Context-Aware Adaptive Model for Smart Energy
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Building energy awareness and providing feedback on energy use is a vital component in transforming the behavior of individuals and communities towards a more efficient use of electric power. An enormous amount of energy consumption data is been collected over the years. In response to the increasing energy demand at multiple levels, there is a need to explore the possibility of better ways to use big data, discover meaningful patterns for autonomous energy saving opportunities, and adapt to evolving contextual data including household characteristics, global economic and climatic trends. In this thesis, we propose a context-aware adaptive model for optimizing energy usage across different geographic locations with appropriate comfort and satisfaction levels. The core of the model, called reverse adaptive fuzzy clustering, is a self-adaptive methodology to intelligently discover evolving features from big data of climatic and socio-economic conditions, structural and geographical attributes of households and electricity usage, and predict present and future energy demands. The model iteratively obtains patterns from matching trends of energy consumption and expenditures in US households over the years. We use this behavior as positive feedback that converts data to self-organized parameters in the model and makes a context-aware recommendation for energy optimization. We examine the unique self-adaptive approach using two real-world datasets in terms of its learning capability from past and present energy usage context-aware personalized recommendations to reduce present energy, and self-adaptation for future energy needs.
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Introduction -- Related work -- Context aware adaptive clustering model for smart energy -- Implementation -- Results and evaluation -- Conclusion and future work -- Appendix
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M.S.