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

释放模型预测控制在非住宅建筑能源管理中的潜力：控制的自动配置和优化

• 批准号: EP/N022351/1
• 负责人: Peter Rockett
• 金额: $ 68.88万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN022351%2F1
• 关键词: Unlocking; Potential; Model; Predictive; Control

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.

目前，非住宅建筑产生的碳排放量占英国碳排放量的 18%，其中超过 40% 是由于空间供暖/制冷造成的。控制创新预计将节省超过 25% 的数字，为英国的碳减排目标做出巨大贡献。然而，此类创新还必须解决建筑物翻新和新建问题，因为建筑物更换率相当低。该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物控制技术，实现非住宅建筑能源管理的重大变革。 MPC 非常适合控制建筑物，这些建筑物通常在输入应用和可观察响应之间有很大的时间延迟。重要的是，MPC 明确基于优化，这与当前使用经验制定规则的建筑管理系统不同。目前，作为 MPC 核心的预测动态模型的生成和更新必须由高技能的控制工程师手动完成。尽管 MPC 已被证明可以在研究环境中通过手工制作的预测模型在非住宅建筑中实现超过 25% 的节能，但手工调整这些模型的成本目前对于商业部署来说是过高的。我们将使用先进的机器学习技术，根据从运行中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的关键特征，例如居住者对建筑能源性能的深远影响。不仅如此，我们的方法将能够定期更新预测模型，以适应建筑结构、使用和当地环境不可避免的变化，所有这些都会影响建筑物的热动力学，从而影响其能源效率。为了经济高效地展示研究想法的潜力，我们将广泛使用计算机建筑模拟来探索各种不同的建筑形式、天气条件和修改。我们还将开发一个适用于非住宅建筑模拟的随机模型，使我们能够更好地考虑建筑居住者对能源性能的影响。我们将通过对不同真实建筑的实际演示来补充这些广泛的模拟研究，其中之一将是被动式设计学校；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。该项目的总体成果将是促进建筑控制的逐步改变，以最少的一次不可再生能源的使用产生可接受的内部环境。这是因为 MPC 明确基于优化。除了有助于实现碳减排目标和改善工作场所福祉外，这还将为建筑运营商带来显着的成本节省。我们将通过各种渠道（包括项目结束研讨会）向英国建筑服务界传播有关 MPC 的知识及其对碳减排的潜在贡献。

项目成果

Nonlinear dynamic system identification and model predictive control using genetic programming

使用遗传编程的非线性动态系统识别和模型预测控制

• 发表时间: 2019
• 作者: Dou Tiantian

GPML: an XML-based standard for the interchange of genetic programming trees

• DOI: 10.1007/s10710-019-09370-4
• 发表时间: 2019-11
• 期刊: Genetic Programming and Evolvable Machines
• 影响因子: 2.6
• 作者: Tiantian Dou;Yuri Kaszubowski Lopes;P. Rockett;E. A. Hathway;Esmail Saber

Model predictive control of non-domestic heating using genetic programming dynamic models

• DOI: 10.1016/j.asoc.2020.106695
• 发表时间: 2020-12-01
• 期刊: APPLIED SOFT COMPUTING
• 影响因子: 8.7
• 作者: Dou, Tiantian;Lopes, Yuri Kaszubowski;Saber, Esmail
• 通讯作者: Saber, Esmail