Abstract: The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.
Abstract: In order to reduce the number of deaths due to heart
problems, we propose the use of Hierarchical Temporal Memory
Algorithm (HTM) which is a real time anomaly detection algorithm.
HTM is a cortical learning algorithm based on neocortex used for
anomaly detection. In other words, it is based on a conceptual theory
of how the human brain can work. It is powerful in predicting unusual
patterns, anomaly detection and classification. In this paper, HTM
have been implemented and tested on ECG datasets in order to detect
cardiac anomalies. Experiments showed good performance in terms
of specificity, sensitivity and execution time.
Abstract: To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.
Abstract: A prototype of an anomaly detection system was
developed to automate process of recognizing an anomaly of
roentgen image by utilizing fuzzy histogram hyperbolization image
enhancement and back propagation artificial neural network.
The system consists of image acquisition, pre-processor, feature
extractor, response selector and output. Fuzzy Histogram
Hyperbolization is chosen to improve the quality of the roentgen
image. The fuzzy histogram hyperbolization steps consist of
fuzzyfication, modification of values of membership functions and
defuzzyfication. Image features are extracted after the the quality of
the image is improved. The extracted image features are input to the
artificial neural network for detecting anomaly. The number of nodes
in the proposed ANN layers was made small.
Experimental results indicate that the fuzzy histogram
hyperbolization method can be used to improve the quality of the
image. The system is capable to detect the anomaly in the roentgen
image.