Khoi Anh Nguyen

An intelligent pattern recognition model to automate the categorisation of residential water end-use events

The rapid dissemination of residential water end-use (e.g. shower, clothes washer, etc.) consumption data to the customer via a web-enabled portal interface is becoming feasible through the advent of high resolution smart metering technologies. However, in order to achieve this paradigm shift in residential customer water use feedback, an automated approach for disaggregating complex water flow trace signatures into a registry of end-use event categories needs to be developed. This outcome is achieved by applying a hybrid combination of gradient vector filtering, Hidden Markov Model (HMM) and Dynamic Time Warping Algorithm (DTW) techniques on an existing residential water end-use database of 252 households located in South-east Queensland, Australia having high resolution water meters (0.0139?L/pulse), remote data transfer loggers (5?s logging) and completed household water appliance audits. The approach enables both single independent events (e.g. shower event) and combined events (i.e. several overlapping single events) to be disaggregated from flow data into a comprehensive end-use event registry. Complex blind source separation of concurrently occurring water end use events (e.g. shower and toilet flush occurring in same time period) is the primary focus of this present study. Validation of the developed model is achieved through an examination of 50 independent combined events.

An autonomous and intelligent expert system for residential water end-use classification

Intelligent metering technology combined with advanced numerical techniques enable a paradigm shift in the current level of water consumption information provision that is available to the customer and the water business. The aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water meters includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed intelligent algorithms to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events using a hybrid combination of techniques including Hidden Markov Model (HMM), Dynamic Time Warping (DTW) algorithm, time-of-day probability functions, threshold values and various physical features. However, the issue still remained, which is the focus of this current paper, on how to integrate self-learning functionality into the visioned expert system, in order that it can learn from newly collected datasets from different cities, regions and countries, to that collected for the training data. Such versatility and adaptive capacity is essential to make the expert system widely applicable. Through applying alternate forms of HMM and DTW in association with a frequency analysis technique, a suitable self-learning methodology was formulated and tested on three independent households located in Melbourne, Australia with a prediction accuracy of between 80% and 90% for the major end-use categories. The three principle flow data processing modules (i.e., single and combined event recognition and self-learning function) were integrated into a prototype software application for performing autonomous water end-use analysis and its functionality is presented in the latter sections of this paper. The developed expert system has profound implications for government, water businesses and consumers, seeking to better manage precious urban water resources.

Intelligent autonomous system for residential water end use classification

Smart metering technology enables the capture of high resolution water consumption data.Intelligent algorithms autonomously categorise single and combined water end use events.Hybrid combination of HMM, ANN and DTW for pattern recognition problem.Expert system developed to autonomously disaggregate water use into end use categories. Over half of the worlds population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water metres connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82,000 events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN-HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid models recognition accuracy ranged from 85.9% to 96.1% for single events and 81.8-91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.

Re-engineering traditional urban water management practices with smart metering and informatics

Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.

Smart Technologies in Reducing Carbon Emission: Artificial Intelligence and Smart Water Meter

Global warming caused by greenhouse gases (GHG) is regarded as one of the biggest threats facing our world. Climate scientists predict that a 1.5°C rise in global temperature may cause the extinction of 25% of the Earths animals and plants disappear. In this fearsome prospect, carbon emission was identified as the main factor contributing to this issue, and needed to be effectively controlled to mitigate their detrimental impacts on the environment as well as human life. GHG mitigation requires developing and implementing policies, and utilizing new technologies to reduce GHG. In this paper, we explore the role of smart technologies in reducing the carbon emission. With the increasing deployment of Smart water meters across Australia in the last five years, an intelligent and knowledge base system called Autoflow© has been developed to help: (i) monitor and predict carbon emission level from water consumption in realtime (e.g. Property A: Carbon emission from 6am-6pm tomorrow is 12.4kg), and (ii) suggest options for reducing water consumption and carbon emission. This Autoflow© system operates based on smart algorithms including Dynamic Time Warping, Hidden Markov Model, Dynamic Harmonic Regression and Artificial Neural Network, and has potential to go beyond Australian border in a very near future to help effectively sustain the limited water resource and environment around the word.

Senile coconut palms: Functional design and biomechanics of stem green tissue

Abstract This study aims at determining the biomechanical behaviour and functional design, at integral level of hierarchical structure, of senile coconut palms (greater than 80 years old). To achieve the objectives, 46 stress/strength analyses were performed on characteristic coconut palm stem green tissues (referred to as cocowood herein) by means of three dimensional finite element analysis (FEA). To estimate the material damage produced when the stresses rose beyond the material strength, the Tsai-Hill failure criterion was used; the progressive material failure was predicted and mapped for seven wind speeds of different categories. Parametric analyses were performed to further analyse the influence of fibrovascular bundle orientations and density distribution on the cocowood stem functional design. The research outcomes showed an optimum orientation of characteristic fibrovascular bundles and an improved cocowood structure in terms of mechanical efficiency and capacity to resist high wind loadings. The analyses allowed for a better understanding of the cocowood biomechanics and functional design, especially the significance of its structural-mechanical advantage over other palm species and trees. As no similar research on the cocowood biomechanics using FEA had been conducted before, the knowledge advanced from the current study has far-reaching implications for enhancing wood materials from a biomimetic perspective.