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

Artificial intelligence models for refrigeration, air conditioning and heat pump systems

Artificial intelligence (AI) models for refrigeration, heat pumps, and air conditioners have emerged in recent decades. The universal approximation accuracy and prediction performances of various AI structures like feedforward neural networks, radial basis function neural networks, adaptive neuro�fuzzy inference and recurrent neural networks are encouraging interest. This review discusses existing topographies of neural network models for RHVAC system modelling, energy prediction and fault(s), and detection and diagnosis. Studies show that AI structures require standardization and improvement for tuning hyperparameters (like weight, bias, activation functions, number of hidden layers and neurons). The selection of activation functions, validation, and learning algorithms depends on author’s suitability for a particular application. Backpropagation, error trial selection of the number of hidden layer, and hidden layers’ neurons, and Levenberg–Marquardt learning algorithms, remain prevalent methodologies for developing AI structures. The major limitations to the application of AI models in RHVAC systems include exploding or/and vanishing gradients, interpretability, and accuracy trade off, and training saturation and limited sensitivity. This review aims to give up-to-date applications of different AI architectures in RHVAC systems and to identify the associated limitations and prospect

Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository

Thermal behaviour model identification for three different office buildings

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The thermal behaviour was investigated of three offices positioned in three buildings built in different periods, one academic institute built in 1920 and two modem commercial buildings in London. The buildings chosen for this study are the Rockefeller Building, which is part of University College London (UCL), Portman House in Oxford Street and the Visa Building in Paddington. Due to the lack of specific information related to the structure of the buildings such as windows, doors, building dimensions and other information that would allow the use of physical models, in this project black-box linear and non-linear mathematical models were used. Data relating to room temperature, hot and chilled water temperature, air flow and temperature from air handling units and outside temperature were collected for one year, from the actual building management systems (BMSs) installed in these buildings. The main assumption of the model development in the three buildings was that although occupancy, computers, printers etc cause an additional internal heat gain, their impact is in part indirectly included in the model. The primary objective of the analysis was to identify the inputs (independent variables) that gave good models for the prediction of room temperature for a certain period. Consequently, the process of input selection and period of validity in obtaining models that give good thermal prediction (within the same period) were the key points in season subdivision. The first part of the analysis applied the following linear parametric mathematical models to the three office buildings selected: Box Jenkins (BJ), autoregressivem oving averagew ith exogenousi nput (ARMAX) and output error (OE) structure. The project then deals with non-linear mathematical models. The same inputs selected and assumptions made with linear analysis were used to build, in turn, models with feedforward backpropagation (FFBP), non-linear autoregressive mathematical models with parallel arrangement (NARX) and series-parallel arrangement (NARXSP). The research presented in this project is related to developing models for three real offices positioned in three different buildings whereas previous researchers have applied these models mainly to experimental rooms and HVAC plants, with the purpose of fault detection and diagnostics. Furthermore, in the past, research on thermal model development has been related to one office or HVAC plant, and for a limited period of time (a few weeks or months). In contrast, this study undertakes an overall analysis of thermal model development for three offices and for a period of one year, where the process of input selection is given priority to obtain good models. Thus, previous studies have not utilized these two types of models for such a long period of data collection nor related them to three different buildings. Finally, model development and then validation were pursued utilizing the same week, different weeks and different days (where the first part of the data in each case was used for model estimation and the following part for model validation). This was one within the period that the models gave good results for the prediction of room temperature. The best mathematical models (linear and non-linear) that predict the room temperature, in terms of the inputs selected, has been determined for each season. The procedures for how to choose the best models are based on the following techniques: final prediction error (FPE for linear models), mean squared error (mse for non-linear models), and model fits and errors between measurements and simulated model output. Overall, the results related for the prediction of room temperature with non-linear models, are better than those obtained with linear models, as a result of comparison between models' errors, FPE and mse obtained with linear and non-linear models.Funding was obtained from the Brunel University Department of Mechanical Engineering

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

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

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones