to carbon reduction to the UK building services community using a variety of channels, including an end-of-project workshop.