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Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island

Author

Listed:
  • Marcelo Moya

    (Technology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, Ecuador)

  • Javier Martínez-Gómez

    (Technology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, Ecuador
    Facultad de Ingeniería y Ciencias Aplicadas, Universidad Internacional SEK, Quito 170302, Ecuador
    Department of Signal Theory and Communication, Mechanical Engineering Area, Universidad de Alcalá, 28801 Madrid, Spain)

  • Esteban Urresta

    (Technology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, Ecuador)

  • Martín Cordovez-Dammer

    (Technology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, Ecuador)

Abstract

Maritime passenger transport in the Galapagos Islands–Itabaca Channel is based on boats with combustion engines that consume an annual average of 4200 gallons of fuel and produce about 38 tons of CO 2 per year. The operation of the solar catamaran “INER 1” electric propulsion (PV) is a renewable and sustainable model for passenger shipping in the Galapagos Islands. In this regard, the detailed study of the relationship between the variability of solar radiation, the abrupt change of tides due to changes in wind speed and direction, and the increase in tourists, according to dry and wet seasons, cause high energy consumption. The boats must absorb energy from the electrical grid of the islands; this energy is from renewable (solar and wind) and, mostly, of fossil origin so identifying the source of the energy absorbed by the boats is essential. The aim of this study was to select the most influential attributes in the operation of the solar catamaran “INER 1” in the Galapagos Islands. The methodology for knowledge discovery in the databases was determined by selecting attributes that combine environmental, social, and energy variables affecting the energy performance of the solar catamaran. The energy consumption of the boats features a direct relationship with the attributes defined in this research as: (1) Energ (energy used), (2) Tur (tourists and residents), (3) Fotov (PV park), (4) Glrad (global radiation), (5) date (date and time), (6) Term9 (thermo-electric 9). Considering the six best attributes filtered by the proposed algorithms, 4.95% in the mean squared error parameter and a 98.94% accuracy in the classification and prediction of the energy consumed by boats were obtained.

Suggested Citation

  • Marcelo Moya & Javier Martínez-Gómez & Esteban Urresta & Martín Cordovez-Dammer, 2022. "Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island," Energies, MDPI, vol. 15(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2761-:d:790124
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    References listed on IDEAS

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    1. Ling-Chin, J. & Heidrich, O. & Roskilly, A.P., 2016. "Life cycle assessment (LCA) – from analysing methodology development to introducing an LCA framework for marine photovoltaic (PV) systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 352-378.
    2. Jurado, Sergio & Nebot, Àngela & Mugica, Fransisco & Avellana, Narcís, 2015. "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Elsevier, vol. 86(C), pages 276-291.
    3. Touati, Farid & Chowdhury, Noor Alam & Benhmed, Kamel & San Pedro Gonzales, Antonio J.R. & Al-Hitmi, Mohammed A. & Benammar, Mohieddine & Gastli, Adel & Ben-Brahim, Lazhar, 2017. "Long-term performance analysis and power prediction of PV technology in the State of Qatar," Renewable Energy, Elsevier, vol. 113(C), pages 952-965.
    4. Hatwaambo, Sylvester & Jain, Prem C. & Perers, Bengt & Karlsson, Bjorn, 2009. "Projected beam irradiation at low latitudes using Meteonorm database," Renewable Energy, Elsevier, vol. 34(5), pages 1394-1398.
    5. Lucas, Alexandre & Neto, Rui Costa & Silva, Carla Alexandra, 2013. "Energy supply infrastructure LCA model for electric and hydrogen transportation systems," Energy, Elsevier, vol. 56(C), pages 70-80.
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