Engineering topics

Maness, Christopher S

Publications and source records attributed to Maness, Christopher S.

3 records

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

Interactive Visual Analysis of High Throughput Text Streams

The scale, velocity, and dynamic nature of large scale social media systems like Twitter demand a new set of visual analytics techniques that support near real-time situational awareness. Social media systems are credited with escalating social protest during recent large scale riots. Virtual communities form rapidly in these online systems, and they occasionally foster violence and unrest which is conveyed in the users language. Techniques for analyzing broad trends over these networks or reconstructing conversations within small groups have been demonstrated in recent years, but state-of- the-art tools are inadequate at supporting near real-time analysis of these high throughput streams of unstructured information. In this paper, we present an adaptive system to discover and interactively explore these virtual networks, as well as detect sentiment, highlight change, and discover spatio- temporal patterns.

sentiment analysis↗

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↗