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dc.contributor.advisorSepúlveda Cano, Lina Maria
dc.contributor.advisorGallego Burgos, Ricardo Alonso
dc.contributor.authorMazzeo, Agustín José
dc.coverage.spatialLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.date.accessioned2021-04-20T18:35:55Z
dc.date.available2021-04-20T18:35:55Z
dc.date.created2019-09-17
dc.identifier.otherCD-ROM 9035 2019
dc.identifier.urihttp://hdl.handle.net/11407/6340
dc.descriptionEl presente trabajo de investigación revisa los modelos desarrollados para el pronóstico de consumos y precios de energía eléctrica, las variables utilizadas y las métricas de error para medir la precisión de dichos modelos. Tiene como objetivo proponer un nuevo modelo de pronóstico que tenga el menor error posible en su predicción para la variable eléctrica precio marginal local para el mercado eléctrico mexicano. Este modelo se utilizaría como base para que, en un futuro cercano, MVM Ingeniería de Software S.A.S. desarrolle el módulo o producto a incorporar en plataforma Energy Suite que le permita adquirir capacidades analíticas.
dc.description.abstract"The following research work analyses the models developed for the forecast of consumption and prices of electric power, the variables used and the error measures that are used to calculate the accuracy of these models in order to elaborate a new forecast model with the smallest possible error in its prediction for the local marginal price electrical variable for the Mexican power market. This model will become the foundation over where \MVM Ingeniería de Software S.A.S."", in the near future, will develop the module or product to be incorporated in the Energy Suite platform allowing it to acquire analytical capabilities."
dc.format.extentp. 1-147
dc.format.mediumElectrónico
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0
dc.subjectModelos de Previsión
dc.subjectPrecio Marginal Local
dc.subjectCorto plazo
dc.subjectMercado Eléctrico Mayorista
dc.subjectMéxico
dc.titleDiseño de un modelo de pronóstico para la mejora de las ofertas comerciales en el mercado eléctrico mayorista de México : caso MVM
dc.rights.accessrightsinfo:eurepo/semantics/openAccess
dc.publisher.programMaestría en Ingeniería de Software
dc.subject.lembComercio mayorista
dc.subject.lembEnergía eléctrica - Precios
dc.subject.lembIngeniería de software - Estudio de casos
dc.subject.lembPronóstico de la economía
dc.subject.lembSector eléctrico - México
dc.subject.keywordForecast models
dc.subject.keywordLocal Marginal Price
dc.subject.keywordShort-Term
dc.subject.keywordWholesale Energy Market
dc.subject.keywordMexico
dc.relation.citationstartpage1
dc.relation.citationendpage147
dc.audienceComunidad Universidad de Medellín
dc.publisher.facultyFacultad de Ingenierías
dc.publisher.placeMedellín
dc.relation.references[1] F. J. Ardakani and M. M. Ardehali, \Long-term electrical energy consumption forecasting for developing and developed economies based on dierent optimized modelsand historical data types," Energy, vol. 65, pp. 452-461, 2014.spa
dc.relation.references[2] R. Weron, \Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, vol. 30(4), p. 1030-1081, 2014.spa
dc.relation.references[3] G. R. T. Esteves, B. Q. Bastos, F. L. Cyrino, R. F. Calili, and R. C. Souza, \Long term electricity forecast: A systematic review," Procedia Computer Science, vol. 55, p. 549-558, 2015.spa
dc.relation.references[4] T. Hong and S. Fan, \Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, vol. 32(3), p. 914-938, 2016.spa
dc.relation.references[5] J. Nowotarski and R.Weron, \Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, vol. 81(Part1), p. 1548-1568, 2018.spa
dc.relation.references[6] C. Kuster, Y. Rezgui, and M. Mourshed, \Electrical load forecasting models: A critical systematic review," Sustainable Cities and Society, vol. 35, p. 257-270, 2017.spa
dc.relation.references[7] P. L. Joskow, \Introduction to electricity sector liberalization: Lessons learned from cross-country studies," Electricity Market Reform, pp. 1-32, 2006.spa
dc.relation.references[8] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2013.spa
dc.relation.references[9] J. Esquivel Zubiri, \Archivo:Mercado eléctrico reforma energética." https://es.wikipedia.org/wiki/Archivo:Mercado_el\%C3\%A9ctrico_reforma_energ\%C3\%A9tica.jpg, 2014. [Consultado el 05/05/2018].spa
dc.relation.references[10] SIE, \Glosario de términos de electricidad." http://sie.energia.gob.mx/docs/glosario_elec_es.pdf. [Consultado el 05/05/2018].spa
dc.relation.references[11] J. Esquivel Zubiri, \Ley de la industria eléctrica." http://www.diputados.gob.mx/LeyesBiblio/pdf/LIElec_110814.pdf, 2014. [Consultado el 14/02/2019].spa
dc.relation.references[12] CENACE, \Cat alogo de NodosP." https://www.cenace.gob.mx/Paginas/Publicas/MercadoOperacion/NodosP.aspx. [Consultado el 05/05/2018].spa
dc.relation.references[13] C. Deb, F. Zhang, J. Yang, S. E. Lee, and K. W. Shah, \A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, vol. 74, pp. 902-924, 2017.spa
dc.relation.references[14] R. Weron, Modeling and forecasting electricity loads and prices: a statistical approach. Chichester: Wiley, 2006.spa
dc.relation.references[15] D. Tran_eld, D. Denyer, and P. Smart, \Towards a methodology for developing evidence-informed management knowledge by means of systematic review," British Journal of Management, vol. 14, pp. 207-222, 2003.spa
dc.relation.references[16] D. Denyer and D. Tran_eld, Producing a systematic review. Sage Publications, 2009.spa
dc.relation.references[17] A. Hernandez Neto and F. Sanzovo Fiorelli, \Comparison between detailed model simulation and arti_cial neural network for forecasting building energy consumption," Energy and Buildings, vol. 40(12), pp. 2169-2176, 2008.spa
dc.relation.references[18] U. Kumar and V. K. Jain, \Time series models (grey-markov, grey model with Rolling mechanism and singular spectrum analysis) to forecast energy consumption in india," Energy, vol. 35(4), pp. 1709-1716, 2010.spa
dc.relation.references[19] A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, \Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors," Energy Conversion and Management, vol. 49, pp. 0196-8904, 2008.spa
dc.relation.references[20] R. K. Jain, K. M. Smith, P. J. Culligan, and J. E. Taylor, \Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, vol. 123, pp. 168-178, 2014.spa
dc.relation.references[21] C. Hamzacebi, \Forecasting of turkey's net electricity energy consumption on sectoral bases," Energy Policy, vol. 35(3), pp. 2009-2016, 2007.spa
dc.relation.references[22] Y.-S. Lee and L.-I. Tong, \Forecasting energy consumption using a grey model improved by incorporating genetic programming," Energy Conversion and Management, vol. 52(1), pp. 147-152, 2011.spa
dc.relation.references[23] S. Saab, E. Badr, and G. Nasr, \Univariate modeling and forecasting of energy onsumption: the case of electricity in lebanon," Energy, vol. 26(1), pp. 1-14, 2001.spa
dc.relation.references[24] A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, \A simulated-based neural network algorithm for forecasting electrical energy consumption in iran," Energy Policy, vol. 36(7), pp. 2637-2644, 2008.spa
dc.relation.references[25] K. Li, H. Su, and J. Chu, \Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study," Energy and Buildings, vol. 43(10), pp. 2893-2899, 2011.spa
dc.relation.references[26] L. Tang, L. Yu, S. Wang, J. Li, and S. Wang, \A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, vol. 93, pp. 432-443, 2012.spa
dc.relation.references[27] A. Azadeh and S. Tarverdian, \Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, vol. 35(10), pp. 5229-5241, 2007.spa
dc.relation.references[28] A. Z. Al-Garni, S. M. Zubair, and J. S. Nizami, \A regression model for electric-energyconsumption forecasting in eastern saudi arabia," Energy, vol. 19(10), pp. 1043-1049, 1994.spa
dc.relation.references[29] X. Lü, T. Lu, C. J. Kibert, and M. Viljanen, \Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach," Applied Energy, vol. 144, pp. 261-275, 2015.spa
dc.relation.references[30] A. Sözen, M. A. Ak_cayol, and E. Arcaklio_glu, \Forecasting net energy consumption using arti_cial neural network," Energy Sources, vol. 1(2), pp. 147-155, 2006.spa
dc.relation.references[31] L. Tang, L. Yu, and K. He, \A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, vol. 128, pp. 1-14, 2014.spa
dc.relation.references[32] G. E. Nasr, E. A. Badr, and M. R. Younes, \Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches," Energy Research, vol. 26(1), pp. 67-78, 2002.spa
dc.relation.references[33] K. Li and H. Su, \Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system," Energy and Buildings, vol. 42(11), pp. 2070-2076, 2010.spa
dc.relation.references[34] F. Zhang, C. Deb, S. E. Lee, J. Yang, and K. W. Shah, \Time series forecasting for building energy consumption using weighted support vector regression with di_erential evolution optimization technique," Energy and Buildings, vol. 126, pp. 94-103, 2016.spa
dc.relation.references[35] K. Karabulut, A. Alkan, and A. Yilmaz, \Long term energy consumption forecasting using genetic programming," Association for Scienti_c Research, vol. 13, pp. 71-80, 2008.spa
dc.relation.references[36] P. K. Adom and W. Bekoe, \Conditional dynamic forecast of electrical energy consumption requirements in ghana by 2020: A comparison of ardl and pam," Energy, vol. 44(1), pp. 367-380, 2012.spa
dc.relation.references[37] D. Baczynski and M. Parol, \Inuence of arti_cial neural network structure on quality of short-term electric energy consumption forecast," Generation, Transmission and Distribution, IEE Proceedings, vol. 151, pp. 241-245, 2004.spa
dc.relation.references[38] S. Barak and S. S. Sadegh, \Forecasting energy consumption using ensemble arimaan_s hybrid algorithm," International Journal of Electrical Power & Energy Systems, vol. 82, pp. 92-104, 2016.spa
dc.relation.references[39] M. Meng, D. Niu, and W. Sun, \Forecasting monthly electric energy consumption using feature extraction," Energies, vol. 4(10), pp. 1495-1507, 2011.spa
dc.relation.references[40] P. Sen, M. Roy, and P. Pal, \Application of arima for forecasting energy consumption and ghg emission: A case study of an indian pig iron manufacturing organization," Energy, vol. 116(Part 1), pp. 1031-1038, 2016.spa
dc.relation.references[41] Y. R. Zeng, Y. Zeng, B. Choi, and L. Wang, \Multifactor-inuenced energy consumption forecasting using enhanced back-propagation neural network," Energy, vol. 127,pp. 381-396, 2017.spa
dc.relation.references[42] L. Tang, S.Wang, K. He, and S.Wang, \A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, vol. 234, pp. 111-132, 2015.spa
dc.relation.references[43] V. A. Kamaev, M. V. Shcherbakov, D. P. Panchenko, N. L. Shcherbakova, and A. Brebels,\Using connectionist systems for electric energy consumption forecasting in shopping centers," Automation and Remote Control, vol. 73(6), pp. 1075-1084, 2012.spa
dc.relation.references[44] P. Zhang and H. Wang, \Fuzzy wavelet neural networks for city electric energy consumption forecasting," Energy Procedia, vol. 17(B), pp. 1332-1338, 2012.spa
dc.relation.references[45] M. Castelli, L. Trujillo, and L. Vanneschi, \Energy consumption forecasting using semantic-based genetic programming with local search optimizer," Computational Intelligence and Neuroscience, vol. 2015, p. 8, 2015.spa
dc.relation.references[46] X. Jiang, H. Ling, J. Yan, B. Li, and Z. Li, \Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization," Mathematical Problems in Engineering, vol. 2013, p. 8, 2013.spa
dc.relation.references[47] J. Yoo and K. Hur, \Load forecast model switching scheme for improved robustnessto changes in building energy consumption patterns," Energies, vol. 6, pp. 1329-1343, 2013.spa
dc.relation.references[48] X. P. Zhang and G. Rui, \Electrical energy consumption forecasting based on cointegration and a support vector machine in china," WSEAS Transactions on Mathematics, vol. 6(6), pp. 878-883, 2007.spa
dc.relation.references[49] L. G. B. Ruiz, R. Rueda, M. P. Cu_ellar, and M. C. Pegalajar, \Energy consumption forecasting based on elman neural networks with evolutive optimization," Expert Systems with Applications, vol. 92, pp. 380-389, 2018.spa
dc.relation.references[50] K. P. Amber, M. W. Aslam, A. Mahmood, A. Kousar, M. Y. Younis, B. Akbar, G. Q. Chaudhary, and S. K. Hussain, \Energy consumption forecasting for university sector buildings," Energies, vol. 10(10), p. 1579, 2017.spa
dc.relation.references[51] S. Singh and A. Yassine, \Big data mining of energy time series for behavioral analytics and energy consumption forecasting," Energies, vol. 11(2), p. 452, 2018.spa
dc.relation.references[52] R. D. Wang, X. Sun, X. Yang, and H. Hu, \Cloud computing and extreme learning machine for a distributed energy consumption forecasting in equipment-manufacturing enterprises," Cybernetics and Information Technologies, vol. 16(6), pp. 83-97, 2016.spa
dc.relation.references[53] Y. Zhang, R. Yang, K. Zhang, H. Jiang, and J. J. Zhang, \Consumption behavior analytics-aided energy forecasting and dispatch," In IEEE Intelligent Systems, vol. 32(4), pp. 59-63, 2017.spa
dc.relation.references[54] M. Petkovic, M. R. Rapai_c, and B. Jakovljevicll, \Electrical energy consumption forecastingin oil re_ning industry using support vector machines and particle swarm optimization,"WSEAS Transactions on Information Science and Applications, vol. 6(11), pp. 1761-1770, 2009.spa
dc.relation.references[55] V. Majazi Dalfard, M. Nazari Asli, S. Nazari-Shirkouhi, S. M. Sajadi, and S. M. Asadzadeh,\Incorporating the e_ects of hike in energy prices into energy consumptionforecasting: a fuzzy expert system," Neural Computing and Applications, vol. 23(1), pp. 153-169, 2013.spa
dc.relation.references[56] M. Meng, W. Shang, and D. Niu, \Monthly electric energy consumption forecasting using multiwindow moving average and hybrid growth models," Journal of Applied Mathematics, vol. 2014, pp. 1-7, 2014.spa
dc.relation.references[57] J. L. Rojas-Renteria, T. D. Espinoza-Huerta, F. S. Tovar-Pacheco, J. L. Gonzalez-Perez, and R. Lozano-Dorantes, \An electrical energy consumption monitoring and forecasting system," Engineering, Technology & Applied Science Research, vol. 6(5), pp. 1130-1132, 2016.spa
dc.relation.references[58] L. Wang, H. Hu, X. Y. Ai, and H. Liu, \E_ective electricity energy consumption forecasting using echo state network improved by di_erential evolution algorithm,"Energy, vol. 153, pp. 801-815, 2018.spa
dc.relation.references[59] E. Meira de Oliveira and F. L. Cyrino Oliveira, \Forecasting mid-long term electric energy consumption through bagging arima and exponential smoothing methods,"Energy, vol. 144, pp. 776-788, 2018.spa
dc.relation.references[60] M. H. Alobaidi, F. Chebana, and M. A. Meguid, \Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, vol. 212, pp. 997-1012, 2018.spa
dc.relation.references[61] S. Mishra and V. K. Singh, \Monthly energy consumption forecasting based on windowed momentum neural network," IFAC-PapersOnLine, vol. 48(30), pp. 433-438, 2015.spa
dc.relation.references[62] B. A. Staroverov and B. A. Gnatyuk, \Universal energy consumption forecasting system based on neural network ensemble," Optical Memory and Neural Networks, vol. 25(3), pp. 198-202, 2016.spa
dc.relation.references[63] L. Cao, \Support vector machines experts for time series forecasting," Neurocomputing, vol. 51, pp. 321-339, 2003.spa
dc.relation.references[64] B. J. Chen, M. W. Chang, and C. J. Lin, \Load forecasting using support vector machines: a study on eunite competition 2001," IEEE Transactions on Power Systems, vol. 19(4), pp. 1821-1830, 2004.spa
dc.relation.references[65] B. Dong, C. Cao, and S. Lee, \Applying support vector machines to predict building energy consumption in tropical region," Energy and Buildings, vol. 37(5), pp. 545-553, 2004.spa
dc.relation.references[66] P. F. Pai and W. C. Hong, \Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms," Electric Power Systems Research, vol. 74(3), pp. 417-425, 2005.spa
dc.relation.references[67] P. F. Pai and W. C. Hong, \Support vector machines with simulated annealing algorithms in electricity load forecasting," Energy Conversion and Management, vol. 46(17), pp. 2669-2688, 2005.spa
dc.relation.references[68] C. C. Hsu and C. Y. Chen, \Applications of improved grey prediction model for power demand forecasting," Energy Conversion and Management, vol. 44(14), pp. 2241-2249, 2002.spa
dc.relation.references[69] J. W. Taylor, L. M. de Menezes, and P. E. McSharry, \A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forescasting, vol. 22(1), pp. 1-16, 2006.spa
dc.relation.references[70] D. Akay and M. Atak, \Grey prediction with rolling mechanism for electricity demand forecasting of turkey," Energy, vol. 32(9), pp. 1670-1675, 2006.spa
dc.relation.references[71] W. R. Christiaanse, \Short-term load forecasting using general exponential smoothing,"IEEE Transactions on Power Apparatus and Systems, vol. PAS-90(2), pp. 900-911, 1971.spa
dc.relation.references[72] W. C. Hong, \Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model," Energy Conversion and Management, vol. 50(1), pp. 105-117, 2009.spa
dc.relation.references[73] P. A. Gonz_alez and J. M. Zamarre~no, \Prediction of hourly energy consumption in buildings based on a feedback arti_cial neural network," Energy and Buildings, vol. 37(6), pp. 595-601, 2005.spa
dc.relation.references[74] L. Ekonomou, \Greek long-term energy consumption prediction using arti_cial neural networks," Energy, vol. 35(2), pp. 512-517, 2009.spa
dc.relation.references[75] A. Sorjamaa, J. Hao, N. Reyhani, Y. Ji, and A. Lendasse, \Methodology for long-term prediction of time series," Neurocomputing, vol. 70(16-18), pp. 2861-2869, 2007.spa
dc.relation.references[76] D. Niu, Y. Wang, and D. D. Wu, \Power load forecasting using support vector machine and ant colony optimization," Expert Systems with Applications, vol. 37(3), pp. 2531-2539, 2010.spa
dc.relation.references[77] Z. A. Bashir and M. E. El-Hawary, \Applying wavelets to short-term load forecasting using pso-based neural networks," IEEE Transactions on Power Systems, vol. 24(1), pp. 20-27, 2009.spa
dc.relation.references[78] S. Karatasou, M. Santamouris, and V. Geros, \Modeling and predicting building's energy use with arti_cial neural networks: methods and results," Energy and Buildings, vol. 38(8), pp. 949-958, 2005.spa
dc.relation.references[79] A. Azadeh, S. F. Ghaderi, S. Tarverdian, and M. Saberi, \Integration of arti_cial neural networks and genetic algorithm to predict electrical energy consumption," Applied Mathematics and Computation, vol. 186(2), pp. 1731-1741, 2007.spa
dc.relation.references[80] Q. Li, Q. Meng, J. Cai, H. Yoshino, and M. A., \Applying support vector machine to predict hourly cooling load in the building," Applied Energy, vol. 86(10), pp. 2249-2256, 2009.spa
dc.relation.references[81] E. Erdogdu, \Electricity demand analysis using cointegration and arima modelling: a case study of turkey," Energy Policy, vol. 35(2), pp. 1129-1146, 2006.spa
dc.relation.references[82] W. C. Hong, \Electric load forecasting by support vector model," Applied Mathematical Modelling, vol. 33(5), pp. 2444-2454, 2008.spa
dc.relation.references[83] J. W. Taylor, \Triple seasonal methods for short-term electricity demand forecasting,"European Journal of Operational Research, vol. 204(1), pp. 139-152, 2010.spa
dc.relation.references[84] P. Zhou, B. Ang, and K. Poh, \A trigonometric grey prediction approach to forecasting electricity demand," Energy, vol. 31(14), pp. 2839-2847, 2005.spa
dc.relation.references[85] A. Kavousi-Fard, H. Samet, and F. Marzbani, \A new hybrid modi_ed _rey algorithm and support vector regression model for accurate short term load forecasting," Expert Systems with Applications, vol. 41(13), pp. 6047-6056, 2014.spa
dc.relation.references[86] H. T. Pao, H. C. Fu, and C. L. Tseng, \Forecasting of co2 emissions, energy consumption and economic growth in china using an improved grey model," Energy, vol. 40(1), pp. 400-409, 2012.spa
dc.relation.references[87] C. M. Huang, C. J. Huang, and M. L. Wang, \A particle swarm optimization to identifying the armax model for short-term load forecasting," IEEE Transactions on Power Systems, vol. 20(2), pp. 1126-1133, 2005.spa
dc.relation.references[88] Q. Li, Q. Meng, J. Cai, H. Yoshino, and M. A., \Predicting hourly cooling load in the building: a comparison of support vector machine and di_erent arti_cial neural networks," Energy Conversion and Management, vol. 50(1), pp. 90-96, 2008.spa
dc.relation.references[89] A. W. L. Yao, S. C. Chi, and J. H. Chen, \An improved grey-based approach for electricity demand forecasting," Electric Power Systems Research, vol. 67(3), pp. 217-224, 2003.spa
dc.relation.references[90] W. C. Hong, \Electric load forecasting by seasonal recurrent svr (support vector regression) with chaotic arti_cial bee colony algorithm," Energy, vol. 36(9), pp. 5568-5578,2011.spa
dc.relation.references[91] J. W. Taylor, \An evaluation of methods for very short-term load forecasting using minute-by-minute british data," International Journal of Forecasting, vol. 24(4), pp. 645-658, 2008.spa
dc.relation.references[92] C.-M. Lee and C.-N. Ko, \Time series prediction using rbf neural networks with a nonlinear time-varying evolution pso algorithm," Neurocomputing, vol. 73(1-3), pp. 449-460,2009.spa
dc.relation.references[93] J. W. Taylor, \Short-term load forecasting with exponentially weighted methods," IEEE Transactions on Power Systems, vol. 27(1), pp. 458-464, 2012.spa
dc.relation.references[94] R. E. Edwards, J. New, and L. E. Parker, \Predicting future hourly residential electrical consumption: a machine learning case study," Energy and Buildings, vol. 49, pp. 591-603, 2012.spa
dc.relation.references[95] S. Bahrami, R.-A. Hooshmand, and M. Parastegari, \Short term electric load forecasting by wavelet transform and grey model improved by pso (particle swarm optimization) algorithm," Energy, vol. 72, pp. 434-442, 2014.spa
dc.relation.references[96] F. Kaytez, M. C. Taplamacioglu, E. Cam, and F. Hardalac, \Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines," Energy Systems, vol. 67, pp. 431-438, 2014.spa
dc.relation.references[97] C. Hamzacebi and H. A. Es, \Forecasting the annual electricity consumption of turkey using an optimized grey model," Energy, vol. 70, pp. 165-171, 2014.spa
dc.relation.references[98] S. R. Singh, \A simple method of forecasting based on fuzzy time series," Applied Mathematics and Computation, vol. 186(1), pp. 330-339, 2007.spa
dc.relation.references[99] H. W.-C., Y. Dong, W. Y. Zhang, L.-Y. Chen, and B. K. Panigrahi, \Cyclic electric load forecasting by seasonal svr with chaotic genetic algorithm," Energy Systems, vol. 44(1), pp. 604-614, 2013.spa
dc.relation.references[100] R. E. Abdel-Aal and A. Z. Al-Garni, \Forecasting monthly electric energy consumption in eastern saudi arabia using univariate time-series analysis," Energy, vol. 22(11), pp. 1059-1069, 1997.spa
dc.relation.references[101] P.-C. Chang and J.-J. Fan C.-Y.and Lin, \Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach," International Journal of Electrical Power & Energy Systems, vol. 33(1), pp. 17-27, 2011.spa
dc.relation.references[102] A. Azadeh, S. M., S. F. Ghaderi, A. Gitiforouz, and V. Ebrahimipour, \Improved estimation of electricity demand function by integration of fuzzy system and data mining approach," Energy Conversion and Management, vol. 49(8), pp. 2165-2177, 2008.spa
dc.relation.references[103] R. Yokoyama, T. Wakui, and R. Satake, \Prediction of energy demands using neural network with model identi_cation by global optimization," Energy Conversion and Management, vol. 50(2), pp. 319-327, 2008.spa
dc.relation.references[104] H. Mao, X.-J. Zeng, G. Leng, Y. Zhai, and J. A. Keane, \Short and mid-term load forecasting using a bilevel optimization model," IEEE Transactions on Power Systems, vol. 24(2), pp. 1080-1090, 2009.spa
dc.relation.references[105] E. Ceperic, V. Ceperic, S. Member, and A. Baric, \A strategy for short-term load forecasting by support vector regression machines," IEEE Transactions on Power Systems, vol. 28(4), pp. 4356-4364, 2013.spa
dc.relation.references[106] H.-T. Yang and C.-M. Huang, \A new short-term load forecasting approach using selforganizing fuzzy armax models," IEEE Transactions on Power Systems, vol. 13(1), pp. 217-225, 1998.spa
dc.relation.references[107] Y. Wang, J. Wang, G. Zhao, and Y. Dong, \Application of residual modi_cation approach in seasonal arima for electricity demand forecasting: a case study of china," Energy Policy, vol. 48, pp. 284-294, 2012.spa
dc.relation.references[108] W.-C. Hong, \Hybrid evolutionary algorithms in a svr-based electric load forecasting model," International Journal of Electrical Power & Energy Systems, vol. 31(7-8), pp. 409-417, 2007.spa
dc.relation.references[109] A. Azadeh, M. Saberi, and O. Seraj, \An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of iran," Energy, vol. 35(6), pp. 2351-2366, 2010.spa
dc.relation.references[110] E. Almeshaiei and H. Soltan, \A methodology for electric power load forecasting,"Alexandria Engineering Journal, vol. 50(2), pp. 137-144, 2011.spa
dc.relation.references[111] V. Bianco, O. Manca, S. Nardini, and M. A. A., \Analysis and forecasting of nonresidential electricity consumption in romania," Applied Energy, vol. 87(11), pp. 3584- 3590, 2010.spa
dc.relation.references[112] A. W. L. Yao and S. C. Chi, \Analysis and design of a taguchi-grey based electricity demand predictor for energy management systems," Energy Conversion and Management, vol. 45(7-8), pp. 1205-1217, 2004.spa
dc.relation.references[113] R.-A. Hooshmand, H. Amooshahi, and M. Parastegari, \A hybrid intelligent algorithm based short-term load forecasting approach," International Journal of Electrical Power & Energy Systems, vol. 45(1), pp. 313-324, 2013.spa
dc.relation.references[114] H.-T. Pao, \Comparing linear and nonlinear forecasts for taiwan's electricity consumption," Energy, vol. 31(12), pp. 2129-2141, 2004.spa
dc.relation.references[115] J. Che, J. Wang, and G. Wang, \An adaptive fuzzy combination model based on selforganizing map and support vector regression for electric load forecasting," Energy, vol. 37(1), pp. 657-664, 2012.spa
dc.relation.references[116] Y. Yao, Z. Lian, S. Liu, and Z. Hou, \Hourly cooling load prediction by a combined forecasting model based on analytic hierarchy process," International Journal of Thermal Sciences, vol. 43(11), pp. 1107-1118, 2004.spa
dc.relation.references[117] B. Wang, N. Tai, H. Zhai, Y. J., J. Zhu, and L. Qi, \A new armax model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting," Electric Power Systems Research, vol. 78(10), pp. 1679-1685, 2008.spa
dc.relation.references[118] Y. T. Chae, R. Horesh, Y. Hwang, and Y. M. Lee, \Arti_cial neural network model for forecasting sub-hourly electricity usage in commercial buildings," Energy and Buildings, vol. 111, pp. 184-194, 2015.spa
dc.relation.references[119] J. Donate, X. Li, G. Guti_errez S_anchez, and A. Sanchis de Miguel, \Time series forecasting by evolving arti_cial neural networks with genetic algorithms, di_erential evolution and estimation of distribution algorithm," Neural Computing and Applications, vol. 22(1), pp. 11-20, 2011.spa
dc.relation.references[120] H. Nie, G. Liu, X. Liu, and Y. Wang, \Hybrid of arima and svms for short-term load forecasting," Energy Procedia, vol. 16(Part C), pp. 1455-1460, 2012.spa
dc.relation.references[121] C. Deb, L. S. Eang, J. Yang, and M. Santamouris, \Forecasting diurnal cooling energy load for institutional buildings using arti_cial neural networks," Energy and Buildings, vol. 121, pp. 284-297, 2015.spa
dc.relation.references[122] Y. Jiang, Y. Yao, S. Deng, and Z. Ma, \Applying grey forecasting to predicting the operating energy performance of air cooled water chillers," International Journal of Refrigeration, vol. 27(4), pp. 385-392, 2004.spa
dc.relation.references[123] I. P. Panapakidis and A. S. Dagoumas, \Day-ahead electricity price forecasting via the application of arti_cial neural network based models," Applied Energy, vol. 172, pp. 132-151, 2016.spa
dc.relation.references[124] A predictive demand of the maximum electric power using chaos-fuzzy, vol. 2, IEEE, 2001.spa
dc.relation.references[125] P. Li, Y. Li, Q. Xiong, Y. Chai, and Y. Zhang, \Application of a hybrid quantized elman neural network in short-term load forecasting," International Journal of Electrical Power & Energy Systems, vol. 55, pp. 749-759, 2014.spa
dc.relation.references[126] D. K. Chaturvedi, A. P. Sinha, and O. P. Malik, \Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network," International Journal of Electrical Power & Energy Systems, vol. 67, pp. 230-237, 2015.spa
dc.relation.references[127] Q. Zhou, S.Wang, X. Xu, and F. Xiao, \A grey-box model of next-day building termal load prediction for energy-e_cient control," International Journal of Energy Research, vol. 32(15), pp. 1418-1431, 2008.spa
dc.relation.references[128] K. Kandananond, \Forecasting electricity demand in thailand with an arti_cial neural network approach," Energies, vol. 4(8), pp. 1246-1257, 2011.spa
dc.relation.references[129] G.-D. Li, C.-H. Wang, S. Masuda, and M. Nagai, \A research on short term load forecasting problem applying improved grey dynamic model," International Journal of Electrical Power & Energy Systems, vol. 33(4), pp. 809-816, 2011.spa
dc.relation.references[130] G.-F. Fan, L.-L. Peng, W.-C. Hong, and F. Sun, \Electric load forecasting by the svr model with di_erential empirical mode decomposition and auto regression," Neurocomputing, vol. 173(Part 3), pp. 958-970, 2016.spa
dc.relation.references[131] A. Azadeh, M. Saberi, A. Gitiforouz, and Z. Saberi, \A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation," Expert Systems with Applications, vol. 36(8), pp. 11108-11117, 2009.spa
dc.relation.references[132] K. Li, C. Hu, G. Liu, and W. Xue, \Building's electricity consumption prediction using optimized arti_cial neural networks and principal component analysis," Energy and Buildings, vol. 108, pp. 106-113, 2015.spa
dc.relation.references[133] W.-J. Lee and J. Hong, \A hybrid dynamic and fuzzy time series model for mid-term power load forecasting," International Journal of Electrical Power & Energy Systems, vol. 64, pp. 1057-1062, 2015.spa
dc.relation.references[134] H. Chitsaz, H. Shaker, H. Zareipour, D. Wood, and N. Amjady, \Short-term electricity load forecasting of buildings in microgrids," Energy and Buildings, vol. 99, pp. 50-60,2015.spa
dc.relation.references[135] M. Jin, X. Zhou, Z. M. Zhang, and M. M. Tentzeris, \Short-term power load forecasting using grey correlation contest modeling," Expert Systems with Applications, vol. 39(1), pp. 773-779, 2012.spa
dc.relation.references[136] R. Enayatifar, H. J. Sadaei, A. H. Abdullah, and A. Gani, \Imperialist competitive algorithm combined with re_ned high-order weighted fuzzy time series (rhwfts-ica) for short term load forecasting," Energy Conversion and Management, vol. 76, pp. 1104-1116, 2013.spa
dc.relation.references[137] G. Sudheer and A. Suseelatha, \Short term load forecasting using wavelet transform combined with holt-winters and weighted nearest neighbor models," International Journal of Electrical Power & Energy Systems, vol. 64, pp. 340-346, 2015.spa
dc.relation.references[138] W. Y. Zhang, W.-C. Hong, Y. Dong, G. Tsai, J.-T. Sung, and G. Fan, \Application of svr with chaotic gasa algorithm in cyclic electric load forecasting," Energy, vol. 45(1), pp. 850-858, 2012.spa
dc.relation.references[139] F.-Y. Ju and W.-C. Hong, \Application of seasonal svr with chaotic gravitational search algorithm in electricity forecasting," Applied Mathematical Modelling, vol. 37(23), pp. 9643-9651, 2013.spa
dc.relation.references[140] T. Chen and Y. Wang, \Long-term load forecasting by a collaborative fuzzy-neural approach," International Journal of Electrical Power & Energy Systems, vol. 43(1), pp. 454-464, 2012.spa
dc.relation.references[141] S. Kouhi and F. Keynia, \A new cascade nn based method to short-term load forecast in deregulated electricity market," Energy Conversion and Management, vol. 71, pp. 76-83, 2013.spa
dc.relation.references[142] J. Wu, J. Wang, H. Lu, Y. Dong, and X. Lu, \Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model," Energy Conversion and Management, vol. 70, pp. 1-9, 2013.spa
dc.relation.references[143] M. Ghofrani, M. Ghayekhloo, A. Arabali, and A. Ghayekhloo, \A hybrid short-term load forecasting with a new input selection framework," Energy, vol. 81, pp. 777-786, 2015.spa
dc.relation.references[144] S. Kouhi, F. Keynia, and S. Naja_ Ravadanegh, \A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection," International Journal of Electrical Power & Energy Systems, vol. 62, pp. 862-867, 2014.spa
dc.relation.references[145] J. Nizami and A. Z. Ai-Garni, \Forecasting electric energy consumption using neural networks," Energy Policy, vol. 23(12), pp. 1097-1104, 1995.spa
dc.relation.references[146] T. Nengling, J. Stenzel, and W. Hongxiao, \Techniques of applying wavelet transform into combined model for short-term load forecasting," Electric Power Systems Research, vol. 76(6-7), pp. 525-533, 2006.spa
dc.relation.references[147] H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, \Short-term load forecasting using a hybrid model with a re_ned exponentially weighted fuzzy time series and an improved harmony search," International Journal of Electrical Power & Energy Systems, vol. 62, pp. 118-129, 2014.spa
dc.relation.references[148] M. Moazzami, A. Khodabakhshian, and R. Hooshmand, \A new hybrid day-ahead peak load forecasting method for iran's national grid," Applied Energy, vol. 101, pp. 489-501, 2013.spa
dc.relation.references[149] T.-T. Chen and S.-J. Lee, \A weighted ls-svm based learning system for time series forecasting," Information Sciences, vol. 299, pp. 99-116, 2015.spa
dc.relation.references[150] B. Akdemir and N. C_ etinkay, \Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data," Energy Procedia, vol. 14, pp. 794-799,2012.spa
dc.relation.references[151] Y. Chen, Y. Yang, C. Liu, C. Li, and L. Li, \A hybrid application algorithm based on the support vector machine and arti_cial intelligence: an example of electric load forecasting," Applied Mathematical Modelling, vol. 39(9), pp. 2617-2632, 2015.spa
dc.relation.references[152] R. Efendi, Z. Ismail, and M. Mat, \A new linguistic out-sample approach of fuzzy time series for daily forecasting of malaysian electricity load demand," Applied Soft Computing, vol. 28, pp. 422-430, 2014.spa
dc.relation.references[153] Time series analysis of household electric consumption with ARIMA and ARMA models, 2013.spa
dc.relation.references[154] T. E. Dikaios, \Forecasting residential electricity consumption in greece using monthly and quarterly data," Energy Economics, vol. 14(3), pp. 226-232, 1992.spa
dc.relation.references[155] Fuzzy based time series forecasting of electric load, IEEE, 1999.spa
dc.relation.references[156] S. Kelo and S. Dudul, \A wavelet elman neural network for short-term electrical load prediction under the inuence of temperature," International Journal of Electrical Power & Energy Systems, vol. 43(1), pp. 1063-1071, 2012.spa
dc.relation.references[157] K. M. El-naggar and K. A. Al-rumaih, \Algorithm, optimal _lter estimator and least error squares technique: comparative study," Int'l J Electr Robot Electron Commun Eng, vol. 1, pp. 941-945, 2006.spa
dc.relation.references[158] M. Brown, C. Barrington-Leigh, and Z. Brown, \Kernel regression for real-time building energy analysis," Journal of Building Performance Simulation, vol. 5(4), pp. 263-276, 2011.spa
dc.relation.references[159] Fuzzy based time series forecasting of electric load, IEEE, 2011.spa
dc.relation.references[160] D. Monfet, M. Corsi, D. Choini_ere, and A. E., \Development of an energy prediction tool for commercial buildings using case-based reasoning," Energy and Buildings, vol. 81, pp. 152-160, 2014.spa
dc.relation.references[161] M. Rezaeian-Zadeh, S. Zand-Parsa, H. Abghari, M. Zolghadr, and V. P. Singh, \Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions," Theoretical and Applied Climatology, vol. 109(3-4), pp. 519-528, 2012.spa
dc.relation.references[162] X. Wang and M. Meng, \A hybrid neural network and arima model for energy consumption forcasting," Journal of Computers, vol. 7(5), pp. 1184-1190, 2012.spa
dc.relation.references[163] Y. Fu, Z. Li, H. Zhang, and P. Xu, \Using support vector machine to predict next day electricity load of public buildings with sub-metering devices," Procedia Engineering, vol. 121, pp. 1016-1022, 2015.spa
dc.relation.references[164] S. R. Rallapalli and S. Ghosh, \Forecasting monthly peak demand of electricity in india|a critique," Energy Policy, vol. 45, pp. 516-520, 2012.spa
dc.relation.references[165] H. Son and C. Kim, \Forecasting short-term electricity demand in residential sector based on support vector regression and fuzzy-rough feature selection with particle swarm optimization," Procedia Engineering, vol. 118, pp. 1162-1168, 2015.spa
dc.relation.references[166] Multivariate k-nearest neighbour regression for time series data | a novel algorithm for forecasting UK electricity demand, IEEE, 2013.spa
dc.relation.references[167] Building cooling load B21forecasting model based on LS-SVM, vol. 1, IEEE, 2009.spa
dc.relation.references[168] C. Deb, L. S. Eang, J. Yang, and M. Santamouris, \Forecasting energy consumption of institutional buildings in singapore," Procedia Engineering, vol. 121, pp. 1734-1740, 2015.spa
dc.relation.references[169] M. Bo_zi_c, M. Stojanovi, and Z. Stajic, \Short-term electric load forecasting using least square support vector machspa
dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis de Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.description.degreenameMagíster en Ingeniería de Software
dc.description.degreelevelMaestría
dc.publisher.grantorUniversidad de Medellín


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