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

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<sub>2</sub> per year. The operation of the solar catamaran “INER 1” electric propulsion (...

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Main Authors: Marcelo Moya, Javier Martínez-Gómez, Esteban Urresta, Martín Cordovez-Dammer
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/15/8/2761
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author Marcelo Moya
Javier Martínez-Gómez
Esteban Urresta
Martín Cordovez-Dammer
author_facet Marcelo Moya
Javier Martínez-Gómez
Esteban Urresta
Martín Cordovez-Dammer
author_sort Marcelo Moya
collection DOAJ
description 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<sub>2</sub> 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.
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spelling doaj.art-8c3d058f2ba549209ec598f6e78973ee2023-12-01T20:48:30ZengMDPI AGEnergies1996-10732022-04-01158276110.3390/en15082761Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos IslandMarcelo Moya0Javier Martínez-Gómez1Esteban Urresta2Martín Cordovez-Dammer3Technology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, EcuadorTechnology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, EcuadorTechnology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, EcuadorTechnology Transfer and Incubation Department, Instituto de Investigación Geológico y Energético (IIGE), Quito 170518, EcuadorMaritime 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<sub>2</sub> 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.https://www.mdpi.com/1996-1073/15/8/2761Galapagos Islandsbiodiversitysolar photovoltaicsustainable shippingalgorithmartificial intelligence
spellingShingle Marcelo Moya
Javier Martínez-Gómez
Esteban Urresta
Martín Cordovez-Dammer
Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
Energies
Galapagos Islands
biodiversity
solar photovoltaic
sustainable shipping
algorithm
artificial intelligence
title Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
title_full Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
title_fullStr Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
title_full_unstemmed Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
title_short Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island
title_sort feature selection in energy consumption of solar catamaran iner 1 on galapagos island
topic Galapagos Islands
biodiversity
solar photovoltaic
sustainable shipping
algorithm
artificial intelligence
url https://www.mdpi.com/1996-1073/15/8/2761
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