All you need to know about model predictive control for buildings

https://escholarship.org/content/qt5rh8s2hc/qt5rh8s2hc.pdf?t=qaoodo

Reinforcement learning for building controls: The opportunities and challenges

A review of operating performance in green buildings: Energy use, indoor environmental quality and occupant satisfaction

https://escholarship.org/content/qt5zt6c00q/qt5zt6c00q.pdf?t=q7nfab

State-of-the-art on research and applications of machine learning in the building life cycle

https://escholarship.org/content/qt3f50w69v/qt3f50w69v.pdf?t=q7nn3a

Building thermal load prediction through shallow machine learning and deep learning

/pdf/modestpy-an-open-source-python-tool-for-parameter-estimation-49831jfir8.pdf

ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.

/pdf/a-digital-twin-framework-for-improving-energy-efficiency-and-ict0hpv73p.pdf

A Digital Twin Framework for Improving Energy Efficiency and Occupant Comfort in Public and Commercial Buildings

Sensing accurately the number of occupants in the rooms of a building enables many important applications for smart building operation and energy management. A range of sensor technologies has been studied and applied to the problem. However, it is costly to achieve high accuracy by instrumenting all rooms in a building with dedicated occupant sensors. In this paper, we propose a new concept for estimating accurate room-level counts of occupants. The idea is to disaggregate accurate building-level counts via existing common sensors available at the room level. This solution is cost-effective as it scales to large buildings without requiring dedicated sensors in each room. We propose an algorithm named DCount that implements this concept. Our results document that DCount can provide room-level counts with a low normalized root mean squared error of 0.93. This is a major improvement compared to a state-of-the-art algorithm using common sensors and ventilation rate measurements resulting in a normalized root mean squared error of 1.54 on the same data set. Further more, we demonstrate how the results enable occupant-driven analysis of plug-load consumption which is one out of many applications using accurate room-level counts of occupants we hope to enable by proposing DCount.

/pdf/dcount-a-probabilistic-algorithm-for-accurately-3xaaz426y0.pdf

DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts

One of the main challenges facing the building sector nowadays is the reported mismatch between the predicted and the actual performance throughout the building operational phase. This mismatch is referred to as the ‘building performance gap’. In this regard, the need for a systematic continuous commissioning framework to monitor, assess and evaluate the buildings performance is vital to bridge the performance gaps. In this paper, an innovative framework for building energy performance monitoring and evaluation is presented, considering a list of performance tests addressing building subsystems. The framework relies on two major pillars, actual data collected from the building site, and calibrated energy model simulations to serve as a dynamic baseline for comparison and evaluation. The framework design, development and implementation in a highly energy efficient building is presented, and findings from the initial stages of implementing the framework are highlighted considering the energy systems operation and indoor comfort perspectives.

An automated framework for buildings continuous commissioning and performance testing – A university building case study

Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To the best of our knowledge, this is the first time that this method is used for applications in smart buildings. First a dynamic model for a zone in a real-case building is developed in which faults are also introduced. Then fault diagnosability is investigated by analyzing the fault isolability of the model. Based on the investigation, it was concluded that not all the faults in the model are diagnosable. Then an approach for placing new sensors is implemented. It is observed that for two test scenarios, placing additional sensors in the model leads to full diagnosability. Since sensors placement is key for an effective FDD process, the optimal placement of such sensors is also studied in this work. A case study of campus building OU44 at the University of Southern Denmark is considered. The results show that as the system gets more complicated by introducing more faults, additional sensors should be added to achieve full diagnosability.

https://www.mdpi.com/1996-1073/12/9/1601/pdf

Krzysztof Arendt

Papers

ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units

A Digital Twin Framework for Improving Energy Efficiency and Occupant Comfort in Public and Commercial Buildings

DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts

An automated framework for buildings continuous commissioning and performance testing – A university building case study

Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings