The applicability of grey-box performance-based modelling techniques in existing office buildings in the UK

基于灰盒性能的建模技术在英国现有办公楼中的适用性

• 批准号: 2618266
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2618266
• 关键词: applicability; grey; box; performance; based

This Ph.D. investigates the applicability of grey-box performance-based modelling techniques to describe the thermal dynamics of an existing office building in the UK. The project aims to answer the question of how well grey-box models can predict the internal air temperature and heat loss of an existing office building, in order to evaluate retrofit options and control strategies. An ultra energy-efficient Passive House office building is studied as the case study to identify the best grey-box model structure in terms of providing the highest accuracy with minimum complexity and computational cost. The type, amount and quality of data coming from sensors of a Building Management System (BMS) and the level of detail (LOD) required to inform the model parameters for the office building will be explored.With the wide adoption of building automation systems (BAS) and the Internet of Things (IoT) in buildings, numerous measurements pertaining to the functioning of the buildings and their equipment are continuously gathered by sensors and other sources. This offers numerous chances to create data-driven models for building control and operation. Data-driven modelling, also known as performance-based modelling or inverse approach, is based on measured data after buildings are occupied. These models reflect the actual building thermal dynamics and provide more accurate predictions of building thermal responses. Therefore, performance-based models can be used in model-based control of space heating and cooling, fault detection of mechanical systems, retrofit evaluation for reducing operation energy consumption, shifting and shaving peak demand, and performance monitoring. Moreover, the uncertainties in Building Performance Simulation (BPS) can be reduced if the modelling process can also make use of operational measurements, recorded during building's operation. These uncertainties can result from of a wide range of dynamic, stochastic, and probabilistic elements such as building geometry, material properties, HVAC systems, occupant behaviour, appliance, use scheduling and even weather data, and increase in the case of existing buildings, where less information is available.Although machine learning techniques, such as Artificial Neural Networks (ANN) have been extensively adopted for building energy use prediction in recent decades, despite their high prediction accuracy, these techniques have some drawbacks, including a high demand for data quality, lack of interpretability and intense computational requirements. This approach also produces models with low generalisation between different buildings, which makes building-to-building comparisons of models difficult. Comparing the thermal behaviour of buildings can be of interest for the purposes of energy consumption classification. Grey-box models are a type of data-driven models that retain some physical meaning, while their parameters are calibrated using measured data. Therefore, the grey-box model is more interpretable than machine learning approach and is more computationally efficient.

这个博士调查灰箱性能为基础的建模技术来描述在英国现有的办公楼的热动力学的适用性。该项目旨在回答灰箱模型如何预测现有办公楼的内部空气温度和热损失的问题，以评估改造方案和控制策略。一个超节能的被动房办公楼的案例研究，以确定最好的灰箱模型结构，提供最高的精度，最小的复杂性和计算成本。本课程将探讨楼宇管理系统（BMS）传感器数据的类型、数量和质量，以及为办公楼提供模型参数所需的细节层次（LOD）。随着楼宇自动化系统（BAS）和物联网（IoT）在楼宇中的广泛采用，通过传感器和其它源连续地收集与建筑物及其设备的功能有关的许多测量。这为创建用于建筑控制和运营的数据驱动模型提供了许多机会。数据驱动建模，也称为基于性能的建模或逆方法，是基于建筑物被占用后的测量数据。这些模型反映了实际的建筑热动力学，并提供更准确的预测建筑热响应。因此，基于性能的模型可以用于基于模型的控制的空间加热和冷却，机械系统的故障检测，改造评估，以减少运行能耗，转移和削峰需求，和性能监测。此外，在建筑性能模拟（BPS）的不确定性可以减少，如果建模过程中也可以使用的操作测量，在建筑物的操作过程中记录。这些不确定性可能来自广泛的动态、随机和概率元素，例如建筑物几何形状、材料属性、HVAC系统、居住者行为、电器、使用调度甚至天气数据，并且在现有建筑物的情况下增加，其中可用的信息较少。近几十年来，人工神经网络（ANN）等技术已被广泛用于建筑能源使用预测，尽管这些技术的预测准确率很高，但这些技术也存在一些缺点，包括对数据质量的要求很高，缺乏可解释性和强烈的计算要求。这种方法也产生了不同建筑物之间的低概括模型，这使得建筑物之间的模型比较困难。比较建筑物的热行为对于能源消耗分类可能是有意义的。灰箱模型是一种数据驱动的模型，它保留了一些物理意义，而它们的参数是使用测量数据校准的。因此，灰箱模型比机器学习方法更可解释，并且计算效率更高。