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

Advanced energy management strategies for HVAC systems in smart buildings

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupant

Advanced energy management strategies for HVAC systems in smart buildings

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupantsPostprint (published version

Low delta-T syndrome in cooling systems:A systematic review of the signs, symptoms, and causes

Return water temperature and flow rate are indicators of the energy efficiency of chilled water systems. Since the late 1980s, the return water temperature has deviated from the designed value, resulting in an increased flow rate. Such deviations have been recognized as a persistent ‘disease’ named low delta-T syndrome. Based on a medical approach, this study aimed to categorise the key signs and symptoms, and causes to classify low delta-T syndrome into subclasses with individual properties; to connect individual causes to the subclasses; and to identify disagreements on individual causes. Through a systematic review of the literature, over 190 papers published since the late 1980s were identified and studied. By combining different return water temperature profiles and flow rates, low delta-T syndrome was classified into four subclasses with severities ranging from 1 (mild) to 4 (extreme). These subclasses were described with 12 signs and symptoms, each characterised by 19 (from a total of 52) individual or combined causes, to provide an improved overview and a fundamental basis for developing treatments. A fundamental analysis of low delta-T syndrome on a cooling coil revealed that cooling coils with a high chilled water temperature difference and a high chilled water supply temperature at design conditions have a higher risk of developing it. This literature review provides an improved understanding of as well as considerations regarding how to prevent, resolve, mitigate, and handle low delta-T syndrome during design and operation

Demonstrating a smart controller in a hospital integrated energy system

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Modelling and data validation for the energy analysis of absorption refrigeration systems

Data validation and reconciliation techniques have been extensively used in the process industry to improve the data accuracy. These techniques exploit the redundancy in the measurements in order to obtain a set of adjusted measurements that satisfy the plant model. Nevertheless, not many applications deal with closed cycles with complex connectivity and recycle loops, as in absorption refrigeration cycles. This thesis proposes a methodology for the steady-state data validation of absorption refrigeration systems. This methodology includes the identification of steady-state, resolution of the data reconciliation and parameter estimation problems and the detection and elimination of gross errors. The methodology developed through this thesis will be useful for generating a set of coherent measurements and operation parameters of an absorption chiller for downstream applications: performance calculation, development of empirical models, optimisation, etc. The methodology is demonstrated using experimental data of different types of absorption refrigeration systems with different levels of redundancy.Los procedimientos de validación y reconciliación de datos se han utilizado en la industria de procesos para mejorar la precisión de los datos. Estos procedimientos aprovechan la redundancia enlas mediciones para obtener un conjunto de datos ajustados que satisfacen el modelo de la planta. Sin embargo, no hay muchas aplicaciones que traten con ciclos cerrados, y configuraciones complejas, como los ciclos de refrigeración por absorción. Esta tesis propone una metodología para la validación de datos en estado estacionario de enfriadoras de absorción. Estametodología incluye la identificación del estado estacionario, la resolución de los problemas de reconciliación de datos y estimación de parámetrosy la detección de errores sistemáticos. Esta metodología será útil para generar un conjunto de medidas coherentes para aplicaciones como: cálculo de prestaciones, desarrollo de modelos empíricos, optimización, etc. La metodología es demostrada utilizando datos experimentales de diferentes enfriadoras de absorción, con diferentes niveles de redundancia

Performance modelling and validation of biomass gasifiers for trigeneration plants

Esta tesis desarrolla un modelo sencillo pero riguroso de plantas de trigeneración con gasificación de biomasa para su simulación, diseño y evaluación preliminar. Incluye una revisión y estudio de diferentes modelos propuestos para el proceso de gasificación de biomasa.Desarrolla un modelo modificado de equilibrio termodinámico para su aplicación a procesos reales que no alcanzan el equilibrio así comodos modelos de redes neuronales basados en datos experimentales publicados: uno para gasificadores BFB y otro para gasificadores CFB. Ambos modelos, ofrecen la oportunidad de evaluar la influencia de las variaciones de la biomasa y las condiciones de operación en la calidad del gas producido. Estos modelos se integran en el modelo de la planta de trigeneración con gasificación de biomasa de pequeña-mediana escala y se proponen tres configuraciones para la generación de electricidad, frío y calor. Estas configuraciones se aplican a la planta de poligeneración ST-2 prevista en Cerdanyola del Vallés.This thesis develops a simple but rigorous model for simulation, design and preliminary evaluation of trigeneration plants based on biomass gasification. It includes a review and study of various models proposed for the biomass gasification process and different plant configurations. A modified thermodynamic equilibrium model is developed for application to real processes that do not reach equilibrium. In addition, two artificial neural network models, based on experimental published data, are also developed: one for BFB gasifiers and one for CFB gasifiers. Both models offer the opportunity to evaluate the influence of variations of biomass and operating conditions on the quality of gas produced. The different models are integrated into the global model of a small-medium scale biomass gasification trigeneration plant proposing three different configurations for the generation of electricity, heat and cold. These configurations are applied to a case study of the ST-2 polygeneration plant foreseen inCerdanyola del Valles

PREDICTIVE ANALYTIC MODEL FOR ELECTRICAL CHILLER SYSTEM (ECC)

District cooling plant is a very complex system consisting of various equipment. One of them is an electric centrifugal chiller that is widely used in the industry to supply chilled water to thermal energy storage (TES). This paper investigates the predictive analytics model of the electric chiller system using real operation data. Data pre-processing was performed to remove the outliers. Further to that, the machine learning model was used to develop an artificial neural network (ANN), whereby the best model with the lowest RMSE was obtained. Then, the ANN was used to correlate between the input and output variables to find the critical parameters which contributed to the Coefficient of Performance (COP). This model could also predict the output from the actual data of the COP. Lastly, the Shewhart control chart was utilised in the root cause analysis model (RCA) to detect the anomalies based on five critical parameters at the early stage before its failure. The supervised learning algorithms using the feedforward ANN model demonstrates the most accurate predictions compare to all models