New methods and models for the ongoing commissioning of HVAC systems in commercial and institutional buildings

The performance of the HVAC systems in buildings tends to decrease after few years of operation. Equipment and sensors degradation lead to remarkable wastes of energy and money, as well as to the increase of building occupants thermal discomfort. HVAC ongoing commissioning (OCx), the continuation of HVAC commissioning well into the occupancy and operation phase of a building life, has been recognized as a cost-effective strategy to reduce energy wastes, equipment degradation and thermal discomfort. Building Automation Systems (BAS) collect and store huge amount of data for the purpose of building systems control. Those data represent a golden mine of information that can be used for the OCx of the building HVAC systems. This research work develops and validates new methods and models to be used for the OCx of HVAC systems using BAS measurements from commonly installed sensors. A Fault Detection and Identification (FD&I) method for chillers operation, and several virtual sensor models for variables of interest in Air Handling Units (AHUs) are presented. A FD&I method based on Principal Components Analysis (PCA) has been developed and used to detect abnormal operation conditions in an existing chiller operation and identify the responsible variables. The proposed FD&I method has been trained using measurements from summer 2009, and then used to detect abnormal observations from the following seven summer seasons (2010-2016). When the detected abnormal observations were replaced with artificially generated fault-free data, the proposed FD&I method did not detect any abnormal value along those artificially faulty-free variables. In summer 2016 the building operators changed several HVAC system operation set points, the FD&I method was effective in detecting almost 100% of the observations and properly identifying those variables whose set point was changed. For two different operation modes of an AHU several virtual outdoor air flow meters have been developed and the predictions have been compared against short-term measurements using uncertainty analysis and statistical indices. Three models have been investigated when the heat recovery coil was off. Results showed that the model with the simplest mathematical formulation was the most accurate, with the lowest value of uncertainty. When a heat recovery coil at the fresh air intake was on, two virtual flow meters have been developed to predict the outdoor air flow rate without the need of additional sensors. Both the models predicted the outdoor air ratio with good statistical indices: the Mean Absolute Error (MAE) was 0.015 for model a and 0.016 for model b. Three methods for the virtual measurement and/or calibration of air temperature and relative humidity have been developed for different AHU operation modes. These methods are different in terms of modelling strategy, information needed and technical knowledge required for implementation. For instance, results from the correction of the faulty measurements of the outdoor air temperature along a 24 hours period using Method A showed a high virtual calibration capability: MAE = 0.2°C and the Coefficient of Variation, CV-RMSE = 1.7%. A new definition of virtual sensor is proposed at the end of this research work. From a review of publications on virtual sensors for building application, the two most recurrent reason for the implementation of virtual sensor models (costs and practical issues) have been highlighted and integrated into the proposed new definition

A Practical Data-Driven Multi-Model Approach to Model Predictive Control: Results from Implementation in an Institutional Building

Model-based Predictive Control (MPC) is an effective solution to improve building controls. It consists of the use of weather and occupancy forecasts along with a control-oriented model to predict the behaviour of the building a few hours or days ahead, and thus optimize the operation of its systems. Although the potential of MPC is widely recognized, and plentiful operational data is often available, the development of a model requires a great deal of effort, significant technical expertise and knowledge of building systems. The challenge of creating a model is a hurdle that makes the on-site implementation of MPC in buildings relatively rare. This study tackles the development of a multi-model approach to optimize the operation of electric and natural gas boilers in an institutional building to reduce greenhouse gas (GHG) emissions while maintaining the required level of comfort. This methodology leverages Machine Learning techniques to rapidly develop and calibrate control-oriented models using a limited number of input variables (indoor air temperature and temperature set-points, weather conditions, power meter data). The proposed multi-model approach consists of five models used to estimate the building total heating demand, the electric baseload, the natural gas boiler power, and the indoor air temperature under free floating conditions and during warming-up periods in the morning. The models are calibrated and validated with operational data and they are then used to optimize the transition between nighttime and daytime indoor air temperature. Since these are black-box models that require only a basic understanding of the building system and a few inputs, the model development was considerably reduced while the modularity of the proposed method makes it flexible. Such an approach could therefore be easily replicated in other buildings equipped with similar pieces of equipment. This methodology has been implemented in a Canadian institutional building, located in Varennes (QC). Results in 2020-21 showed that the COVID-19 pandemic has significantly impacted building performance and reduced energy use, thus creating a new baseline. The MPC strategy allowed to achieve an additional 20.2% GHG emission reduction compared to this new baseline while thermal comfort was improved. Nevertheless, energy costs increased, which was mainly due to the impact of the pandemic, which eventually made the pre-COVID-19 model and optimization parameters outdated; lower costs are expected with model recalibration, currently ongoing

Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study

Model-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging cycles, etc. An obstacle to the adoption of MPC is the modelling step: developing a dedicated control-oriented model is a time-consuming process, requiring technical expertise and a large amount of information about the building and its operation. To overcome these issues, this paper proposes a new approach for the development of MPC strategies based on Artificial Intelligence (AI) techniques, aiming to map correlations among commonly available operation variables and to develop models suitable for predictive control. The proposed approach was applied in an institutional building in Varennes, QC, with the aim of reducing the natural gas consumption during the heating season. Early results show a remarkable effectiveness of the proposed approach, with a reduction of natural gas and building heating consumption of 23.9% and 6.3%, respectively