In recent years, a significant amount of research has focused on problems related to electricity distribution and consumption in the nation. Although the efficiency and robustness of the electricity distribution network can be improved by deploying a smart grid infrastructure, the end users and their consumption behavior continue to play an important role in the overall performance of such a grid, in particular their impact on the peak usage. At the same time, due to rising retail energy prices and growing concerns about the environment, end users have become more interested into technological solutions that can help them reduce electricity consumption. In this proposal, the team introduces a Web-based application that intelligently helps customers lower their electricity consumption. The application leverages existing data sources, such as sensors, smart meters, and smartphones, to collect data not only about electricity consumption, but also the context in which this occurred (e.g., user location, activity), and then transform them into actionable information for the user by means of a MapReduce-based data fusion and visualization on interactive Web-based charts and maps. Unlike existing applications that present a one-dimensional view of the smart meter data, the proposed approach offers personalized actionable information in the form of simple targeted actions that users can take to reduce their electricity consumption. This project builds on concepts and solutions for sensing, networking, and processing of data in wireless sensor networks and smart environments. More specifically, the proposed technology can successfully integrate large dynamic heterogeneous data streams originating from live third-party sources using a MapReduce-like paradigm, and then present the relevant trends and patterns to the end user on interactive charts and maps on the Web.
The potential societal and commercial impact of this project is significant. Based on the initial exploratory interviews with potential customers, there exists a significant demand to use an application like the one proposed. These potential users are attracted by detailed personalized suggestions as to what actions to take in order to reduce electricity consumption, thus essentially lowering the load on the generation and distribution grid as a by-product. The team also expects the solution methodologies developed for this I-Corps project to be applicable in other domains, such as smart healthcare, and private or public transportation. For instance, existing data sources could be leveraged to offer an accurate overview of one?s health and well-being, leading to more accurate diagnosis and pre-emptive actions to improve health conditions.
