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As the leader of the largest economy, President of the United States has substantive influence on addressing climate change problems. However, a presidential election is often dominated by issues other than energy problems. This paper focuses on the 2016 presidential election, and examines the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters were more concerned about terrorism and economic issues than environmental issues. We then compare the Democratic and Republican candidate's view of America's energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggests that Democratic candidates have moderate to ambitious goals for developing solar and other renewables. The Republican candidates favor fossil fuels and they choose not to provide any specific target for developing renewable energy. In addition, this trend of party polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy policies need to be discussed more often regarding the diversification and decarbonization of the nation's energy system. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error. [ABSTRACT FROM AUTHOR]

In this paper, the performances of various lithium-ion chemistries for use in plug-in hybrid electric vehicles have been investigated and compared to several other rechargeable energy storage systems technologies such as lead-acid, nickel-metal hydride and electrical-double layer capacitors. The analysis has shown the beneficial properties of lithium-ion in the terms of energy density, power density and rate capabilities. Particularly, the nickel manganese cobalt oxide cathode stands out with the high energy density up to 160 Wh/kg, compared to 70-110, 90 and 71 Wh/kg for lithium iron phosphate cathode, lithium nickel cobalt aluminum cathode and, lithium titanate oxide anode battery cells, respectively. These values are considerably higher than the lead-acid (23-28 Wh/kg) and nickel-metal hydride (44-53 Wh/kg) battery technologies. The dynamic discharge performance test shows that the energy efficiency of the lithium-ion batteries is significantly higher than the lead-acid and nickel-metal hydride technologies. The efficiency varies between 86% and 98%, with the best values obtained by pouch battery cells, ahead of cylindrical and prismatic battery design concepts. Also the power capacity of lithium-ion technology is superior compared to other technologies. The power density is in the range of 300-2400 W/kg against 200-400 and 90-120 W/kg for lead-acid and nickel-metal hydride, respectively. However, considering the influence of energy efficiency, the power density is in the range of 100-1150 W/kg. Lithium-ion batteries optimized for high energy are at the lower end of this range and are challenged to meet the United States Advanced Battery Consortium, SuperLIB and Massachusetts Institute of Technology goals. Their association with electric-double layer capacitors, which have low energy density (4-6 Wh/kg) but outstanding power capabilities, could be very interesting. The study of the rate capability of the lithium-ion batteries has allowed for a new state of charge estimation, encompassing all essential performance parameters. The rate capabilities tests are reflected by Peukert constants, which are significantly lower for lithium-ion batteries than for nickel-metal hydride and lead-acid. Furthermore, rate capabilities during charging have been investigated. Lithium-ion batteries are able to store about 80% of the capacity at current rate 2It, with high power cells accepting over 90%. At higher charging rates of 5It or more, the internal resistance impedes charge acceptance by high energy cells. The lithium titanate anode, due to its high surface area (100 m2/g compared to 3 m2/g for the graphite based anode) performs much better in this respect. The behavior of lithium-ion batteries has been investigated at different conditions. The analysis has leaded us to a new lithium ion battery model. This model will be compared to existing battery models in future research contributions. [ABSTRACT FROM AUTHOR]

Passenger ferries serve a variety of transport needs in the U.S., such as providing vital links across bodies of water, and supplementing highway bridges. In some cases in which there is a ferry connection but no bridge, a bridge would be impractical; in other cases, a bridge might be feasible. The paper compares the energy consumption of ferries and motor vehicles on bridges, to determine which link is more fuel efficient. One finding is that limited data are available on ferry boat fuel consumption: despite there being 208 ferry boat operators in the U.S. as of 2008, only eight were providing energy use data to the National Transit Database. Examinations of three of the systems found that the passenger-MPG of the ferries ranged from 2.61 to 14.00 (1.11 to 5.95 km/L), while that of the motor vehicles on adjacent highway bridge connections ranged from 25.34 to 32.45 (10.77 to 13.79 km/L). Data from the eight systems are used to develop a ferry MPG model. The model is used to show that the Ryer Island and Charles Hall Ferries are less fuel efficient than hypothetical bridges in those locations. The fuel efficiencies and consumptions of the ferries would equal those of motor vehicles on the bridges, however, if smaller vessels were used, and if the frequency of service was reduced. [ABSTRACT FROM AUTHOR]

For many sensor network applications such as military or homeland security, it is essential for users (sinks) to access the sensor network while they are moving. Sink mobility brings new challenges to secure routing in large-scale sensor networks. Previous studies on sink mobility have mainly focused on efficiency and effectiveness of data dissemination without security consideration. Also, studies and experiences have shown that considering security during design time is the best way to provide security for sensor network routing. This paper presents an energy-efficient secure routing and key management for mobile sinks in sensor networks, called SCODEplus. It is a significant extension of our previous study in five aspects: (1) Key management scheme and routing protocol are considered during design time to increase security and efficiency; (2) The network topology is organized in a hexagonal plane which supports more efficiency than previous square-grid topology; (3) The key management scheme can eliminate the impacts of node compromise attacks on links between non-compromised nodes; (4) Sensor node deployment is based on Gaussian distribution which is more realistic than uniform distribution; (5) No GPS or like is required to provide sensor node location information. Our security analysis demonstrates that the proposed scheme can defend against common attacks in sensor networks including node compromise attacks, replay attacks, selective forwarding attacks, sinkhole and wormhole, Sybil attacks, HELLO flood attacks. Both mathematical and simulation-based performance evaluation show that the SCODEplus significantly reduces the communication overhead, energy consumption, packet delivery latency while it always delivers more than 97 percent of packets successfully. [ABSTRACT FROM AUTHOR]

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption). [ABSTRACT FROM AUTHOR]

This paper aims to analyse: (a) how the attitude towards renewable energy-based heating systems, pro-environmental behaviour and the perceived attributes of technology influence intention to convert residential heating systems from fossil fuels to biofuels, and (b) the moderating role of culture based on Hofstede's individualism dimension. A total of 425 responses were collected from a panel of internet users from representative countries in three continents (the United States, the United Kingdom and South Africa); the data analysis was carried out using structural equation models in a multigroup analysis. The results showed that attitude towards renewable energy-based heating systems is influenced by environmental variables in the United States and the United Kingdom, and by the perceived attributes of clean residential heating systems in the United States and South Africa. Attitude, in turn, impacts on the intention to convert from fossil fuels to biofuels. In addition, individualism has a moderating effect between these variables and there are intercultural differences in the degree of importance attributed to them. The study concludes the use of these energy systems as drivers of environmentally-sustainable development. [ABSTRACT FROM AUTHOR]

The operations of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations. [ABSTRACT FROM AUTHOR]