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{"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.

In the Meyer-Burger labs, pilot production of its proprietary 6''-Heterojunction (HJT) cells has been conducted on full-scale production tools (Roth & Rau Helia PECVD & Helia PVD). Overall, close to 10.000 cells have been manufactured with efficiencies up to 21,1%. From theses HJT cells, 60-cell modules have been produced. Temperature coefficient measurements of both cells and modules have been conducted by independent institutes, resulting in outstanding -0,20%/K and -0,22%/K, respectively. The modules have been deployed on an outdoor test field in Mid-European climate conditions for over 1 year. The results demonstrate the durability of the HJT modules and comparison with standard crystalline modules show an increase of energy yield of up to 7% on sunny days already in spring time. Under hot climate conditions in southern regions this increase is supposed to be even higher, making HJT modules extremely suitable for southern climates and low LCOE´s. 28th European Photovoltaic Solar Energy Conference and Exhibition; 1887-1889

Utility regulators frequently attempt to use tariff structures to pursue both distributional and efficiency goals. Efficiency necessitates setting prices as close to marginal costs as possible while still allowing the firm to cover its costs. The common distributional goal is to protect low-income customers from high prices. Perhaps nowhere is the conflict between these goals greater than in the use of increasing-block residential utility pricing, in which the marginal price to the customer increases as the customer’s usage rises. Since the 2000-01 California electricity crisis, the state has adopted some of the most steeply increasing-block tariffs in electric utility history, but the distributional and efficiency effects have not been analyzed in detail. Using a novel approach for matching customer bill data with census data on area income distributions, I derive estimates of the income redistribution effected by the increasing-block tariffs used by California regulated electric utilities. I find that the rate structure does redistribute income to lower-income groups, but that the effect is fairly modest, particularly compared to a means-tested program also in use. While the distributional impact of these tariffs do not seem to be large, the efficiency costs may not be great either. Examining the distribution of customer demand quantities, I find preliminary evidence that customers do not respond to the increasing marginal prices they face.

Building materials have played a more and more important role in saving building energy since the central government of China set higher standards and requirements for new-constructed and retrofit buildings after 2005. Glazing units, especially energy-saving units including LOW-E coated glazing units and PVB laminated glass, are utilized nation-wide. This paper employs energy simulation to analyze the energy-saving effects of different glazing units in residential buildings in the city of Guangzhou, as an example of hot-humid climate in China. It appeals that the PVB laminated glass can refuse 44 % solar radiation to enter rooms and reduce 40 % of the shading coefficient comparing to clear glass. Meanwhile, in the aspects of operation and design of the HVAC system, 28 % of cooling load, 21 % of installed capacity and 8.6 % of full-load operation time can be saved.

The Method of Characteristics (MOC) is a popular method to solve the multi-group neutron transport equation. While this method is most widely used in two dimensions, extension to three dimensions allows for more accurate calculation of axial leakage and reaction rates. However, the 3D form of MOC can be computationally prohibitive. One concern is the massive memory requirements imposed by storing all segments of 3D tracks. In this study, an alternative approach is presented for axially extruded geometries that only saves segments in two dimensions. This is accomplished by first creating a 2D xy-plane that incorporates all radial detail at every axial level. Then, standard 2D ray tracing is applied to this plane. Axial extruded regions are constructed during segmentation, each containing an axial mesh. During transport sweeps the 3D segments are reconstructed on-the-fly using 2D segment lengths and 1D axial meshes. This strategy implicitly transforms geometries into an axially extruded representation. The resulting algorithm consumes far less memory with minimal computational overhead for common reactor physics problems. United States. Office of the Assistant Secretary for Nuclear Energy (Nuclear Energy University Programs Fellowship) Center for Exascale Simulation of Advanced Reactors (U.S. Department of Energy Contract No. DE-AC02-06CH11357)