Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Bayesian Networks for Whole Building Level Fault Diagnosis and Isolation

Buildings consume more than 40% of primary energy in the U.S. and 57% of the energy usage in commercial and residential buildings are consumed by the heating, ventilation and air conditioning (HVAC) system.Malfunctioning sensors, components, and control systems, as well as degrading systems in HVAC and lighting systems are main reasons for energy waste and unsatisfactory indoor environment. In HVAC systems, faults occur in one component or equipmentcan also cause abnormality in other subsystems because of the coupling among different subsystems. Therefore, whole building level fault diagnosis methods is critical to locate fault root cause and isolate the fault. Bayesian network (BN) is a prevalent toolin fault diagnosis which can deal withprobabilistic reasoning of uncertainty. In this paper, a two-layer Bayesian network which consists of fault layer and fault symptom layer is developed to diagnose whole building HVAC system fault. Weather information based Pattern Matching (WPM) method which was employed in fault detection was also used to create baseline data and generate LEAK probability. BAS data from a campus building are collected to evaluate the effectiveness of the proposed method

A Probabilistic Framework To Diagnose Faults in Air Handling Units.

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration, which can result in, hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. Although APAR has many advantages over other methods, for example, no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. There is no established way to have the correct diagnosis for rule based fault detection system. In this study, we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief Network. BBN is used as a decision support tool for rule-based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. The proposed model tested with real time measured data of a campus building at University of Texas at San Antonio (UTSA)

Fault Detection for Systems with Multiple Unknown Modes and Similar Units

This dissertation considers fault detection for large-scale practical systems with many nearly identical units operating in a shared environment. A special class of hybrid system model is introduced to describe such multi-unit systems, and a general approach for estimation and change detection is proposed. A novel fault detection algorithm is developed based on estimating a common Gaussian-mixture distribution for unit parameters whereby observations are mapped into a common parameter-space and clusters are then identified corresponding to different modes of operation via the Expectation- Maximization algorithm. The estimated common distribution incorporates and generalizes information from all units and is utilized for fault detection in each individual unit. The proposed algorithm takes into account unit mode switching, parameter drift, and can handle sudden, incipient, and preexisting faults. It can be applied to fault detection in various industrial, chemical, or manufacturing processes, sensor networks, and others. Several illustrative examples are presented, and a discussion on the pros and cons of the proposed methodology is provided. The proposed algorithm is applied specifically to fault detection in Heating Ventilation and Air Conditioning (HVAC) systems. Reliable and timely fault detection is a significant (and still open) practical problem in the HVAC industry { commercial buildings waste an estimated 15% to 30% ($20.8B -$41.61B annually) of their energy due to degraded, improperly controlled, or poorly maintained equipment. Results are presented from an extensive performance study based on both Monte Carlo simulations as well as real data collected from three operational large HVAC systems. The results demonstrate the capabilities of the new methodology in a more realistic setting and provide insights that can facilitate the design and implementation of practical fault detection for systems of similar type in other industrial applications

Semi-supervised transfer learning methodology for fault detection and diagnosis in air-handling units

Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Fault detection and diagnosis of low delta-T syndrome in air handling unit cooling coils

In the built environment, most energy is used to promote well-being, health, and comfort. The demand for cooling will increase sharply as a result of global warming, better thermal insulation, and the heat island effect. It is therefore increasingly important that cooling installations function optimally. Currently, there are many chilled water installations that suffer from the so-called low delta-T syndrome. The return water temperature from the installations is lower than predetermined and the difference with the supply temperature is smaller, low delta-T. This has adverse consequences for the efficiency of the chillers and/or heat pump and for the energy consumption of the pumps. The result is an energy consumption that is 20-40% higher for cooling than calculated in advance. It is important to be able to detect and analyze the low-dT syndrome properly. Based on this, a software module has been developed that can use the data from a building management system to determine the low-dT syndrome and identify possible causes. Building management systems can be equipped with fault detection and diagnosis module for continuous monitoring of the performance of installations, and continuous commissioning (Cx). This would ensure that the energy consumption of the cooling installations remains as low as possible. Within the project, the first prototype of such a module was built. This will be further refined and expanded in ongoing future projects of other PDEng trainees