4 research outputs found
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Geovisualization of household energy consumption characteristics
A vast amount of quantitative data is available within the energy sector, however, there is limited understanding of the relationships between neighbourhoods, demographic characteristics and domestic energy consumption habits. We report upon research that will combine datasets relating to energy consumption, saving and loss with geodemographics to enable better understanding of energy user types. A novel interactive interface is planned to evaluate the performance of these energy-based classifications. The research aims to help local governments and the energy industry in targeting households and populations for new energy saving schemes and in improving efforts to promote sustainable energy consumption. Energy based neighbourhood classifications will also promote consumption awareness amongst domestic users. This poster describes the research methodology, data sources and visualization requirements
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Visualisation for household energy analysis: techniques for exploring multiple variables across scale and geography
The visualisation of large volumes of data can provide rich and meaningful representations that enable users to gain insights quickly and efficiently. Household energy consumer characteristics are explored in this thesis using innovative interactive visualisation techniques. Initial research with energy analysts, from a major UK utility company, investigates visual possibilities and opportunities for future (smart home) energy analytics and explicitly uses creativity techniques for information visualisation requirements gathering. The results, along with exploratory visual analysis combining geodemographic groups and energy consumption, identifes a need for profiling consumers by typical traits. While energy consumption has been a popular topic of research in recent years, there is still limited understanding of the relationship between energy consumption and measurable characteristics of the general population. An investigation of the process of creating an energy-based geodemographic classification led to the proposal and design of a new theoretical framework for visually comparing multivariate data across scale and geography; a necessary step when selecting reliable variables for running clustering algorithms, such as during the geodemographic classification creation process.
The framework for including geography and scale in multivariate comparison forms the major contribution of this thesis. This framework is demonstrated and justified through the building of an interactive visualisation prototype, using input variables deemed relevant for consideration for energy-based geodemographic classification. Important transitions in the framework are highlighted in the proposed design, which uses both statistical and spatial representations. The utility of the framework is validated in the context of energy-based geodemographic variable selection where the multivariate geography of the UK is explored. The sensitivities of varying scale and geography { through varying resolution, extent and the calculation of locally weighted summary statistics { are investigated in context and are shown to be important elements to consider during the variable selection process. The broader applicability of the framework is demonstrated through two further scenarios where multivariate visualisation across scale and geography is shown to be important. The research provides a framework and viable solutions through which geographical visual parameter space analysis (gvPSA) can be undertaken. It uses a design science approach that results in a series of artifacts that open up new visualisation possibilities. This project covers a wide topic where the breadth of research options is extensive and many possibilities for continued research are identified
AN INVESTIGATION INTO CONTEXT-AWARE AUTOMATED SERVICE IN SMART HOME FACILITIES: SEARCH ENGINE AND MACHINE LEARNING WITH SMARTPHONE
Technological advances, in general, coupled with the widespread use of smartphones, create ever more opportunities for mobile applications. This thesis considers the use of such devices within embedded systems to provide automated services in smart home automation. The overall approach links together context-aware data from the physical environment, sensors and actuators for domestic appliances and statistics-based decision-making. A prototype system named ‘Wireless Sensor/Actuator Mobile Computing in the Smart Home’ (WiSAMCinSH) is developed, which in turns aims to provide services that can benefit clients who are currently dependent on others in their daily activities.
This research highlights and covers the following concepts. Firstly, it addresses the need to improve the prototypical decision-making model by enabling it to take into account context-aware information as conditions under which particular action decisions are appropriate. Secondly, an essential aspect of context-aware performance architecture is that its features must be of high accuracy, explicitly readable and fast. Thirdly, it is necessary to determine which probability-based rules are most effective in generating the dynamic environment to control the home facilities. Finally, it is important to analyse and classify in depth the accuracy of context acquisition and the corresponding context control using cross-validation methods.
A case study uses integrated mobile detection technology to improve the efficiency of mobile applications, taking into account the resource limitations forced on the use of mobile devices. It also utilises other embedded sensing technologies to predict expectations, thereby enabling automatic control of facilities in the home. The main approach is to combine search engines and machine learning to create a system architecture for a context-aware computing service. Among the major challenges are finding the best statistics-based rules for decision-making and overcoming the heterogeneous character of the many devices which are used together. The results achieved show very promising potential for the use of mobile applications within a context-aware computing service, albeit one which still presents problems to be resolved through future research