Dumidu Wijayasekara

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVACs primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyber-physical systems (smart cities) and evolve and interact with their surroundings. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting. Thus, even in cases of ideally designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.This article provides an overview of issues related to modern BEMSs with a multitude of (often conflicting) requirements. Because of massive and often incomplete data sets, control, sensing, and the evolving nature of these complex systems, computational intelligence (CI) techniques present a natural solution to optimal energy efficiency, energy security, and occupant comfort in buildings. The article further presents an overall architecture where CI can be used in BEMSs and concludes with a case study of the practical applications of using CI techniques in the BEMS domain.

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzyneural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system stateawareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratorys hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FNDFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

Computational intelligence based anomaly detection for Building Energy Management Systems

In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the state-awareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.

CAVE-SOM: Immersive visual data mining using 3D Self-Organizing Maps

Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.

Mining Bug Databases for Unidentified Software Vulnerabilities

Identifying software vulnerabilities is becoming more important as critical and sensitive systems increasingly rely on complex software systems. It has been suggested in previous work that some bugs are only identified as vulnerabilities long after the bug has been made public. These vulnerabilities are known as hidden impact vulnerabilities. This paper discusses existing bug data mining classifiers and present an analysis of vulnerability databases showing the necessity to mine common publicly available bug databases for hidden impact vulnerabilities. We present a vulnerability analysis from January 2006 to April 2011 for two well known software packages: Linux kernel and MySQL. We show that 32% (Linux) and 62% (MySQL) of vulnerabilities discovered in this time period were hidden impact vulnerabilities. We also show that the percentage of hidden impact vulnerabilities has increased from 25% to 36% in Linux and from 59% to 65% in MySQL in the last two years. We then propose a hidden impact vulnerability identification methodology based on text mining classifier for bug databases. Finally, we discuss potential challenges faced by a development team when using such a classifier.

Vulnerability identification and classification via text mining bug databases

As critical and sensitive systems increasingly rely on complex software systems, identifying software vulnerabilities is becoming increasingly important. It has been suggested in previous work that some bugs are only identified as vulnerabilities long after the bug has been made public. These bugs are known as Hidden Impact Bugs (HIBs). This paper presents a hidden impact bug identification methodology by means of text mining bug databases. The presented methodology utilizes the textual description of the bug report for extracting textual information. The text mining process extracts syntactical information of the bug reports and compresses the information for easier manipulation. The compressed information is then utilized to generate a feature vector that is presented to a classifier. The proposed methodology was tested on Linux vulnerabilities that were discovered in the time period from 2006 to 2011. Three different classifiers were tested and 28% to 88% of the hidden impact bugs were identified correctly by using the textual information from the bug descriptions alone. Further analysis of the Bayesian detection rate showed the applicability of the presented method according to the requirements of a development team.

Next Generation Emergency Communication Systems via Software Defined Networks

The existing Emergency Communication System (ECS) infrastructure is becoming increasingly outdated with many members of the pubic moving away from landline based telecommunications and broadcast television in favor of cellular telephones and internet-based streaming entertainment services. Current systems for public services such as E911 and Emergency Alert System broadcasts are no longer a reliable means for reaching the public. In addition, both wired and wireless telecommunications systems can become overwhelmed, as was the case following Hurricane Katrina in 2005 and the World Trade Center disaster in 2001, and in fact, when communications are needed most urgently, the difficulty of maintaining effective communication increases exponentially. While the use of Internet based alternatives could resolve some of these problems, existing Internet infrastructure offers no dedicated or priority bandwidth to the user for emergency communications (e.g. E911 or Emergency Alert System). The current Internet capacity can also be overloaded due to high volume network data streams. Under these conditions, emergency communications (e.g. inbound and outbound communications reporting catastrophic or emergency events) may have their packets dropped resulting in incomplete and/or delayed communications. To alleviate these problems, this paper presents a novel framework for ECS using network virtualization via Software Defined Networks (SDN). A table top demonstration of ECS using SDN was developed at the University of Idaho, Idaho Falls. This paper details the foundational technologies and overviews the steps taken at the University of Idaho to develop ECS suing SDN.

Human machine interaction via brain activity monitoring

Brain Computer Interfaces (BC!) are becoming increasingly studied as methods for users to interact with computers because recent technological developments have lead to low priced, high precision BCI devices that are aimed at the mass market. This paper investigates the ability for using such a device in real world applications as well as limitations of such applications. The device tested in this paper is called the Emotiv EPOC headset, which is an electroencephalograph (EEG) measuring device and enables the measuring of brain activity using 14 strategically placed sensors. This paper presents: 1) a BCI framework driven completely by thought patterns, aimed at real world applications 2) a quantitative analysis of the performance of the implemented system. The Emotiv EPOC headset based BCI framework presented in this paper was tested on a problem of controlling a simple differential wheeled robot by identifying four thought patterns in the user: “neutral”, “move forward”, “turn left”, and “turn right”. The developed approach was tested on 6 individuals and the results show that while BCI control of a mobile robot is possible, precise movement required to guide a robot along a set path is difficult with the current setup. Furthermore, intense concentration is required from users to control the robot accurately.

Neural Network based downscaling of Building Energy Management System data

Building Energy Management Systems (BEMSs) are responsible for maintaining indoor environment by controlling Heating Ventilation and Air Conditioning (HVAC) and lighting systems in buildings. Buildings worldwide account for a significant portion of world energy consumption. Thus, increasing building energy efficiency through BEMSs can result in substantial financial savings. In addition, BEMSs can significantly impact the productivity of occupants by maintaining a comfortable environment. To increase efficiency and maintain comfort, modern BEMSs rely on a large array of sensors inside the building that provide detailed data about the building state. However, due to various reasons, buildings frequently lack sufficient number of sensors, resulting in a suboptimal state awareness. In such cases, a cost effective method for increasing state awareness is needed. Therefore, this paper presents a novel method for increasing state awareness through increasing spatial resolution of data by means of data downscaling. The presented method estimates the state of occupant zones using state data gathered at floor level using Artificial Neural Networks (ANN). The presented method was tested on a real-world CO2 dataset, and compared to a time based estimation of CO2 concentration. The downscaling method was shown to be capable of consistently producing accurate estimates while being more accurate than time based estimations.