Josh Wall

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Graphical abstractDisplay Omitted HighlightsOur algorithm aims to improve the feature quality in general fault diagnosis system.The algorithm filters out redundant features using consensus evolutionary clustering.The algorithm was tested on the ASHRAE-1312-RP experimental fault data.Sensitivity & specificity were >95%, with considerably less false positives up to as low as 1.6%. Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI)=0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI=0.0199) Complete Linkage (Median NMI=0.1305), Evidence Accumulation K-means (Median NMI=0.04) and Weighted Evidence Accumulation K-means (Median NMI=0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.

Dynamic zone modelling for HVAC system control

This paper presents the development and validation of a dynamic zone model used for improved control of a heating, ventilation and air conditioning (HVAC) system to reduce energy consumption and improve the quality of the indoor environment. In particular, the paper focuses on a zone modelling technique that uses physical-principles based real-time model fitting and prediction methodology, taking advantage of genetic algorithm based problem solving. An air-conditioning zone model is deduced from an energy and mass balance and then expressed in terms of electric circuit theory, where the electric circuit is used to represent functions of the building elements. Experimental results for real-time zone model fitting and prediction are given. The results show that our model is capable of accurately predicting the indoor temperature of a dynamic zone. This dynamic model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.

Pervasive Computing Approaches to Environmental Sustainability

This issues Works in Progress department lists eight projects with a focus on environmental sustainability. The first three projects explore sensing and pervasive computing techniques for monitoring environmental conditions in outdoor situations. The next four projects use pervasive computing in indoor environments to inform individuals about their energy and resource consumption with the goal of positively influencing their behaviors. The final project aims to develop an energy generation infrastructure that combines multiple types of renewable energy sources.

Automatic Feature Selection Using Multiobjective Cluster Optimization for Fault Detection in a Heating Ventilation and Air Conditioning System

The performance of Automatic Fault Detection and Diagnostics (AFDD) algorithms to identify faults in complex building Heating Ventilation and Air-Conditioning (HVAC) systems depend on the appropriateness of features. This paper proposes a knowledge-discovery approach for discovering characteristic features using Multi-Objective Clustering Rapid Centroid Estimation (MOC-RCE). The proposed method has been tested on experimental fault data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) research project 1312-RP Winter 2008 dataset. An experiment involving 100 clustering trials shows that using the proposed method, on average 15 characteristic features have been selected from the original 320 features. Sensitivity, specificity, accuracy, precision, and F-score values of greater than 95% are achieved with the provided features.

HVAC control strategies for thermal comfort and indoor air quality

With the building sector contributing over 30% of global carbon dioxide (CO2) emissions, sustainable building technologies have been identified as one of the most cost-effective approaches for improving energy efficiency and reducing our carbon footprint on the environment. This paper presents two-HVAC control strategies to maintain adequate thermal comfort and indoor air quality with least energy consumption. One is based on virtual comfort sensing, which is an informative software tool that provides a means for informing building occupants about key indicators relating to building energy performance. Another control strategy, which is based on monitoring and modelling of indoor CO2 concentration, is employed to respond to the changes of indoor CO2 generation through appropriate adjustment of ventilation rates, i.e., the rate of ventilation is modulated over time based on the signals from indoor CO2 concentration. In particular, the paper focuses on the development of adaptive indoor air quality model based on soft real-time indoor occupant prediction for implementing control strategies. This dynamic indoor air quality model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.

Real-Time HVAC Sensor Monitoring and Automatic Fault Detection System

Real-time monitoring of heating, ventilation, and air conditioning (HVAC) systems is crucial to maintaining optimal performance such as providing thermal comfort and acceptable indoor air quality, guaranteeing energy saving, and assuring system reliability. In a realistic situation, HVAC systems can degrade in performance or even fail due to a variety of operational problems, such as stuck open or closed air dampers and water valves, supply or exhaust air fan faults, hot or chilled water pump faults, and inefficiencies in the way HVAC systems or pieces of equipemnt are controlled. This paper presents automatic fault detection techniques, as well as a key sensor sets selection approach that can help to maintain the performance of HVAC systems, and optimise fault detection results. One key step to make sure the approach succeeds is the sensor feature selection process. This paper implements the ensemble rapid centroid estimation (ERCE) as the data-driven sensor and feature selection algorithm, which is the core method to assure the automatic fault detection can function correctly. Instead of choosing sensors manually, ERCE method can automatically select representative features that are unique and relevant to the faults in a HVAC system. The methodology presented is implemented in real-world commercial buildings with experimental results showing that different types of faults are detected successfully.

Experiences with Sensors for Energy Efficiency in Commercial Buildings

Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however, can only be taken if the building control system has access to reliable real-time data. Sensors providing this data need to be ubiquitous, accurate, have low maintenance cost, and should not violate privacy of building occupants. We conducted a 3 year study in a mid-size office space with 15 offices and 25 people. Specifically, we focused on sensing modalities that can help improve energy efficiency of buildings. We have deployed 25 indoor climate sensor nodes and 41 wireless power meters, submetered 12 electric loads in circuit breaker boxes, logged data from our building control system and tracked activity on 40 desktop computers. We summarize our experiences with the cost, data yields, and user privacy concerns of the different sensing modalities and evaluate their accuracy using ground-truth experiments.

What Comes After the Low-Hanging Fruit?

This chapter considers the next generation of energy efficiency approaches, designed to realize significant energy reductions in already-efficient buildings. Three quite different approaches are discussed, that represent the breadth of new energy efficiency technology- solar cooling systems, comfort based HVAC control, and energy behavior intervention, that each shows great promise. Ultimately, realizing energy savings is a continual process, with no end in sight – no matter how efficient a building is, with these technologies, further savings can be made.