Julian Perez Garcia

Personal Details

First Name:	Julian
Middle Name:
Last Name:	Perez Garcia
Suffix:	J
RePEc Short-ID:	ppr28
	[This author has chosen not to make the email address public]
	Instituto "L.R.Klein"- Centro Stone Facultad de CC.EE. y EE. Módulo E-XIV. Universidad Autónoma de Madrid Campus Cantoblanco. MADRID 28049 SPAIN
	34914973942

Affiliation

(50%) Centro de Predicción Económica
Facultad de Ciencias Económicas y Empresariales
Universidad Autónoma de Madrid

Madrid, Spain
http://www.ceprede.com/
RePEc:edi:cpuames (more details at EDIRC)

(50%) Instituto de Predicción Económica Lawrence R. Klein
Facultad de Ciencias Económicas y Empresariales
Universidad Autónoma de Madrid

Madrid, Spain
http://www.uam.es/otroscentros/klein/
RePEc:edi:ipuames (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Pérez García, Julián & Moral Carcedo, Julián, 2015. "Analysis and long term forecasting of electricity demand through a decomposition model: A case study for Spain," Working Papers in Economic Theory 2015/07, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
   • Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.

   Cited by:

   1. Deguo Su & Beibei Tan & Anbing Zhang & Yikai Hou, 2023. "Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
   2. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
   3. David I. Okorie, 2021. "A network analysis of electricity demand and the cryptocurrency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3093-3108, April.
   4. Nunes, Juliana Barbosa & Mahmoudi, Nadali & Saha, Tapan Kumar & Chattopadhyay, Debabrata, 2018. "A stochastic integrated planning of electricity and natural gas networks for Queensland, Australia considering high renewable penetration," Energy, Elsevier, vol. 153(C), pages 539-553.
   5. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
   6. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
   7. Jinchai Lin & Kaiwei Zhu & Zhen Liu & Jenny Lieu & Xianchun Tan, 2019. "Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation," Energies, MDPI, vol. 12(11), pages 1-28, June.
   8. García-Gusano, Diego & Suárez-Botero, Jasson & Dufour, Javier, 2018. "Long-term modelling and assessment of the energy-economy decoupling in Spain," Energy, Elsevier, vol. 151(C), pages 455-466.
   9. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
   10. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
   11. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
   12. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
   13. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
   14. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
   15. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
   16. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
   17. Ribó-Pérez, David & Van der Weijde, Adriaan H. & Álvarez-Bel, Carlos, 2019. "Effects of self-generation in imperfectly competitive electricity markets: The case of Spain," Energy Policy, Elsevier, vol. 133(C).
   18. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
   19. Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
   20. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
   21. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
   22. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
   23. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
   24. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
   25. da Silva, Felipe L.C. & Cyrino Oliveira, Fernando L. & Souza, Reinaldo C., 2019. "A bottom-up bayesian extension for long term electricity consumption forecasting," Energy, Elsevier, vol. 167(C), pages 198-210.
   26. Pedro J. Zarco-Periñán & Irene M. Zarco-Soto & Fco. Javier Zarco-Soto, 2021. "Influence of the Population Density of Cities on Energy Consumption of Their Households," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
   27. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.

2. Moral Carcedo, Julián & Pérez García, Julián, 2015. "Temperature Effects on Firms’ Electricity Demand: An Analysis of Sectorial Differences in Spain," Working Papers in Economic Theory 2015/01, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
   • Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.

   Cited by:

   1. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
   2. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
   3. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
   4. Tang, Wenliang & Yang, Mian & Duan, Hongbo, 2023. "Temperature and corporate tax avoidance: Evidence from Chinese manufacturing firms," Energy Economics, Elsevier, vol. 117(C).
   5. Lanlan Li & Xinpei Song & Jingjing Li & Ke Li & Jianling Jiao, 2023. "The impacts of temperature on residential electricity consumption in Anhui, China: does the electricity price matter?," Climatic Change, Springer, vol. 176(3), pages 1-26, March.
   6. Manh-Hung Nguyen, 2021. "A Resilient Energy System to Climate change," Post-Print hal-04044554, HAL.
   7. Anukoolthamchote, Pam Chasuta & Assané, Djeto & Konan, Denise Eby, 2020. "Net electricity load profiles: Shape and variability considering customer-mix at transformers on the island of Oahu, Hawai'i," Energy Policy, Elsevier, vol. 147(C).
   8. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
   9. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego F., 2018. "Effect of stopping hydroelectric power generation on the dynamics of electricity prices: An event study approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 456-467.
   10. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
   11. Mukherjee, Sayanti & Vineeth, C.R. & Nateghi, Roshanak, 2019. "Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework," Applied Energy, Elsevier, vol. 235(C), pages 1561-1582.
   12. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.

3. Gallego López, Nuria & Llano, Carlos & Pérez García, Julian, 2010. "Estimación de los Flujos de Transporte de Mercancías Interregionales Trimestrales mediante Técnicas de Interpolación Temporal," Working Papers in Economic Theory 2010/03, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).

   Cited by:

Articles

1. Moral-Carcedo, Julián & Pérez-García, Julián, 2019. "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, vol. 174(C), pages 169-183.

   Cited by:

   1. José Rubio-León & José Rubio-Cienfuegos & Cristian Vidal-Silva & Jesennia Cárdenas-Cobo & Vannessa Duarte, 2023. "Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
   2. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
   3. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
   4. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
   5. Miguel López & Sergio Valero & Carlos Sans & Carolina Senabre, 2020. "Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy," Energies, MDPI, vol. 14(1), pages 1-14, December.
   6. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
   7. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
   8. Derumigny Alexis & Fermanian Jean-David, 2019. "On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior," Dependence Modeling, De Gruyter, vol. 7(1), pages 292-321, January.

2. Carlos Llano & Santiago Pérez-Balsalobre & Julian Pérez-García, 2018. "Greenhouse Gas Emissions from Intra-National Freight Transport: Measurement and Scenarios for Greater Sustainability in Spain," Sustainability, MDPI, vol. 10(7), pages 1-33, July.

   Cited by:

   1. Đurđica Stojanović & Jelena Ivetić & Marko Veličković, 2021. "Assessment of International Trade-Related Transport CO 2 Emissions—A Logistics Responsibility Perspective," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
   2. Izabela Kotowska & Marta Mańkowska & Michał Pluciński, 2018. "Inland Shipping to Serve the Hinterland: The Challenge for Seaport Authorities," Sustainability, MDPI, vol. 10(10), pages 1-17, September.
   3. Melinda Timea Fülöp & Miklós Gubán & György Kovács & Mihály Avornicului, 2021. "Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity," Energies, MDPI, vol. 14(10), pages 1-22, May.
   4. Vit Malinovsky, 2022. "Neural Networks as an Alternative Tool for Predicting Fossil Fuel Dependency and GHG Production in Transport," Sustainability, MDPI, vol. 14(18), pages 1-12, September.

3. Julián Pérez-García & Julián Moral-Carcedo, 2017. "Why Electricity Demand Is Highly Income-Elastic in Spain: A Cross-Country Comparison Based on an Index-Decomposition Analysis," Energies, MDPI, vol. 10(3), pages 1-20, March.

   Cited by:

   1. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
   2. Monika Sipa & Iwona Gorzeń-Mitka, 2021. "Assessment of the Progress towards the Management of Renewable Energy Consumption in the Innovativeness Context—A Country Approach," Energies, MDPI, vol. 14(16), pages 1-21, August.

4. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.

   Cited by:

   1. Jannik Schütz Roungkvist & Peter Enevoldsen & George Xydis, 2020. "High-Resolution Electricity Spot Price Forecast for the Danish Power Market," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
   2. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
   3. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
   4. Swasti R. Khuntia & Jose L. Rueda & Mart A.M.M. Van der Meijden, 2018. "Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model," Energies, MDPI, vol. 11(12), pages 1-19, November.
   5. Kalhori, M. Rostam Niakan & Emami, I. Taheri & Fallahi, F. & Tabarzadi, M., 2022. "A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting," Applied Energy, Elsevier, vol. 314(C).
   6. Moral-Carcedo, Julián & Pérez-García, Julián, 2019. "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, vol. 174(C), pages 169-183.
   7. Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
   8. Fu, Xin & Zeng, Xiao-Jun & Feng, Pengpeng & Cai, Xiuwen, 2018. "Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China," Energy, Elsevier, vol. 165(PB), pages 76-89.
   9. Chapaloglou, Spyridon & Nesiadis, Athanasios & Iliadis, Petros & Atsonios, Konstantinos & Nikolopoulos, Nikos & Grammelis, Panagiotis & Yiakopoulos, Christos & Antoniadis, Ioannis & Kakaras, Emmanuel, 2019. "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, Elsevier, vol. 238(C), pages 627-642.
   10. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
   11. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).

5. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
   • Pérez García, Julián & Moral Carcedo, Julián, 2015. "Analysis and long term forecasting of electricity demand through a decomposition model: A case study for Spain," Working Papers in Economic Theory 2015/07, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
   See citations under working paper version above.
6. Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.
   • Moral Carcedo, Julián & Pérez García, Julián, 2015. "Temperature Effects on Firms’ Electricity Demand: An Analysis of Sectorial Differences in Spain," Working Papers in Economic Theory 2015/01, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
   See citations under working paper version above.
7. Carlos Llano Verduras & Almudena Esteban de la Fuente & Julián Pérez García & Antonio Pulido San Román, 2008. "La base de datos C-intereg sobre el comercio interregional de bienes en España: método y primeros resultados (1995-2006)," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, vol. 69(03), pages 244-269.

   Cited by:

   1. Miguel A Márquez & Julian Ramajo & Geoffrey Hewings, 2017. "Regional Public Stock Reductions in Spain: Estimations from a Multiregional Spatial Vector Autorregressive Model," REGION, European Regional Science Association, vol. 4, pages 129-146.
   2. Julian Ramajo & Miguel A. Marquez & Geoffrey J.D. Hewings, 2013. "Spatio-temporal Analysis of Regional Systems: A Multiregional Spatial Vector Autoregressive Model for Spain," ERSA conference papers ersa13p159, European Regional Science Association.
   3. Julián Ramajo & Miguel A. Márquez & Geoffrey J. D. Hewings, 2017. "Spatiotemporal Analysis of Regional Systems," International Regional Science Review, , vol. 40(1), pages 75-96, January.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ENE: Energy Economics (1) 2015-02-11

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