Mining Correlation Patterns among Appliances in Smart Home Environment

[[abstract]]Since the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel algorithm, namely, Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. With several new optimization techniques, CoPMiner can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140513~20140516[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

An Intelligent System for Mining and Maintaining Correlation Patterns among Appliances in Smart Home

[[abstract]]Recently, due to the great advent of sensor technology, residents can collect the usage data of appliances in a house easily. However, with data progressively generating, it is still a challenge to visualize how these appliances are used. Thus, a mining and maintaining system is needed to incrementally discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance and do not consider the incremental maintenance of mining results. In this paper, a novel system, namely, Dynamic Correlation Mining System (DCMS) is developed to capture and maintain the correlation patterns among appliances incrementally. The experimental results indicate that proposed system is efficient in execution time and possesses scalability. Furthermore, we apply DCMS on a real-world dataset to show the practicability.[[conferencetype]]國內[[conferencedate]]20140826~20140827[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Personalized Ambience: An Integration of Learning Model and Intelligent Lighting Control

The number of households and offices adopting automation system is on the rise. Although devices and actuators can be controlled through wireless transmission, they are mostly static with preset schedules, or at different times it requires human intervention. This paper presents a smart ambience system that analyzes the user’s lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the user’s preferences and automate the room ambience dynamically. Context information is obtained from Yahoo Weather and environmental data pertaining to the room is collected via Cubesensors to study the user’s lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the users’ preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the user’s comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level

Development of Economic Water Usage Sensor and Cyber-Physical Systems Co-Simulation Platform for Home Energy Saving

In this thesis, two Cyber-Physical Systems (CPS) approaches were considered to reduce residential building energy consumption. First, a flow sensor was developed for residential gas and electric storage water heaters. The sensor utilizes unique temperature changes of tank inlet and outlet pipes upon water draw to provide occupant hot water usage. Post processing of measured pipe temperature data was able to detect water draw events. Conservation of energy was applied to heater pipes to determine relative internal water flow rate based on transient temperature measurements. Correlations between calculated flow and actual flow were significant at a 95% confidence level. Using this methodology, a CPS water heater controller can activate existing residential storage water heaters according to occupant hot water demand. The second CPS approach integrated an open-source building simulation tool, EnergyPlus, into a CPS simulation platform developed by the National Institute of Standards and Technology (NIST). The NIST platform utilizes the High Level Architecture (HLA) co-simulation protocol for logical timing control and data communication. By modifying existing EnergyPlus co-simulation capabilities, NIST’s open-source platform was able to execute an uninterrupted simulation between a residential house in EnergyPlus and an externally connected thermostat controller. The developed EnergyPlus wrapper for HLA co-simulation can allow active replacement of traditional real-time data collection for building CPS development. As such, occupant sensors and simple home CPS product can allow greater residential participation in energy saving practices, saving up to 33% on home energy consumption nationally

Intelligent Coordination and Automation for Smart Home Accessories

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user\u27s smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish

Understanding the indoor environment through mining sensory data - a case study

A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building