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 CO2 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.

Automated summary

This study selects the most influential attributes in the operation of the solar catamaran INER 1 in the Galapagos Islands, including energy consumption, number of tourists and residents, PV park status, global radiation, date and time, and thermo-electric conditions. After filtering with the proposed algorithm, high accuracy and prediction of energy consumption were obtained.