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The need for an improved control strategy for the operation of a wind-powered refrigeration system for the storage of apples was investigated. The results are applicable to other systems which employ intermittently available power sources, battery and thermal storage, and an auxiliary, direct current power supply. Tests were conducted on the wind-powered refrigeration system at the Virginia Polytechnic Institute and State University Horticulture Research Farm in Blacksburg, Virginia. Tests were conducted on the individual components of the system. In situ windmill performance were also conducted. The results of these tests have been presented. An improved control strategy was developed to improve the utilization of available wind energy and to reduce the need for electrical energy from an external source while maintaining an adequate apple storage environment. Ph. D.

In the pursuit of creating efficient CdTe p-n homojunctions, we developed iodine (I) n-type doped CdTe using Cadmium Iodide (CdI2) as a dopant in varying concentrations (1018 cm-3, 1019 cm-3 and 1020 atoms·cm-3 target concentrations) in CdTe. Iodide doped crystals were grown using a Modified Vertical Bridgman furnace (MVB). Single crystals were characterized using XRD (X-Ray Diffraction), Hall effect, IR (Infrared) Microscopy, UV-VIS-NIR (Ultraviolet-Visible-Near infrared Spectroscopy) and FTIR (Fourier Transform Infrared Spectroscopy). Partners at the National Renewable Energy Laboratory (NREL) also provided data for Hall Effect and Two Photon Excitation Time Resolved Photoluminescence (2PE TRPL) of wafers and films. Photoluminescence mapping (PL mapping) was obtained from Klar scientific, and glow discharge mass spectrometry (GDMS) for purity and final doping concentration was obtained from the National Research Council Canada. Due to poor carrier properties in the crystals as-grown, two annealing treatments were explored, in either tellurium or cadmium vapor. Homojunctions were made at NREL by depositing n-type films from these crystals on p-type single crystals, CdTe:P grown and WSU. The n-type films were created using the close-space sublimation epitaxy (CSSE) process. Herein is reported the results of the grown CdTe:I crystals and, to a lesser extent, the properties of the CdTe:I thin films formed by CSSE.

This Article examines the genesis and context of SE4All, placing the effort within both itshistorical and international policy contexts. It highlights the voluntary nature of the initiative andargues that its effective implementation and the achievement of its goals require the articulation ofan applicable international legal framework that aids the transformation of SE4All’s policy actionsinto binding international legal commitments. The article contends that such a transformation doesnot depend on the creation of entirely new legal rules or institutions. Instead, an effective frame-work for successful implementation of SE4All can be derived from existing rules of internationalhuman rights law and sustainable development law. Reliance on these twin bodies of interna-tional law will increase the prospects for SE4All to achieve energy access and related goals thatits predecessor initiatives have failed to accomplish

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

I derive and test predictions from the classic Mussa and Rosen (1978) second-degree price discrimination model using data from the United States clothes washer market. I find evidence consistent with price discrimination in the market response to energy efficiency policy changes. Concurrent with the effective dates of both the new 2004 and 2007 federal minimum efficiency and ENERGY STAR standards, within-model clothes washer prices dropped on average. The heterogeneous pattern of price reduction across market segments, and adjustments in the menu of products, were consistent with predictions from the price-discrimination model, and not with a perfectly competitive market.

This presentation gives an overview of the Sustainable Agriculture and Natural Resource Management Collaborative Research Support Program (SANREM CRSP). The SANREM CRSP utilizes a systems approach to promote many goals including the improvement of agricultural productivity, the empowerment of smallholders, and the promotion of sustainable development. This presentation shows the different components, partners, and structure of the SANREM CRSP, the extent of capacity building efforts, and the long-term research activities for Phase IV. ME (Management Entity)

Climate change is one of the preeminent policy issues of our day, and the social cost of carbon (SCC) is one of the foremost tools for determining the socially optimal policy response. The SCC is estimated using Integrated Assessment Models (IAMs), of which Nordhaus’ DICE is the oldest and one of the best respected. These numerical models capture the various steps in the climate and economic processes that translate a marginal unit of CO2 emissions into economic damage. While accuracy at each of these steps is necessary to precisely estimate the SCC, correct calibrating the climate damage function, which translates a temperature change into a percentage change in GDP, is critical. Calibration of the damage function determines which climate damages are included and excluded from the cost of carbon. Traditionally, Nordhaus calibrated the DICE damage function using a global damage estimate calculated by aggregating a series of region-sector specific damage estimates (Nordhaus and Boyer, 2000; Nordhaus, 2008). However, in DICE-2013, Nordhaus moved to calibrating the DICE damage function using a meta-analysis at the global scale (Nordhaus and Sztorc, 2013). This paper critiques this meta-analysis approach as it is currently applied and re-estimates the DICE-2013 damage function using up-to-date meta-analysis techniques to more accurately reflect climate damages and the uncertainty underlying them. This paper finds that DICE-2013 damage function significantly under-estimates climate damages by a factor of two to three. This is a working paper.

The goal of this study is to determine the difference in CO2 emissions between 2019-2020 and 2020-2021, more specifically during lockdown periods during the COVID-19 pandemic. In the beginning of the pandemic, most countries were forced into lockdowns, and a countless number of people had to continue their daily work from home in isolation. Previously, people would go to an office or to school and leave their houses empty for eight hours, without having lights or any electronics on. Because of this, there should be a direct correlation between electricity usage before and during lockdowns, as a private residence should have higher electricity consumption during 2020-2021, when they are at home. Using machine learning, we will investigate to see if COVID-19 affected CO2 emissions as a result of more electricity usage in private residences. A model will be made to predict what the CO2 emissions would be for 2019-2020, based on electricity usage data from 2020-2021. Then, the real CO2 emissions from 2019-2020 will be compared with the model’s predicted values, and the difference will indicate if COVID-19 caused an inconsistency between actual and predicted CO2 emissions. Factors that were taken into account when making a model were independent variables relating to outdoor conditions, the number of people living in the house, and the temperature that the thermostat is set at, making the response variable CO2 emissions.

Photovoltaic (PV) technologies constitute a leading renewable energy source with a worldwide installed capacity of 135 GW in 2013 that may increase to nearly 4700 GW in 2050. To achieve this production level while minimizing environmental impacts, decision makers must rely at national level on relevant technological, economic and planning aspects which are highly geographically dependent. The access to performance data is a critical issue in the decision-making process and determines the successful development of efficient PV systems. For this reason, a new interactive tool is proposed here to provide the users with easy-to-use data and maps for the solar irradiation and screening level environmental results of representative PV technologies. The calculation procedures account for the geographic location and the PV system layout (installation, orientation and inclination angles). The tool has a worldwide coverage with a multi-criteria scope, both in terms of the numerous technological scenarios and of the wide range of environmental indicators. Moreover, the user is given the possibility to compare the PV environmental performance to the corresponding country electricity mix environmental footprint. 32nd European Photovoltaic Solar Energy Conference and Exhibition; 2869-2873