Engineering topics

Kodysh, Jeffrey B

Publications and source records attributed to Kodysh, Jeffrey B.

5 records

DOE OSTI2013

Automating Natural Disaster Impact Analysis: An Open Resource to Visually Estimate a Hurricane s Impact on the Electric Grid

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.

EARSS↗

DOE OSTI2012

Modeling and Analysis of Solar Radiation Potentials on Building Rooftops

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.

computational modeling↗

DOE OSTI2012

CoNNECT: Data Analytics for Energy Efficient Communities

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.

CoNNECT↗

DOE OSTI2012

Prediction of Solar Radiation on Building Rooftops: A Data-Mining Approach

Solar energy technologies offer a clean, renewable, and domestic energy source, and are essential components of a sustainable energy future. The accurate measurement of solar radiation data is essential for optimum site selection of future distributed solar power plants as well as sizing photovoltaic systems. However, solar radiation data are not readily available because measured sequences of radiation values are obtained for a few locations in a country. When the data are available, they are usually at different time periods and spatial scale. The availability of solar radiation data at hourly or daily time scale will enhance the integration of solar energy into electricity generation and promote a sustainable energy future. The ability to generate approximate solar radiation values is often the only practical way to obtain radiation data at hourly or daily time scale. As a result, several models have been developed for estimating solar radiation values based on analytical, numerical simulation, and statistical approaches. However, these models have inherent challenges. We will discuss some of those challenges in this paper. To enhance the prediction of solar radiation values, a novel approach is presented for estimating solar radiation values using support vector machine technique. The approach accounts for unique characteristics that influence solar radiation values. The preliminary results obtained offer useful insights for model enhancements.

machine learning↗

DOE OSTI2012

An Integrated Geovisual Analytics Framework for Analysis of Energy Consumption Data and Renewable Energy Potentials

We present an integrated geovisual analytics framework for utility consumers to interactively analyze and benchmark their energy consumption. The framework uses energy and property data already available with the utility companies and county governments respectively. The motivation for the developed framework is the need for citizens to go beyond the conventional utility bills in understanding the patterns in their energy consumption. There is also a need for citizens to go beyond one-time improvements that are often not monitored and measured over time. Some of the features of the framework include the ability for citizens to visualize their historical energy consumption data along with weather data in their location. The quantity of historical energy data available is significantly more than what is available from utility bills. An overlay of the weather data provides users with a visual correlation between weather patterns and their energy consumption patterns. Another feature of the framework is the ability for citizens to compare their consumption on an aggregated basis to that of their peers other citizens living in houses of similar size and age and within the same or different geographical boundaries, such as subdivision, zip code, or county. The users could also compare their consumption to others based on the size of their family and other attributes. This feature could help citizens determine if they are among the best in class . The framework can also be used by the utility companies to better understand their customers and to plan their services. To make the framework easily accessible, it is developed to be compatible with mobile consumer electronics devices.

energy apps↗