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The recent dispositions related to the energy performance of buildings, launched by the European Directives 2002/91/EC and 2010/31/EU, turned attention to the Energy Performance Certificate (EPC). The aim of this study is to investigate the economic effects of the recent Italian statutory provisions related to energy performance of buildings on listing behaviour. With this aim an hedonic regression analysis is performed, in order to measure the impact of EPC level on listing prices. The study, based on a set of more than 500 housing property asset collected in 2012 from real estate advertisements websites, is focused on the Turin real estate market as a case study. Furthermore this paper contributes to the early limited literature on implication of the impact of EPC level on Italian housing market, representing one of the first systematic evidence about the energy certifications in the housing listing prices. Aestimum, Aestimum 65 (2014)

{"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.

Recent extreme weather events have resulted in an ongoing discussion on the issues of land use and compensation payments within Austrian agriculture. Building on a functional evaluation system for agricultural lands as developed within the Interreg IIIB project “ILUP”, the national project “Agriculture and Flooding” has as its goal to classify the flood-protection contribution and flood sensitivity of agricultural lands. This, in turn, enables the recommendation of targeted measures for potentially improving flood situations, as well as an estimate of their implementation costs. In addition to the digital soil map, other fundamental sources used for the project are the digital flood risk map, IACS land-use data and works by the Institute for Land and Water Management Research. Reference values and marginal returns sourced from the Federal Institute of Agricultural Economics also flow into the cost estimates for the recommended combination. The results will contribute to an understanding of the multifunctionality of agricultural lands and to the setting of priorities on a regional scale regarding packaged flood-prevention and damage-minimization. However, the results at hand can only serve as one step toward regional flood protection projects, whose development will require the cooperation of all interest groups.

The devolatilization of solid fuels is of crucial importance for all thermo­chemical processes and is the basic step in gasification and combustion models. Here a structural model is developed to provide an Advanced tool for Biomass and Coal Devolatilization (ABCD model), even in blend. The main features are founded on the original approach of the CPD (Chemical Percolation Devolatilization) model by Fletcher [1]. The ABCD model extends the approach also to biomass fuels. Further improvements of the ABCD model are: (i) a population balance between n­mers in liquid metaplast; (ii) elemental balance closure with the speciation of light gases, hetero­species and tar composition; (iii) introduction of secondary reactions of tar­cracking and crosslinking. The ABCD model results agree with a selection of experimental data (from homemade and literature works) on different biomasses. The results reported in this paper encouraged IFRF in continuing the experimental campaign for the validation of the model by extending the Solid Fuel DataBase SFDB. The inclusion of ABCD in comprehensive codes (e.g., Reactor Network Analysis, RNA [2]) and process models is valuable because it gives detailed distribution of pyrolysis products in a wide range of conditions with a low computational cost. Proceedings of the 18th European Biomass Conference and Exhibition, 3-7 May 2010, Lyon, France, pp. 923-930

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.