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Juan Jose Flores

Not to be confused with: Juan Huitzilihuitl Flores Zendejas

Personal Details

First Name:Juan
Middle Name:Jose
Last Name:Flores
Suffix:
RePEc Short-ID:pfl32
[This author has chosen not to make the email address public]
http://dep.fie.umich.mx/~juan/
Terminal Degree:1997 (from RePEc Genealogy)

Affiliation

University of Oregon, Computer Science Department


https://scds.uoregon.edu/cs
Eugene, Oregon, USA
5413463487

Research output

as
Jump to: Articles Chapters

Articles

  1. Baldwin Cortés & Roberto Tapia & Juan J. Flores, 2021. "System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles," Energies, MDPI, vol. 14(16), pages 1-18, August.
  2. Jose R. Cedeño González & Juan J. Flores & Claudio R. Fuerte-Esquivel & Boris A. Moreno-Alcaide, 2020. "Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-24, October.
  3. Juan. J. Flores & José R. Cedeño González & Héctor Rodríguez & Mario Graff & Rodrigo Lopez-Farias & Felix Calderon, 2019. "Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison," Energies, MDPI, vol. 12(18), pages 1-19, September.
  4. Rodrigo Lopez Farias & Vicenç Puig & Hector Rodriguez Rangel & Juan J. Flores, 2018. "Multi-Model Prediction for Demand Forecast in Water Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.
  5. Santoyo Federico GONZÁLEZ & Romero Beatriz FLORES & Ana María GIL LAFUENTE & Juan FLORES J., 2017. "Fuzzy Logic in the Design of Public Policies: Application of Law," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(2), pages 281-290.
  6. Carlos Lara-Alvarez & Juan J. Flores & Chieh-Chih Wang, 2015. "Detecting the Boundary of Sensor Networks from Limited Cyclic Information," International Journal of Distributed Sensor Networks, , vol. 11(7), pages 401838-4018, July.
  7. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
  8. Norberto Hernandez-Romero & Juan Carlos Seck-Tuoh-Mora & Manuel Gonzalez-Hernandez & Joselito Medina-Marin & Juan Jose Flores-Romero, 2010. "Modeling A Nonlinear Liquid Level System By Cellular Neural Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 489-501.
  9. González Santoyo, Federico & Flores Romero, Beatriz & Chagolla Farías, Mauricio & Flores, Juan J., 2004. "Uncertainty Theory Applied to Optimal Selection of Personnel in an Enterprise," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 75-92, November.
  10. Flores, Juan & González, Federico & Flores, Beatriz, 2001. "Qualitative/Quantitative Financial Analysis," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 75-86, November.

Chapters

  1. González S. Federico & Flores R. Beatriz & Anna Maria Gil-Lafuente & Flores Juan, 2015. "Uncertain Optimal Inventory as a Strategy for Enterprise Global Positioning," Lecture Notes in Economics and Mathematical Systems, in: Anna Maria Gil-Lafuente & Constantin Zopounidis (ed.), Decision Making and Knowledge Decision Support Systems, edition 127, pages 29-42, Springer.
  2. F. González Santoyo & J. Flores Romero & B. Flores Romero & J. Mendoza Ramírez, 2001. "Multiple Fuzzy Irr In The Financial Decision Environment," World Scientific Book Chapters, in: Constantin Zopounidis & Panos M Pardalos & George Baourakis (ed.), Fuzzy Sets In Management, Economics And Marketing, chapter 15, pages 223-237, World Scientific Publishing Co. Pte. Ltd..

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.

Articles

  1. Baldwin Cortés & Roberto Tapia & Juan J. Flores, 2021. "System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles," Energies, MDPI, vol. 14(16), pages 1-18, August.

    Cited by:

    1. Bryam Paúl Lojano-Riera & Carlos Flores-Vázquez & Juan-Carlos Cobos-Torres & David Vallejo-Ramírez & Daniel Icaza, 2023. "Electromobility with Photovoltaic Generation in an Andean City," Energies, MDPI, vol. 16(15), pages 1-16, July.
    2. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.

  2. Rodrigo Lopez Farias & Vicenç Puig & Hector Rodriguez Rangel & Juan J. Flores, 2018. "Multi-Model Prediction for Demand Forecast in Water Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.

    Cited by:

    1. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    2. Jindamas Sutthichaimethee & Kuskana Kubaha, 2018. "Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    3. Pauline Macharia & Nzula Kitaka & Paul Yillia & Norbert Kreuzinger, 2021. "Assessing Future Water Demand and Associated Energy Input with Plausible Scenarios for Water Service Providers (WSPs) in Sub-Saharan Africa," Energies, MDPI, vol. 14(8), pages 1-22, April.
    4. Kang-Min Koo & Kuk-Heon Han & Kyung-Soo Jun & Gyumin Lee & Jung-Sik Kim & Kyung-Taek Yum, 2021. "Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea," Sustainability, MDPI, vol. 13(11), pages 1-18, May.

  3. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.

    Cited by:

    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    3. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    4. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    5. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    6. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    7. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    8. Renbo Liu & Yuhui Ge & Peng Zuo, 2023. "Study on Economic Data Forecasting Based on Hybrid Intelligent Model of Artificial Neural Network Optimized by Harris Hawks Optimization," Mathematics, MDPI, vol. 11(21), pages 1-28, November.
    9. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    10. Abdoulaye Camara & Wang Feixing & Liu Xiuqin, 2016. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(5), pages 231-231, April.
    11. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
    12. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.

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