Gestión de la energía: el usuario de energía como parte activa del sistema

Aalami, H. A., Parsa Moghaddam, M., & Yousefi, G. R. (2015). Evaluation of nonlinear models for time-based rates demand response programs. International Journal of Electrical Power & Energy Systems, 65, 282–290. https://doi.org/https://doi.org/10.1016/j.ijepes.2014.10.021

Abushnaf, J., Rassau, A., & Górnisiewicz, W. (2015). Impact of dynamic energy pricing schemes on a novel multi-user home energy management system. Electric Power Systems Research, 125, 124–132. Retrieved from https://doi.org/10.1016/j.epsr.2015.04.003

Afaneen A.Abbood, Mohammed A.Salih, H. N. M. (2010). Management of electricity peak load for residential sector in Baghdad city by using solar generation. International Journal of Energy and Environment, 1(3), 487–500

Ahmed, M. S., Mohamed, A., Khatib, T., Shareef, H., Homod, R. Z., & Ali, J. A. (2017). Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy and Buildings, 138, 215–227. https://doi.org/https://doi.org/10.1016/j. enbuild.2016.12.052

Al Essa, M. J. M. (2019). Home energy management of thermostatically controlled loads and photovoltaic-battery systems. Energy, 176, 742– 752. https://doi.org/10.1016/j.energy.2019.04.041

Albert, A., & Maasoumy, M. (2016). Predictive segmentation of energy consumers. Applied Energy, 177, 435–448. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.05.128

Ali, M., Alahäivälä, A., Malik, F., Humayun, M., Safdarian, A., & Lehtonen, M. (2015). A market-oriented hierarchical framework for residential demand response. International Journal of Electrical Power and Energy Systems, 69, 257–263. https://doi.org/10.1016/j.ijepes.2015.01.020

Alimi, O., & Ouahada, K. (2018). Smart Home Appliances Scheduling to Manage Energy Usage. https://doi.org/10.1109/ICASTECH.2018.8507138

Allen, D., Janda, K., & College, O. (2006). The Effects of Household Characteristics and Energy Use Consciousness on the Effectiveness of Real-Time Energy Use Feedback : A Pilot Study Continuous Feedback : The Next Step In Residential Energy Conservation ? Buildings, (September), 1–12. https://doi.org/http://dx.doi.org/10.1037/0021-9010.69.3.416

ALRababah, I., & Rababah, L. (2018). The Use of Brainstorming Strategy Among Teachers of Arabic for Speakers of Other Languages (ASOL) in Writing Classes. International Journal of English Linguistics, 9(1), 15. https://doi.org/10.5539/ijel.v9n1p15

Annala, S., Viljainen, S., Tuunanen, J., & Honkapuro, S. (2014). Does Knowledge Contribute to the Acceptance of Demand Response? Journal of Sustainable Development of Energy, Water and Environment Systems, 2, 51–60. https://doi.org/10.13044/j.sdewes.2014.02.0005

Arias, S. M., & Suarez, E. (2016). Evaluación de eficiencia energética en un conjunto residencial con la entrada masiva de vehículos eléctricos. Universidad Distrital Francisco José de Caldas.

Arribas, C. (2012). Análisis del balance neto fotovoltaico. Carlos III de Madrid. Retrieved from https://core.ac.uk/download/pdf/30046508.pdf

Ayan, O., & Turkay, B. E. (2017). Comparison of lighting technologies in residential area for energy conservation. In 2017 2nd International Conference Sustainable and Renewable Energy Engineering (ICSREE) (pp. 116– 120). https://doi.org/10.1109/ICSREE.2017.7951523

Aznavi, S., Fajri, P., Asrari, A., & Khazaei, J. (2019). Two-Stage Energy Management of Smart Homes in Presence of Intermittencies. https://doi. org/10.1109/ITEC.2019.8790479

Baldi, S., Korkas, C. D., Lv, M., & Kosmatopoulos, E. B. (2018). Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach. Applied Energy, 231(October),1246–1258. https://doi.org/10.1016/j.apenergy.2018.09.188

Barber, C., Borras, C., Hone, G., Macleod, I., Mcmaster, R., Salmon, P., & Stanton, N. (2005). Cognitive Task Analysis: Current use and practice in the UK Armed Forces and elsewhere

Barot, S., & Taylor, J. A. (2017). A concise, approximate representation of a collection of loads described by polytopes. International Journal of Electrical Power & Energy Systems, 84, 55–63. https://doi.org/https://doi. org/10.1016/j.ijepes.2016.05.001

Bayindir, R., Colak, I., Fulli, G., & Demirtas, K. (2016). Smart grid technologies and applications. Renewable and Sustainable Energy Reviews, 66, 499–516. https://doi.org/10.1016/j.rser.2016.08.002

Beunder, A., & Groot, L. (2015). Energy consumption, cultural background and payment structure. Journal of Cleaner Production, 94, 137–143. https://doi.org/https://doi.org/10.1016/j.jclepro.2015.01.083

Borshchev, A., & Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools. In The 22nd International Conference of the System Dynamics Society. Oxford.

Boynuegri, A. R., Yagcitekin, B., Baysal, M., Karakas, A., & Uzunoglu, M. (2013). Energy management algorithm for smart home with renewable energy sources. In International Conference on Power Engineering, Energy and Electrical Drives (pp. 1753–1758). https://doi.org/10.1109/PowerEng.2013.6635883

Byun, J, Jeon, B., Noh, J., Kim, Y., & Park, S. (2012). An intelligent self-adjusting sensor for smart home services based on ZigBee communications. IEEE Transactions on Consumer Electronics, 58(3), 794–802. https://doi.org/10.1109/TCE.2012.6311320

Byun, Jinsung, Hong, I., & Park, S. (2012). Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability. IEEE Transactions on Consumer Electronics, 58(4), 1194–1201. https://doi.org/10.1109/ TCE.2012.6414985

Caldera, M., Ungaro, P., Cammarata, G., & Puglisi, G. (2018). Survey-based analysis of the electrical energy demand in Italian households. Mathematical Modelling of Engineering Problems, 5(3), 217–224. https://doi. org/10.18280/mmep.050313

Calvillo, C. F., Sánchez-Miralles, A., Villar, J., & Martín, F. (2016). Optimal planning and operation of aggregated distributed energy resources with market participation. Applied Energy, 182, 340–357. https://doi.org/ https://doi.org/10.1016/j.apenergy.2016.08.117

Camero, A., Luque, G., Bravo, Y., & Alba, E. (2018). Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case. Energies . https://doi.org/10.3390/en11071788

Castaño, N. (2013). Una aproximación a la adopción de medidores inteligentes en el mercado eléctrico colombiano y su influencia en la demanda. Universidad Nacional.

Chavali, P., Yang, P., & Nehorai, A. (2014). A Distributed Algorithm of Appliance Scheduling forHome Energy Management System. IEEE Transactions on Smart Grid, 5(1), 282–290. https://doi.org/10.1109/TSG.2013.2291003

Choi, D.-H.,&Xie, L.(2016). A framework for sensitivity analysis of data errors on home energy management system. Energy, 117, 166–175. https:// doi.org/https://doi.org/10.1016/j.energy.2016.10.062

Choi, S., Haque, M. S., Arafat, A., & Toliyat, H. A. (2017). Detection and Estimation of Extremely Small Fault Signature by Utilizing Multiple Current Sensor Signals in Electric Machines. IEEE Transactions on Industry Applications, 53(3), 2805–2816. https://doi.org/10.1109/TIA.2017.2660463

Codensa. (2016). Tarifas de energía de Codensa. Retrieved from https://www.codensa.com.co/hogar/tarifas

Collotta, M., & Pau, G. (2015). A solution based on bluetooth low energy for smart home energy management. Energies, 8(10), 11916–11938. https://doi.org/10.3390/en81011916

Comisión de Regulación de energía y gas (CREG). (1996). Resolución 086 de 1996. Retrieved from http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resoluci%25C3%25B3n-1996-CRG86-96

Comisión de Regulación de Energía y Gas (CREG). (2007). Resolución 119 de 2007. Retrieved from http://apolo.creg.gov.co/Publicac.nsf/2b8fb06f012cc9c245256b7b00789b0c/c63f06a9114e1a150525785a007a6fa2/$FILE/Creg119-2007.pdf

Comisión de Regulación de Energía y Gas (CREG). (2017). Resolución 140 de 2017. Retrieved from http://apolo.creg.gov.co/Publicac. nsf/1c09d18d2d5ffb5b05256eee00709c02/e0df6446d8799169052581a90076fa95?OpenDocument

Comision de Regulacion de Energia y Gas CREG. (2018). Resolución 30 de 2018. Mme. Retrieved from http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/83b41035c2c4474f05258243005a1191/$FILE/Creg030-2018.pdf

Comoglio, C., & Botta, S. (2012). The use of indicators and the role of environmental management systems for environmental performances improvement: A survey on ISO 14001 certified companies in the automotive sector. Journal of Cleaner Production, 20(1), 92–102. https://doi.org/10.1016/j.jclepro.2011.08.022

Congreso de la República de Colombia. Ley 1715 de 2014 (2014). Bogotá D.C., Colombia: Congreso de Colombia. Retrieved from http://www. secretariasenado.gov.co/senado/basedoc/ley_1715_2014.html

Consorcio CORPOEMA CUSA. (2012). Caracterización Energética del Sector Residencial Urbano y Rural en Colombia.

Coria, G.E.; Sanchez, A.M.; Al-Sumaiti, A.S.; Rattá, G.A.; Rivera, S.; Romero, A. (2019). A Framework for Determining a Prediction-Of-Use Tari ff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles. Energies, 12(23:4487). https://doi.org/doi:10.3390/en12234487

Corporación Ambiental Empresarial [CAEM], & Corporación Autonoma Regional [CAR]. (2013). Guía Metodológica para el cálculo de la Huella de Carbono Corporativa a Nivel Sectorial. Retrieved from https://www.car. gov.co/index.php?idcategoria=42211&download=Y%0A

Cui, T., Carr, J., Brissette, A., & Ragaini, E. (2017). Connecting the Last Mile: Demand Response in Smart Buildings. Energy Procedia, 111(September 2016), 720–729. https://doi.org/10.1016/j.egypro.2017.03.234

Da Silva Gonçalves, V., & Mil-Homens dos Santos, F. (2019). Energy management system ISO 50001:2011 and energy management for sustainable development. Energy Policy, 133, 110868. https://doi.org/https://doi. org/10.1016/j.enpol.2019.07.004

Daae, J., & Boks, C. (2015). A classification of user research methods for design for sustainable behaviour. Journal of Cleaner Production, 106

Darby, S. (2001). Making it Obvious: Designing Feedback into Energy Consumption BT - Energy Efficiency in Household Appliances and Lighting. In P. Bertoldi, A. Ricci, & A. de Almeida (Eds.), Energy Efficiency in Household Appliances and Lighting (pp. 685–696). Berlin, Heidelberg: Springer Berlin Heidelberg.

Dave, S., Sooriyabandara, M., & Yearworth, M. (2013). System behaviour modelling for demand response provision in a smart grid. Energy Policy, 61, 172–181. https://doi.org/https://doi.org/10.1016/j.enpol.2013.05.098

De la Horra Navarro, J. (2014). Estadística Aplicada. (D. de Santos, Ed.). Madrid.

Dessens, O., Anandarajah, G., & Gambhir, A. (2016). Limiting global warming to 2 °C: What do the latest mitigation studies tell us about costs, technologies and other impacts? Energy Strategy Reviews 13.

Ding, Y., Wang, Q., Wang, Z., Han, S., & Zhu, N. (2019). An occupancy-based model for building electricity consumption prediction: A case study of three campus buildings in Tianjin. Energy and Buildings, 202, 109412. https://doi.org/10.1016/J.ENBUILD.2019.109412

Domae, A., Oe, T., Kiryu, S., & Kaneko, N. (2016). Evaluation of a resistive voltage divider based on a quantized Hall resistance voltage divider. In 2016 Conference on Precision Electromagnetic Measurements (CPEM 2016) (pp. 1–2). https://doi.org/10.1109/CPEM.2016.7540762

Eid, C., Koliou, E., Valles, M., Reneses, J., & Hakvoort, R. (2016). Time-based pricing and electricity demand response: Existing barriers and next steps. Utilities Policy, 40, 15–25. https://doi.org/https://doi.org/10.1016/j.jup.2016.04.001

Electrocalculator. (2018). Tabla con los electrodomésticos por grupos. Retrieved from http://www.electrocalculator.com/tabla-agrupada.php

Esther, B. P., & Kumar, K. S. (2016). A survey on residential Demand Side Management architecture, approaches, optimization models and methods. Renewable and Sustainable Energy Reviews, 59, 342–351. https:// doi.org/10.1016/j.rser.2015.12.282

Farmani, F., Parvizimosaed, M., Monsef, H., & Rahimi-Kian, A. (2018). A conceptual model of a smart energy management system for a residential building equipped with CCHP system. International Journal of Electrical Power & Energy Systems, 95, 523–536.

Faruqui, A., Harris, D., & Hledik, R. (2010). Unlocking the €53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy, 38(10), 6222–6231. https://doi.org/https://doi.org/10.1016/j. enpol.2010.06.010

Faruqui, A., Sergici, S., & Sharif, A. (2010). The impact of informational feedback on energy consumption-A survey of the experimental evidence. Energy, 35(4), 1598–1608. https://doi.org/10.1016/j.energy.2009.07.042

Fera, M., Macchiaroli, R., Iannone, R., Miranda, S.,&Riemma, S.(2016). Economic evaluation model for the energy Demand Response. Energy, 112, 457–468. https://doi.org/https://doi.org/10.1016/j.energy.2016.06.123

Ferreri, E., Salotti, J. M., & Favier, P. A. (2016). Simulation of Habits and Planned Activities Related to Electrical Domestic Consumption. Proceedings - UKSim-AMSS 17th International Conference on Computer Modelling and Simulation, UKSim 2015, 171–175. https://doi.org/10.1109/UKSim.2015.102

Fluke Brands. (2019a). Fluke 435 Series II Power Quality and Energy Analyzer. Retrieved from http://en-us.fluke.com/products/power-quality-analyzers/fluke-435-ii-power-quality.html

Fluke Brands. (2019b). Registrador trifásico de energía Fluke 1730. Retrieved from http://www.fluke.com/fluke/coes/medidores-de-calidad-dela-energia-electrica/logging-power-meters/fluke-1730.htm?pid=77038

Ford, R., Pritoni, M., Sanguinetti, A., & Karlin, B. (2017). Categories and functionality of smart home technology for energy management. Building and Environment, 123, 543–554. https://doi.org/10.1016/j.buildenv.2017.07.020

Gellings, C. W. (2016). Chapter 15 - Back to Basics: Enhancing Efficiency in the Generation and Delivery of Electricity. In F. P. B. T.-E. E. Sioshansi (Ed.), Energy Efficiency and Renewable Energy Handbook (Second, pp. 375–417). Boston: Academic Press. https://doi.org/https://doi.org/10.1016/ B978-0-12-397879-0.00015-3

González,O., Pavas, A., & Sánchez, S.(2017). Cuantificación del Ahorro de Energía Eléctrica en Clientes Residenciales Mediante Acciones de Gestión de Demanda. UIS Ingenierías, 1–8.

Gooding, L., & Gul, M. S. (2016). Energy efficiency retrofitting services supply chains: A review of evolving demands from housing policy. Energy Strategy Reviews, 11–12, 29–40.

Gosak, M., Markovič, R., Dolenšek, J., Slak Rupnik, M., Marhl, M., Stožer, A., & Perc, M. (2018). Network science of biological systems at different scales: A review. Physics of Life Reviews, 24, 118–135. https://doi. org/10.1016/j.plrev.2017.11.003

Grandjean, A., Adnot, J., & Binet, G. (2012). A review and an analysis of the residential electric load curve models. Renewable and Sustainable Energy Reviews, 16(9), 6539–6565.

Guo, X., Bao, Z., & Yan, W. (2019). Stochastic model predictive control based scheduling optimization of multi-energy system considering hybrid CHPs and EVs. Applied Sciences (Switzerland), 9(2). https://doi. org/10.3390/app9020356

Gupta, S., Campa, A., & Ruffo, S. (2014). Kuramoto model of synchronization: equilibrium and nonequilibrium aspects. Journal of Statistical Mechanics: Theory and Experiment, 2014(8), R08001. https://doi. org/10.1088/1742-5468/14/08/r08001

Han, D., & Lim, J. (2010). Design and implementation of smart home energy management systems based on zigbee. IEEE Transactions on Consumer Electronics, 56(3), 1417–1425. https://doi.org/10.1109/TCE.2010.5606278

Harding, M., & Lamarche, C. (2016). Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time-of-Use Electricity Pricing Policies. Journal of Policy Analysis and Management, 35(4), 906–931. https://doi.org/10.1002/pam.21928

Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014). A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings. IEEE Communications Surveys & Tutorials, 16(3), 1460–1495. https://doi.org/10.1109/SURV.2014.032014.00094

Hiller, C. (2015). Factors influencing residents’ energy use—A study of energy-related behaviour in 57 Swedish homes. Energy and Buildings, 87, 243–252.

Horta, L. A. (2010). Indicadores de políticas públicas en materia de eficiencia energética en América Latina y el Caribe, 131. https://doi.org/ LC/W.322

HT Instruments. (2019). PQA823 de HT Instruments. Retrieved from http://www.ht-instruments.com/en/products/power-quality-analyzers/ touch-screen/pqa823/

Hu, X., Chiu, Y.-C., & Zhu, L. (2015). Behavior Insights for an Incentive-Based Active Demand Management Platform. International Journal of Transportation Science and Technology, 4(2), 119–133. https://doi.org/https://doi.org/10.1260/2046-0430.4.2.119

Huang,G., Yang, J.,& Wei, C.(2016). Cost-Effective and Comfort-Aware Electricity Scheduling for Home Energy Management System. In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)(pp. 453–460). https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.73

Huang, S., Tushar, W., Yuen, C., & Otto, K. (2015). Quantifying economic benefits in the ancillary electricity market for smart appliances in Singapore households. Sustainable Energy, Grids and Networks, 1, 53–62. https://doi.org/https://doi.org/10.1016/j.segan.2014.12.002

Huang, Y., Zhan, J., Luo, C., Wang, L., Wang, N., Zheng, D., … Ren, R. (2019). An electricity consumption model for synthesizing scalable electricity load curves. Energy, 169, 674–683. https://doi.org/10.1016/J.ENERGY.2018.12.050

Hung, M.-F., & Huang, T.-H. (2015). Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing. Energy Economics, 48, 168–177. https://doi.org/https://doi.org/10.1016/j.eneco.2015.01.010

Hutton, R. B., Mauser, G. A., Filiatrault, P., & Ahtola, O. T. (1986). Effects of Cost-Related Feedback on Consumer Knowledge and Consumption Behavior: A Field Experimental Approach. Journal of Consumer Research, 13(3), 327–336. https://doi.org/10.1086/209072

IAEA Departamento de Asuntos Económicos y Sociales de las Naciones Unidas, IEA, Eurostat, & Agencia Europea de Medio Ambiente. (2008). Indicadores energéticos del desarrollo sostenible: directrices y metodologías. https://doi.org/10.4016/46611.01

Instituto Nicaraguense de energía. (2018). Consumo promedio de aparatos eléctricos en base a horas de uso domiciliario. Retrieved from http:// www.ine.gob.ni/DAC/consultas/Tabla_Aparatos_Elec_Agost10.pdf.

International Energy Agency. (2014). Energy Efficiency Indicators. https://doi.org/https://doi.org/https://doi.org/10.1787/9789264215672-en

Issi, F., & Kaplan, O. (2018). The determination of load profiles and power consumptions of home appliances. Energies, 11(3). https://doi. org/10.3390/en11030607

Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable and Sustainable Energy Reviews, 103, 308–319. https://doi.org/10.1016/J.RSER.2018.12.054

Jovanović, B., & Filipović, J. (2016). ISO 50001 standard-based energy management maturity model – proposal and validation in industry. Journal of Cleaner Production, 112, 2744–2755. https://doi.org/https://doi.org/10.1016/j.jclepro.2015.10.023

Kandil, M. S., El-Debeiky, S. M., & Hasanien, N. E. (2002). Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System. Power Engineering Review, IEEE, 22, 78. https://doi. org/10.1109/MPER.2002.4312144

Kazmi, A. H., O’Grady, M. J., & O’hare, G. M. . (2013). Energy Management in the Smart Home. 2013 IEEE 10th International Conference on Ubiquitous Intelligence & Computing.

Kipping, A., & Trømborg, E. (2018). Modeling aggregate hourly energy consumption in a regional building stock. Energies, 11(1). https://doi. org/10.3390/en11010078

Klöckner, C., Matthies, E., & Hunecke, M. (2003). Problems of Operationalizing Habits and Integrating Habits in Normative Decision‐Making Models1. Journal of Applied Social Psychology, 33, 396–417. https://doi.org/10.1111/j.1559-1816.2003.tb01902.x.

Kowsari, R., & Zerriffi, H. (2011). Three dimensional energy profile:. A conceptual framework for assessing household energy use. Energy Policy, 39(12), 7505–7517. https://doi.org/10.1016/j.enpol.2011.06.030

Krishna, P. N., Gupta, S. R., Shankaranarayanan, P. V., Sidharth, S., & Sirphi, M. (2018). Fuzzy Logic Based Smart Home Energy Management System. 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018. https://doi.org/10.1109/ ICCCNT.2018.8493744

Lai, Y.-X., Lai, C.-F., Huang, Y.-M., & Chao, H.-C. (2013). Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home. Information Sciences, 230, 39–55. https://doi.org/https://doi.org/10.1016/j.ins.2012.10.002

Langlois-Bertrand, S., Benhaddadi, M., Jegen, M., & Pineau, P.-O. (2015). Political-institutional barriers to energy efficiency. Energy Strategy Reviews, 8, 30–38. https://doi.org/https://doi.org/10.1016/j.esr.2015.08.001

Lavín, F. V., Dale, L., Hanemann, M., & Moezzi, M. (2011). The impact of price on residential demand for electricity and natural gas. Climatic Change, 109(1), 171–189. https://doi.org/10.1007/s10584-011-0297-0

Lei, M., Tang, M., Li, L., Ye, L., & Pan, Z. (2019). Forecasting Short-Term Residential Electricity Consumption Using a Deep Fusion Model. In Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering (Vol. 529). Singapore: Springer. https://doi.org/https://doi.org/10.1007/978-981-13-2291-4_36

Levine, D. M. (2010). Estadistica Para Administración. (Pearson, Ed.). México

Losi, A., Mancarella, P., & Vicino, A. (2015a). Active Consumer Characterization and Aggregation. In Integration of Demand Response Into the Electricity Chain (pp. 11–39). John Wiley & Sons, Ltd. https://doi. org/10.1002/9781119245636.ch2.

Losi, A., Mancarella, P., & Vicino, A. (2015b). System-Level Benefits of Demand Response. In Integration of Demand Response Into the Electricity Chain (pp. 143–172). John Wiley & Sons, Ltd. https://doi. org/10.1002/9781119245636.ch7.

Louis, J. N., Caló, A., Leiviskä, K., & Pongrácz, E. (2016). Modelling home electricity management for sustainability: The impact of response levels, technological deployment & occupancy. Energy and Buildings, 119, 218–232. https://doi.org/10.1016/j.enbuild.2016.03.012.

Maguire, M. (2001). Methods to support human-centred design. International Journal of Human-Computer Studies, 55(4), 587–634. https://doi. org/https://doi.org/10.1006/ijhc.2001.0503

McKenna, E., Krawczynski, M., & Thomson, M. (2015). Four-state domestic building occupancy model for energy demand simulations. Energy and Buildings, 96, 30–39. https://doi.org/10.1016/j.enbuild.2015.03.013

Megahed, T. F., Abdelkader, S. M., & Zakaria, A. (2019). Energy management in zero-energy building using neural network predictive control. IEEE Internet of Things Journal, 6(3), 5336–5344. https://doi.org/10.1109/ JIOT.2019.2900558

Mehdi, L., Ouallou, Y., Mohamed, O., & Hayar, A. (2018). New smart home’s energy management system design and implementation for frugal smart cities. 2018 International Conference on Selected Topics in Mobile and Wireless Networking, MoWNeT 2018, 149–153. https://doi.org/10.1109/ MoWNet.2018.8428865

Meyers, R. J., Williams, E. D., & Matthews, H. S. (2010). Scoping the potential of monitoring and control technologies to reduce energy use in homes. Energy and Buildings, 42(5), 563–569. https://doi.org/https://doi. org/10.1016/j.enbuild.2009.10.026

Michael, J., Henke, C., & Trachtler, A. (2019). Decentralized Energy Management for Smart Home System of Systems. https://doi.org/10.1109/ SYSCON.2019.8836960

Miu, L. M., Mazur, C. M., van Dam, K. H., Lambert, R. S. C., Hawkes, A., & Shah, N. (2019). Going smart, staying confused: Perceptions and use of smart thermostats in British homes. Energy Research & Social Science, 57, 101228. https://doi.org/https://doi.org/10.1016/j.erss.2019.101228

Mlecnik, E., Parker, J., Ma, Z., Corchero, C., Knotzer, A., & Pernetti, R. (2019). Policy challenges for the development of energy flexibility services. Energy Policy, 111147. https://doi.org/https://doi.org/10.1016/j. enpol.2019.111147

Mohassel, R. R., Fung, A. S., Mohammadi, F., & Raahemifar, K. (2014). A survey on advanced metering infrastructure and its application in Smart Grids. In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1–8). https://doi.org/10.1109/CCECE.2014.6901102

Montgomery, D. C.(2014). Diseño y análisis de experimentos.(Limusa, Ed.). México.

Muratori, M., Schuelke-Leech, B. A., & Rizzoni, G. (2014). Role of residential demand response in modern electricity markets. Renewable and Sustainable Energy Reviews, 33, 546–553. https://doi.org/10.1016/j. rser.2014.02.027

Niesten, E., & Alkemade, F. (2016). How is value created and captured in smart grids? A review of the literature and an analysis of pilot projects. Renewable and Sustainable Energy Reviews, 53, 629–638. https://doi.org/10.1016/j.rser.2015.08.069

Nyeng, P., Kok, K., Pineda, S., Grande, O., Sprooten, J., Hebb, B., & Nieuwenhout, F. (2013). Enabling demand response by extending the European electricity markets with a real-time market. In IEEE PES ISGT Europe 2013 (pp. 1–5). https://doi.org/10.1109/ISGTEurope.2013.6695418

O’Reilly, M., Wathey, D., & Gelber, M. (2000). ISO 14031: Effective mechanism to environmental performance evaluation. Corporate Environmental Strategy, 7(3), 267–275. https://doi.org/https://doi.org/10.1016/ S1066-7938(00)80121-9

Otsuka, A. (2019). Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake. In Asia-Pacific Journal of Regional Science (pp. 887–910). https://doi.org/https://doi. org/10.1007/s41685-019-00129-4

Palizban, O., Kauhaniemi, K., & Guerrero, J. M. (2014). Microgrids in active network management—Part I: Hierarchical control, energy storage, virtual power plants, and market participation. Renewable and Sustainable Energy Reviews, 36, 428–439. https://doi.org/https://doi.org/10.1016/j. rser.2014.01.016

Panos, E., Densing, M., & Volkart, K. (2016). Access to electricity in the World Energy Council’s global energy scenarios: An outlook for developing regions until 2030. Energy Strategy Reviews, 9, 28–49. https://doi.org/https://doi.org/10.1016/j.esr.2015.11.003

Patterson, Z., Darbani, J. M., Rezaei, A., Zacharias, J., & Yazdizadeh, A. (2017). Comparing text-only and virtual reality discrete choice experiments of neighbourhood choice. Landscape and Urban Planning, 157, 63–74. https://doi.org/10.1016/j.landurbplan.2016.05.024

Pinzon, J., Corredor, A., Santamaria, F., Hernandez, J., & Trujillo, C. (2014). Implementación de indicadores energéticos en centros educativos. Caso de estudio: Edificio Alejandro Suárez Copete-Universidad Distrital Francisco José de Caldas. Revista Escuela de Administración de Negocios, 0(77 SE-Casos empresariales), 186–200. Retrieved from https://journal.universidadean.edu.co/index.php/Revista/article/view/823

Podgornik, A., Sucic, B., & Blazic, B. (2016). Effects of customized consumption feedback on energy efficient behaviour in low-income households. Journal of Cleaner Production, 130, 25–34. https://doi.org/https:// doi.org/10.1016/j.jclepro.2016.02.009

Ponce, P., Peffer, T., & Molina, A. (2017). Framework for communicating with consumers using an expectation interface in smart thermostats. Energy and Buildings, 145, 44–56. https://doi.org/https://doi.org/10.1016/j. enbuild.2017.03.065

PowerSim. (2019). PowerSim Software. Retrieved from http://www. powersim.com/main/products-services/academic-tools/

Putra, L., Michael, Yudishtira, & Kanigoro, B. (2015). Design and Implementation of Web Based Home Electrical Appliance Monitoring, Diagnosing, and Controlling System. Procedia Computer Science, 59, 34–44. https://doi.org/https://doi.org/10.1016/j.procs.2015.07.335

Quevedo, A. D., Suarez, E. G., Arias, S. M., Santamaria, F., & Alarcon, J. A. (2016). Assessment of Energy Efficiency indicators on a residential building with Plug-in Electric Vehicles and energy action plans for users. In 2015 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT LATAM 2015 (pp. 881–886). https://doi.org/10.1109/ISGT-LA.2015.7381271

Rasouli, M. (2010). A Game-Theoretic Framework for Studying Dynamics of Multi Decision-maker Systems. In The 28th International Conference of the System Dynamics Society. Seul.

Richardson, I., Thomson, M., Infield, D., & Clifford, C. (2010). Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 42(10), 1878–1887. https://doi.org/10.1016/J.ENBUILD.2010.05.023

Roy, J., Dowd, A.-M., Muller, A., Pal, S., Prata, N., & Lemmet, S. (2012). Lifestyles, Well-Being and Energy. Global Energy Assessment (GEA), 1527– 1548. https://doi.org/10.1017/cbo9780511793677.027

S., R., & V., M. (2016). HEM algorithm based smart controller for home power management system. Energy and Buildings, 131, 184–192. https:// doi.org/https://doi.org/10.1016/j.enbuild.2016.09.026

Sachdeva, A., & Wallis, P. (2010). Our demand : reducing electricity use in Victoria through demand management.

Sauter, R., & Watson, J. (2007). Strategies for the deployment of micro-generation: Implications for social acceptance. Energy Policy, 35(5), 2770–2779. https://doi.org/https://doi.org/10.1016/j.enpol.2006.12.006

Schuschny, A., & Soto, H. (2009). Documento de proyecto Guía metodológica Diseño de indicadores compuestos de desarrollo sostenible. Retrieved from https://www.cepal.org/ilpes/noticias/paginas/9/35989/diseno_indicadores_compuestos_ddss.pdf

Seligman, C., & Darley, J. M. (1977). Feedback as a means of decreasing residential energy consumption. Journal of Applied Psychology, 62(4), 363–368. https://doi.org/10.1037/0021-9010.62.4.363

Senado de la República de Colombia. (1994). Ley 142 de 1994. Retrieved from http://www.alcaldiabogota.gov.co/sisjur/normas/Norma1.jsp?i=2752

Shafie-khah, M., Parsa Moghaddam, M., Sheikh-El-Eslami, M. K., & Rahmani-Andebili, M. (2012). Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment. Energy, 40(1), 139–150. https://doi.org/https:// doi.org/10.1016/j.energy.2012.02.019

Shahgoshtasbi, D., & Jamshidi, M. . (2014). A New Intelligent Neuro– Fuzzy Paradigm for Energy-Efficient Homes. IEEE Systems Journal, 8(2), 664–673.

Shakeri, M., Shayestegan, M., Abunima, H., Reza, S. M. S., Akhtaruzzaman, M., Alamoud, A. R. M., … Amin, N. (2017). An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy and Buildings, 138, 154–164. https:// doi.org/10.1016/j.enbuild.2016.12.026

Shiraki, H., Nakamura, S., Ashina, S., & Honjo, K. (2016). Estimating the hourly electricity profile of Japanese households – Coupling of engineering and statistical methods. Energy, 114, 478–491. https://doi.org/https:// doi.org/10.1016/j.energy.2016.08.019

Sim, J.,&Waterfield, J.(2019). Focus group methodology: some ethical challenges. Quality & Quantity, 53(6), 3003–3022. https://doi.org/10.1007/ s11135-019-00914-5

Smith, C. B., & Parmenter, K. E. (2016a). Chapter 1 - Introduction. In C. B. Smith & K. E. B. T.-E. M. P. (Second E. Parmenter (Eds.), Energy Management Principles Applications, Benefits, Savings (pp. 1–12). Oxford: Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-802506-2.00001-X

Smith, C. B., & Parmenter, K. E. (2016b). Chapter 3 - General Principles of Energy Management. In C. B. Smith & K. E. B. T.-E. M. P. (Second E. Parmenter (Eds.), Energy Management Principles Applications, Benefits, Savings (pp. 35–44). Oxford: Elsevier. https://doi.org/https://doi.org/10.1016/ B978-0-12-802506-2.00003-3

Sorrell, S. (2015). Reducing energy demand: A review of issues, challenges and approaches. Renewable and Sustainable Energy Reviews, 47, 74–82. https://doi.org/https://doi.org/10.1016/j.rser.2015.03.002

Southern California Edison. (2012). Design & Engineering Services Demand Response Potential of Residential Appliances – Refrigerator ( LG ) (Report No. DR12SCE1.08). Retrieved from http://www.etcc-ca.com/reports/dr-potential-residential-appliancesrefrigerator-%0Alg

Southern California Edison. (2013). Demand Response Potential of Residential Appliances: Dishwasher (Report No. DR10SCE1.16.03), (December). Retrieved from http://www.etcc-ca.com/reports/demand-response-potential-residentialappliances-%0Adishwasher

Stokes, M. (2005). Removing barriers to embedded generation : load a model to support low voltage network performance analysis network performance analysis. Sustainable Development, 447.

Thiaux, Y., Dang, T. T., Schmerber, L., Multon, B., Ben Ahmed, H., Bacha, S., & Tran, Q. T. (2019). Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior. Applied Energy, 253, 113530. https://doi.org/10.1016/J.APENERGY.2019.113530

Toogoshi, M., Kano, S. S., & Zempo, Y. (2015). The Maximum Entropy Method for Optical Spectrum Analysis of Real-Time TDDFT. Journal of Physics: Conference Series, 640(1). https://doi.org/10.1088/1742- 6596/640/1/012069

Trujillo, C. L., Santamaría, F., Hernández, J. A., Jaramillo, A. A., Gaona, E. E., Rivas, E., … Rojas, H. E. (2015). Microrredes eléctricas. Microrredes Eléctricas (Vol. Primera Ed). Bogotá D.C.: Universidad Distrital Francisco José de Caldas.

Trujillo, C., & Santamaría, F. (2017). PERS Cundinamarca: Diagnóstico energético del departamento de Cundinamarca. Bogotá, Colombia.

Ueno, T., Inada, R., Saeki, O., & Tsuji, K. (2006). Effectiveness of an energy-consumption information system for residential buildings. Applied Energy, 83(8), 868–883. https://doi.org/https://doi.org/10.1016/j.apenergy.2005.09.004

Ueno, T., Tsuji, K., & Nakano, Y. (2003). Effectiveness of Displaying Energy Consumption Data in Residential Buildings: To Know Is to Change. ECEEE Summer Study on Energy Efficiency in Buildings, (Ueno 2003), 264–277. Retrieved from https://energytaxincentives.org/files/proceedings/2006/data/papers/SS06_Panel7_Paper22.pdf

UPME. (2008). Guía para la Implementación Sistema de Gestión Integral de la Energía.

UPME. (2016). Plan de Acción Indicativo de Eficiencia Energética 2017-2022. Ministerio de Minas y Energía, 1–157. Retrieved from http:// www1.upme.gov.co/DemandaEnergetica/MarcoNormatividad/PAI_PROURE_2017-2022.pdf

Vallés Rodriguez, M., Frías Marín, P., Reneses Guillén, J., & González Sotres, L. (2013). Gestión Activa de la Demanda para una Europa más eficiente. Anales De Mecánica Y Electricidad, (2009), 55–61.

Vega, A. M. (2018). Gestión de la Energía Eléctrica Domiciliaria con base en la gestión activa de la demanda. Retrieved from https://doctoradoingenieria.udistrital.edu.co/index.php/es/inicio/documentos/repositorio-de-tesis-doctoral/item/510-energia-electrica-domiciliaria-con-base-en-la-gestion-activa

Vega, A., Santamaria, F., & Rivas, E. (2015). Modeling for home electric energy management : A review. Renewable and Sustainable Energy Reviews, 52, 948–959. https://doi.org/10.1016/j.rser.2015.07.023

Vega, A., Santamaría, F., & Rivas, E. (2018). Supervision, monitoring and control of home appliances through power line communication. In 2018 IEEE PES Transmission&Distribution Conference and Exhibition - Latin America (T&D-LA) (pp. 1–5). https://doi.org/10.1109/TDC-LA.2018.8511636

Villa, D., Martin, C., Villanueva, F. J., Moya, F., & Lopez, J. C. (2011). A dynamically reconfigurable architecture for smart grids. IEEE Transactions on Consumer Electronics, 57(2), 411–419. https://doi.org/10.1109/ TCE.2011.5955174

Vladimirovna, O., & Gutiérrez González, E. (2014). Probabilidad y Estadística: Aplicaciones a la Ingeniería y Ciencias. (G. E. Patria, Ed.).

Wade, N. S., Taylor, P. C., Lang, P. D., & Jones, P. R. (2010). Evaluating the benefits of an electrical energy storage system in a future smart grid. EnergyPolicy,38(11),7180–7188.https://doi.org/10.1016/j.enpol.2010.07.045

"Wang, Y., & Li, L. (2016). Critical peak electricity pricing for sustainable

manufacturing: Modeling and case studies. Applied Energy, 175, 40–53. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.04.100"

Wilson, Caroline, & Marselle, M. R. (2016). Insights from psychology about the design and implementation of energy interventions using the Behaviour Change Wheel. Energy Research & Social Science, 19, 177–191.

Wilson, Charlie, Hargreaves, T., & Hauxwell-Baldwin, R. (2017). Benefits and risks of smart home technologies. Energy Policy, 103(January), 72–83. https://doi.org/10.1016/j.enpol.2016.12.047

Wood, G., & Newborough, M. (2003). Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design. Energy and Buildings, 35(8), 821–841. https://doi.org/https://doi. org/10.1016/S0378-7788(02)00241-4

Yao, R., & Steemers, K. (2005). A method of formulating energy load profile for domestic buildings in the UK. Energy and Buildings, 37(6), 663– 671. https://doi.org/10.1016/J.ENBUILD.2004.09.007

Yousef, E Hamouda, M. (2019). Optimal home energy management for smart home using random bit climbing. Computer and Information Sciences.

Yu, Z., McLaughlin, L., Jia, L., Murphy-Hoye, M. C., Pratt, A., & Tong, L. (2012). Modeling and stochastic control for home energy management. IEEE Power and Energy Society General Meeting, (July). https://doi. org/10.1109/PESGM.2012.6345471

Yue, T., Long, R., & Chen, H. (2013). Factors influencing energy-saving behavior of urban households in Jiangsu Province. Energy Policy, 62, 665– 675. https://doi.org/https://doi.org/10.1016/j.enpol.2013.07.051

Zaeri, M., Sharda, N., & Zahedi, A. (2014). A five layer model for simulating a virtual power plant. IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 435–440. https://doi.org/10.1109/ISGT-Asia.2014.6873831

Zhang, Y., Zhang, T., Wang, R., Liu, Y., & Guo, B. (2015). Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts. Solar Energy, 122, 1052–1065.

Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., & Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61, 30–40. https://doi.org/10.1016/j.rser.2016.03.047

Zhu, N., Bai, X., & Meng, J. (2011). Benefits Analysis of All Parties Participating in Demand Response. 2011 Asia-Pacific Power and Energy Engineering Conference.