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Frank Anthony Monforte

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Personal Details

First Name:Frank
Middle Name:Anthony
Last Name:Monforte
Suffix:
RePEc Short-ID:pmo356
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Affiliation

Itron, Inc.


http://www.itron.com/
USA, San Diego, California

Research output

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Jump to: Articles

Articles

  1. Monforte, Frank A., 2002. "Predictive Modular Neural Networks - Applications to Time Series," International Journal of Forecasting, Elsevier, vol. 18(1), pages 157-158.
  2. J. Stuart McMenamin & Frank A. Monforte, 1998. "Short Term Energy Forecasting with Neural Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 43-61.
  3. Stuart McMenamin, J. & Monforte, Frank A. & Sioshansi, Fereidoon P., 1997. "Environmental benefits of electrification and end-use efficiency," The Electricity Journal, Elsevier, vol. 10(4), pages 26-33, May.
  4. Joseph B. Creegan & Frank A. Monforte, 1990. "Southern California Gas Company Uses Special Ordered Sets to Model Regulatory Guidelines," Interfaces, INFORMS, vol. 20(4), pages 28-42, August.

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. J. Stuart McMenamin & Frank A. Monforte, 1998. "Short Term Energy Forecasting with Neural Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 43-61.

    Cited by:

    1. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    2. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    3. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    4. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    5. Nima Amjady & Farshid Keynia, 2011. "A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems," Energies, MDPI, vol. 4(3), pages 1-16, March.
    6. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.

  2. Stuart McMenamin, J. & Monforte, Frank A. & Sioshansi, Fereidoon P., 1997. "Environmental benefits of electrification and end-use efficiency," The Electricity Journal, Elsevier, vol. 10(4), pages 26-33, May.

    Cited by:

    1. Wang, Yongpei & Yan, Qing & Yang, Jieru & Komonpipat, Supak & Zhang, Qian, 2024. "Can inter-provincial transmission reduce regional carbon emissions? Evidence from China," Energy Policy, Elsevier, vol. 184(C).

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