A small wind turbine output model for spatially constrained remote island miicro-grids

• Autorzy: Žigman D. , Meštrović K. , Tomiša T.

Identyfikatory

DOI

10.12716/1001.16.01.16

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2022
• Tom: Vol. 16 No. 1
• Strony: 143--146
• Opis fizyczny: Bibliogr. 5 poz., rys., tab.

Twórcy

autor

Žigman D.

• Zagreb University of Applied Sciences, Zagreb, Croatia

autor

Meštrović K.

• Zagreb University of Applied Sciences, Zagreb, Croatia

autor

Tomiša T.

• University of Zagreb, Zagreb, Croatia

Bibliografia

• 1. Acevedo, M.F.: Introduction to Renewable Power Systems and the Environment with R. CRC Press (2018). https://doi.org/10.1201/b21919.
• 2. Department of Energy: Small Wind Guidebook, https://windexchange.energy.gov/small-wind-guidebook.
• 3. Efron, B., Hastie, T.: Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press (2016).
• 4. Kuhn, M., Johnson, K.: Feature Engineering and Selection: A Practical Approach for Predictive Models. Chapman and Hall/CRC (2019).
• 5. Sotavento: Experimental Ecological Park Sotavento Real Time Data Archive, http://www.sotaventogalicia.com/en/technical-area/real-time-data/historical/, last accessed 2021/03/30.