Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide realtime feedback to consumers to encourage more efficient use of electricity.

As the need for alternative transportation fuels increases, it is important to understand the many effects of introducing fuels based upon feedstocks other than petroleum. Water intensity in "gallons of water per mile traveled" is one method to measure these effects on the consumer level. In this paper we investigate the water intensity for light duty vehicle (LDV) travel using selected fuels based upon petroleum, natural gas, unconventional fossil fuels, hydrogen, electricity, and two biofuels (ethanol from corn and biodiesel from soy). Fuels more directly derived from fossil fuels are less water intensive than those derived either indirectly from fossil fuels (e.g., through electricity generation) or directly from biomass. The lowest water consumptive (<0.15 gal H 2 O/mile) and withdrawal (<1 gal H 2 O/mile) rates are for LDVs using conventional petroleumbased gasoline and diesel, nonirrigated biofuels, hydrogen derived from methane or electrolysis via nonthermal renewable electricity, and electricity derived from nonthermal renewable sources. LDVs running on electricity and hydrogen derived from the aggregate U.S. grid (heavily based upon fossil fuel and nuclear steam-electric power generation) withdraw 5-20 times and consume nearly 2-5 times more water than by using petroleum gasoline. The water intensities (gal H 2 O/mile) of LDVs operating on biofuels derived from crops irrigated in the United States at average rates is 28 and 36 for corn ethanol (E85) for consumption and withdrawal, respectively. For soyderived biodiesel the average consumption and withdrawal rates are 8 and 10 gal H 2 O/mile.

This work estimates the energy embedded in wasted food annually in the United States. We calculated the energy intensity of food production from agriculture, transportation, processing, food sales, storage, and preparation for 2007 as 8080 ± 760 trillion BTU. In 1995 approximately 27% of edible food was wasted. Synthesizing these food loss figures with our estimate of energy consumption for different food categories and food production steps, while normalizing for different production volumes, shows that 2030 ± 160 trillion BTU of energy were embedded in wasted food in 2007. The energy embedded in wasted food represents approximately 2% of annual energy consumption in the United States, which is substantial when compared to other energy conservation and production proposals. To improve this analysis, nationwide estimates of food waste and an updated estimate for the energy required to produce food for U.S. consumption would be valuable.

This letter consists of a first-order analysis of the primary energy embedded in water in the United States. Using a combination of top-down sectoral assessments of energy use together with a bottom-up allocation of energy-for-water on a component-wise and service-specific level, our analysis concludes that energy use in the residential, commercial, industrial and power sectors for direct water and steam services was approximately 12.3 ± 0.3 quadrillion BTUs or 12.6% of the 2010 annual primary energy consumption in the United States. Additional energy was used to generate steam for indirect process heating, space heating and electricity generation.

Understanding the nexus between energy and water -water used for energy and energy used for water -has become increasing important in a changing world. As growing populations demand more energy supplies and water resources, research aims to analyze the interconnectedness of these two resources.Our study sought to quantify the energy-water relationship in Texas, specifically the relationship between electricity generation and water resources as it pertains to policy and society. We examined the water requirements for various types of electricity generating facilities, for typical systems both nationwide and in Texas. We also addressed the energy requirements of water supply and wastewater treatment systems, comparing national averages with Texas-specific values. Analysis of available data for Texas reveals that approximately 595,000 megaliters of water annually -enough water for over three million people for a year -are consumed by cooling the state's thermoelectric power plants while generating approximately 400 terawatt-hours of electricity. At the same time, each year Texas uses an estimated 2.1 to 2.7 terawatthours of electricity for water systems and 1.8 to 2.0 terawatt-hours for wastewater systems -enough electricity for about 100,000 people for a year. In preparing our analysis, it became clear that substantially more site-specific data are necessary for a full understanding of the nature of the energy-water nexus and the sustainability of economic growth in Texas. We recommend that Texas increase efforts to collect accurate data on the withdrawal and consumption of cooling and process water at power plants, as well as data on electricity consumption for public water supply and wastewater treatment plants and distribution systems. The overarching conclusion of our work is that increased efficiency advances the sustainable use of both energy and water. Improving water efficiency will reduce power demand, and improving energy efficiency will reduce water demand. Greater efficiency in usage of either energy or water will help stretch our finite supplies of both, as well as reduce costs to water and power consumers.

Thermal electricity generation is a major consumer of freshwater for cooling, fuel extraction and air emissions controls, but the life cycle water impacts of different fossil fuel cycles are not well understood. Much of the existing literature relies on decades-old estimates for water intensity, particularly regarding water consumed for fuel extraction. This work uses contemporary data from specific resource basins and power plants in Texas to evaluate water intensity at three major stages of coal and natural gas fuel cycles: fuel extraction, power plant cooling and power plant emissions controls. In particular, the water intensity of fuel extraction is quantified for Texas lignite, conventional natural gas and 11 unconventional natural gas basins in Texas, including major second-order impacts associated with multi-stage hydraulic fracturing. Despite the rise of this water-intensive natural gas extraction method, natural gas extraction appears to consume less freshwater than coal per unit of energy extracted in Texas because of the high water intensity of Texas lignite extraction. This work uses new resource basin and power plant level water intensity data to estimate the potential effects of coal to natural gas fuel switching in Texas' power sector, a shift under consideration due to potential environmental benefits and very low natural gas prices. Replacing Texas' coal-fired power plants with natural gas combined cycle plants (NGCCs) would reduce annual freshwater consumption in the state by an estimated 53 billion gallons per year, or 60% of Texas coal power's water footprint, largely due to the higher efficiency of NGCCs.

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h i g h l i g h t sOptimal k-means clustering finds seasonal groups of residential electricity use. We find that each season has two nominal groups. One group typically uses more expensive electricity than the other. Regression analysis allows for insight as to which homes will be in which cluster. a b s t r a c tLittle is known about variations in electricity use at finely-resolved timescales, or the drivers for those variations. Using measured electricity use data from 103 homes in Austin, TX, this analysis sought to (1) determine the shape of seasonally-resolved residential demand profiles, (2) determine the optimal number of normalized representative residential electricity use profiles within each season, and (3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the kmeans clustering algorithm. Then probit regression was performed to determine if homeowner survey responses could serve as predictors for the clustering results. This analysis found that Austin homes fall into one of two seasonal groups with some homes using more expensive electricity (from a wholesale electricity market perspective) than others. Regression results indicate that variables such as if someone works from home, hours of television watched per week, and education levels have significant correlations with average profile shape, but might vary across seasons. The results herein also indicate that policies such as time-of-use or real-time electricity structures might be more likely to affect lower income households during some high electricity use parts of the year.