Literatura académica sobre el tema "Residential demand modelling"
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Artículos de revistas sobre el tema "Residential demand modelling"
Dilaver, Zafer y Lester C. Hunt. "Modelling and forecasting Turkish residential electricity demand". Energy Policy 39, n.º 6 (junio de 2011): 3117–27. http://dx.doi.org/10.1016/j.enpol.2011.02.059.
Texto completoAssimakopoulos, V. "Residential energy demand modelling in developing regions". Energy Economics 14, n.º 1 (enero de 1992): 57–63. http://dx.doi.org/10.1016/0140-9883(92)90025-9.
Texto completoWorthington, Andrew C. y Mark Hoffman. "AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING". Journal of Economic Surveys 22, n.º 5 (24 de julio de 2008): 842–71. http://dx.doi.org/10.1111/j.1467-6419.2008.00551.x.
Texto completoAtalla, Tarek N. y Lester C. Hunt. "Modelling residential electricity demand in the GCC countries". Energy Economics 59 (septiembre de 2016): 149–58. http://dx.doi.org/10.1016/j.eneco.2016.07.027.
Texto completoAlcocer Yamanaka, Víctor Hugo y Velitchko G. Tzatchkov. "Neyman-Scott-based water distribution network modelling". Ingeniería e Investigación 32, n.º 3 (1 de septiembre de 2012): 32–36. http://dx.doi.org/10.15446/ing.investig.v32n3.35937.
Texto completoMegri, Ahmed Cherif y Yao Yu. "Study of residential underfloor air distribution (UFAD) systems using a new modelling approach". Indoor and Built Environment 26, n.º 1 (28 de julio de 2016): 5–20. http://dx.doi.org/10.1177/1420326x15597544.
Texto completoStarr, Claudia, Thomas G. Cowing y David L. McFadden. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of the Operational Research Society 37, n.º 8 (agosto de 1986): 823. http://dx.doi.org/10.2307/2581969.
Texto completoChatterjee, Samprit, Thomas G. Cowing, Daniel L. McFadden y Paul C. Stern. "Macroeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand". Journal of Business & Economic Statistics 3, n.º 4 (octubre de 1985): 413. http://dx.doi.org/10.2307/1391737.
Texto completoStarr, Claudia. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand". Journal of the Operational Research Society 37, n.º 8 (agosto de 1986): 823–24. http://dx.doi.org/10.1057/jors.1986.145.
Texto completoBen Zaied, Younes y Marie Estelle Binet. "Modelling seasonality in residential water demand: the case of Tunisia". Applied Economics 47, n.º 19 (21 de enero de 2015): 1983–96. http://dx.doi.org/10.1080/00036846.2014.1002896.
Texto completoTesis sobre el tema "Residential demand modelling"
Gardner, Kerry. "Residential water demand modelling and behavioural economics". Thesis, University of East Anglia, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539372.
Texto completoUrban, Graeme John. "Probabalistic load modelling of electrical demand of residential water heating". Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20071.
Texto completoENGLISH ABSTRACT: Energy efficiency and the move to renewable energy resources are of vital importance in growing profitable and sustainable economies. In recent years, greater emphasis has been placed on institutions, companies and individuals to reduce their electrical energy demand through energy management. In an attempt to reduce the demand, the electrical power utility in South Africa, Eskom, has introduced Demand Side Management programs and substantial increases in electricity tariffs. In addition to these, tax incentives have been offered to help off-set the capital costs associated with the investments made in replacing old electrical equipment with new electrically efficient equipment. Thus the need for accurate Measurement and Verification of electrical energy demand reduction, to substantiate fiscal claims, has become imperative. The main purpose of Measurement and Verification is to investigate the actual monetary performance of an energy savings project. Energy savings assessments, based on purely deterministic baseline demand, do not adequately represent the statistical nature of the savings impacts of many practical load systems, as disclosed in a reporting period. This thesis presents the development of a generic probabilistic methodology to determine the demand profiles of preand post-Energy Conservation Measures (ECMs) for practical load systems. The difference between the simulated demand of the pre- and post-ECMs for a particular set of variables represent the electrical demand impact. The electrical demand of the pre- and post-ECMs is defined in terms of Probability Density Functions, and derived using a multivariate kernel density estimation algorithm. The approach is tested using a simulation model of a waterheating geyser implemented in MATLAB. Three different ECMs are simulated using the geyser model and demand density estimation. The results of the demand impacts of the ECMs are presented and evaluated. With regards to possible future research this methodology could be applied to the evaluation of the demand impacts of heat pump technologies and solar water heaters.
AFRIKAANSE OPSOMMING: en die skuif na hernubare energiebronne is van deurslaggewende belang vir die ontwikkeling van winsgewende en volhoubare ekonomieë. Onlangs is meer klem geplaas op instansies, maatskappye en individue om hul aanvraag na energie te verminder met behulp van energiebestuur. In ‘n poging om die aanvraag te verlaag, het Eskom, Suid-Afrika se elektrisiteitsverskaffer, aansienlike elektrisiteitstariefverhogings ingelyf en Aanvraagbestuursprogramme van stapel gestuur. Bykomend hiertoe is belastingaansporings ook aangebied, waarteen kapitale kostes, geassosieer met die vervanging van ou elektriese toerusting met nuwe elektries doeltreffende toerusting, afgeset kan word. Derhalwe het die behoefte aan akkurate Meting en Verifikasie van elektriese energie aanvraagvermindering, om finansiële eise te staaf, noodsaaklik geword. Die hoofdoel van Meting en Verifikasie is om die werklike finansiële prestasie van energiebesparingsprojek te ondersoek soos bekend gemaak word tydens ’n verslagdoeningstydperk. Energiebesparingassesserings wat slegs gebaseer word op die suiwer deterministiese basislyn aanvraag na elektrisiteit, verteenwoordig nie die werklike statistiese aard van die besparingsimpakte van baie praktiese lasstelsels nie. Hierdie tesis stel die ontwikkeling van generiese waarskynlikheids-metodologie voor, om die voor- en na- Energiebesparings-maatreëls se aanvraagprofiele vir sulke praktiese lasstelsels, vas te stel. Die verskil tussen die gesimuleerde aanvraag van die voor- en na- Energiebesparings-maatreëls vir spesifieke stel veranderlikes verteenwoordig die elektriese aanvraag impak. Die voor- en na- Energiebesparings-maatreëls van die energieverbruik profieldata word gedefinieer in terme van Waarskynlikheidsdigtheidsfunksies en afgelei deur gebruik te maak van meerveranderlike kerndigtheidafskattingsalgoritme. Die benadering is getoets deur gebruik te maak van simuleringsmodel van warmwaterstelsel geïmplimenteer in MATLAB. Drie verskillende voor- en na- Energiebesparings-maatreëls is gesimuleer met behulp van die warmwaterstelselmodel en aanvraag digtheidafskatting. Die resultate van die elektriese aanvraag impakte van die voor- en na- Energiebesparings-maatreëls word vervolgens bespreek en geëvalueer. Met betrekking tot moontlike toekomstige navorsing kan hierdie metodologie toegepas word om die aanvraag impakte van hittepomp- en sonwaterverwarmingstegnologieë te evalueer.
Rahman, Md Moktadir. "Modelling and analysis of demand response implementation in the residential sector". Thesis, Rahman, Md Moktadir (2018) Modelling and analysis of demand response implementation in the residential sector. PhD thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/40779/.
Texto completoBoyce, Daniel J. B. "Micro-component water demand scenario modelling for catchment scale residential water use". Thesis, Cranfield University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443748.
Texto completoGyamfi, Samuel. "Demand Response Assessment and Modelling of Peak Electricity Demand in the Residential Sector: Information and Communcation Requirements". Thesis, University of Canterbury. Mechanical Engineering Department, 2010. http://hdl.handle.net/10092/5063.
Texto completoRahman, Mohammad Lutfur. "Modelling flyover induced travel demand in Dhaka, Bangladesh". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/113830/1/Mohammad%20Lutfur_Rahman_Thesis.pdf.
Texto completoSancho, Tomás Ana. "Integrated modelling of electrical energy systems for the study of residential demand response strategies". Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/46872/.
Texto completoMaurer, Nathalie. "Modelling urban development trends and outdoor residential water demand in the Okanagan Basin, British Columbia". Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/17533.
Texto completoTsagkarakis, George. "Domestic demand and network management in a user-inclusive electrical load modelling framework". Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16207.
Texto completoKelly, Scott. "Decarbonising the English residential sector : modelling policies, technologies and behaviour within a heterogeneous building stock". Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244708.
Texto completoLibros sobre el tema "Residential demand modelling"
Elsland, Rainer. Long-Term Energy Demand in the German Residential Sector: Development of an Integrated Modelling Concept to Capture Technological Myopia. Nomos Verlagsgesellschaft, 2017.
Buscar texto completoCapítulos de libros sobre el tema "Residential demand modelling"
Garbacz, Christopher. "Residential Electricity Demand Modelling with Secret Data". En Regulating Utilities in an Era of Deregulation, 137–54. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-08714-3_9.
Texto completoBartlett, Sarita, Steinar Strøm y Øystein Olsen. "Residential energy demand — the evolution and future potential of natural gas in Western Europe". En Recent Modelling Approaches in Applied Energy Economics, 29–47. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-011-3088-2_2.
Texto completoMaroufmashat, Azadeh, Q. Kong, Ali Elkamel y Michael Fowler. "Modelling the Impact of Uncontrolled Electric Vehicles Charging Demand on the Optimal Operation of Residential Energy Hubs". En Electric Vehicles in Energy Systems, 289–312. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34448-1_12.
Texto completoElsland, Rainer. "2. Relevant developments in the residential sector and existing modelling approaches". En Long-term Energy Demand in the German Residential Sector, 43–68. Nomos Verlagsgesellschaft mbH & Co. KG, 2016. http://dx.doi.org/10.5771/9783845267487-42.
Texto completoElsland, Rainer. "3. Development of an integrated modelling concept for long-term energy demand analysis". En Long-term Energy Demand in the German Residential Sector, 69–78. Nomos Verlagsgesellschaft mbH & Co. KG, 2016. http://dx.doi.org/10.5771/9783845267487-68.
Texto completoActas de conferencias sobre el tema "Residential demand modelling"
"Forecast of residential demand drivers for Greater Darwin". En 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.beatty.
Texto completoPieterse-Quirijns, E. J., E. J. M. Blokker, J. H. G. Vreeburg y E. v.d. Blom. "Modelling Characteristic Values for Non-Residential Water Demand". En 12th Annual Conference on Water Distribution Systems Analysis (WDSA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41203(425)111.
Texto completoDu, Baoxiang, Gregor Verbic y John Fletcher. "Thermal modelling for demand response of residential buildings". En 2017 Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2017. http://dx.doi.org/10.1109/aupec.2017.8282379.
Texto completoTiedemann, Kenneth H. "Modelling Residential and Commercial Demand for Electricity Using Autoregressive Distributed Lag Models". En Modelling, Identification and Control / 827: Computational Intelligence. Calgary,AB,Canada: ACTAPRESS, 2015. http://dx.doi.org/10.2316/p.2015.826-013.
Texto completoSimani, Kyppy N., Yuval O. Genga y Yu-Chieh J. Yen. "Using LSTM To Perform Load Modelling For Residential Demand Side Management". En 2023 31st Southern African Universities Power Engineering Conference (SAUPEC). IEEE, 2023. http://dx.doi.org/10.1109/saupec57889.2023.10057875.
Texto completoTiedemann, Kenneth H. "Time-Series Modeling of the Demand for Energy Efficient Residential Appliances". En Visualization, Imaging and Image Processing / 783: Modelling and Simulation / 784: Wireless Communications. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.783-006.
Texto completoDing, Y. y J. Yang. "An event-based flexible load modelling method for optimising residential demand response". En 11th International Conference on Renewable Power Generation - Meeting net zero carbon (RPG 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.1678.
Texto completoJambagi, Akhila, Michael Kramer y Vicky Cheng. "Residential electricity demand modelling: Activity based modelling for a model with high time and spatial resolution". En 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. http://dx.doi.org/10.1109/irsec.2015.7455047.
Texto completoBroka, Zane, Jevgenijs Kozadajevs, Antans Sauhats, Donal P. Finn y William J. N. Turner. "Modelling residential heat demand supplied by a local smart electric thermal storage system". En 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2016. http://dx.doi.org/10.1109/rtucon.2016.7763128.
Texto completoTardioli, Giovanni, Ruth Kerrigan, Mike Oates, James O'Donnell y Donal Finn. "A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings". En 2017 Building Simulation Conference. IBPSA, 2017. http://dx.doi.org/10.26868/25222708.2017.464.
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