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and numerous other organizations with whom the three-laboratory team has interacted during this project. The project team would also like to acknowledge the Clean Energy States Alliance (CESA) for very close collaboration in implementing its energy policy and regulations database into the Energy Zones Mapping Tool, as well as Navigant Consulting for its contributions related to demand-side resources.

An ORNL team working on the Energy Awareness and Resiliency Standardized Services (EARSS) project developed a fully automated procedure to take wind speed and location estimates provided by hurricane forecasters and provide a geospatial estimate on the impact to the electric grid in terms of outage areas and projected duration of outages. Hurricane Sandy was one of the worst US storms ever, with reported injuries and deaths, millions of people without power for several days, and billions of dollars in economic impact. Hurricane advisories were released for Sandy from October 22 through 31, 2012. The fact that the geoprocessing was automated was significant-there were 64 advisories for Sandy. Manual analysis typically takes about one hour for each advisory. During a storm event, advisories are released every two to three hours around the clock, and an analyst capable of performing the manual analysis has other tasks they would like to focus on. Initial predictions of a big impact and landfall usually occur three days in advance, so time is of the essence to prepare for utility repair. Automated processing developed at ORNL allowed this analysis to be completed and made publicly available within minutes of each new advisory being released.

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The active application of photovoltaic for electricity generation could effectively transform neighborhoods and commercial districts into small, localized power plants. This application, however, relies heavily on an accurate estimation of the amount of solar radiation that is available on individual building rooftops. While many solar energy maps exist at higher spatial resolution for concentrated solar energy applications, the data from these maps are not suitable for roof-mounted photovoltaic for several reasons, including lack of data at the appropriate spatial resolution and lack of integration of building-specific characteristics into the models used to generate the maps. To address this problem, we have developed a modeling framework for estimating solar radiation potentials on individual building rooftops that is suitable for utility-scale applications as well as building-specific applications. The framework uses light detection and ranging (LIDAR) data at approximately 1-meter horizontal resolution and 0.3-meter vertical resolution as input for modeling a large number of buildings quickly. One of the strengths of this framework is the ability to parallelize its implementation. Furthermore, the framework accounts for building specific characteristics, such as roof slope, roof aspect, and shadowing effects, that are critical to roof-mounted photovoltaic systems. The resulting data has helped us to identify the so-called “solar panel sweet spots” on individual building rooftops and obtain accurate statistics of the variation in solar radiation as a function of time of year and geographical location.

Energy efficiency is the lowest cost option being promoted for achieving a sustainable energy policy. Thus, there have been some innovations to reduce residential and commercial energy usage. There have also been calls to the utility companies to give customers access to timely, useful, and actionable information about their energy use, in order to unleash additional innovations in homes and businesses. Hence, some web-based tools have been developed for the public to access and compare energy usage data. In order to advance on these efforts, we propose a data analytics framework called Citizen Engagement for Energy Efficient Communities (CoNNECT). On the one hand, CoNNECT will help households to understand (i) the patterns in their energy consumption over time and how those patterns correlate with weather data, (ii) how their monthly consumption compares to other households living in houses of similar size and age within the same geographic areas, and (iii) what other customers are doing to reduce their energy consumption. We hope that the availability of such data and analysis to the public will facilitate energy efficiency efforts in residential buildings. These capabilities formed the public portal of the CoNNECT framework. On the other hand, CoNNECT will help the utility companies to better understand their customers by making available to the utilities additional datasets that they naturally do not have access to, which could help them develop focused services for their customers. These additional capabilities are parts of the utility portal of the CoNNECT framework. In this paper, we describe the CoNNECT framework, the sources of the data used in its development, the functionalities of both the public and utility portals, and the application of empirical mode decomposition for decomposing usage signals into mode functions with the hope that such mode functions could help in clustering customers into unique groups and in developing guidelines for energy conservation.