On the Usage of Artificial Neural Networks for the Determination of Optimal Wind Farms Allocation

Abstract

Worldwide energy demand is constantly increasing. This fact, in combination with the ever growing need to reduce the energy production footprint on the environment, has led to the adoption of cleaner and more sustainable forms of energy production. Renewable Energy Sources (RES) are constantly developing in an effort to increase their conversion efficiency and improve their life cycle. However, not all types of RES are accepted by the general public. Wind Turbines (WTs) are considered by many researchers as the least acceptable type of RES. This is mostly because of how their installation alters the surrounding landscape, produces noise and puts birds in danger when they happen to fly over the installation area. This paper aims to apply a methodology which, by using Rational Basis Function Neural Networks (RBFNN), is capable of investigating the criteria used for the installation locations of WTs in a transparent way. The results from the Neural Network (NN) will be combined with protected areas and the Land Fragmentation Index (LFI), in order to determine possible new installation locations with increased social acceptance and, at the same time, increased energy production. A case study of the proposed methodology has been implemented for the entire Greek territory, which is considered one of the most suitable areas for the installation of wind farms due to its particular geomorphology.

Winds of Change: A Study on the Resource Viability of Offshore Wind Energy in Montenegro

“…An integral step in the MCDM process involves determining the weighting criteria for each layer. Xentidis et al (2022) enumerate various techniques for criterion determination, including the Analytic Hierarchy Process, Weighted Linear Combination, Simple Additive Weighting, Fuzzy Analytic Hierarchy Process, and Best Worst Method [38].…”