@article{Žigman_MeÅAtrovic_TomiÅAa_2022,
	author = {Žigman, Dubravko and MeÅAtrovic, KreÅAimir and TomiÅAa, Tomislav},
	title = {A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {16},
	number = {1},
	pages = {143-146},
	year = {2022},
	url = {./Article_A_Small_Wind_Turbine_Output_Model_Žigman,61,1206.html},
	abstract = {Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.},
	doi = {10.12716/1001.16.01.16},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Wind Turbine, Small Wind Turbine, Decision Tree Model, Artificial Neural Network Model, Random Forest Model, Micro-Grids, Spatially Constrained Remote Island Micro-Grids, Remote Island Micro-Grid}
}