A Machine Learning Approach for Investment Analysis in Renewable Energy Sources: A Case Study in Photovoltaic Farms

Abstract

Farmland offers excellent conditions for developing solar energy while farmers seem to appreciate its notable revenues. The increasing adoption of photovoltaics (PVs) on farmland raises various concerns with the most important being the loss of productive farmland and the increased farmland prices, which may prevent young farmers from entering the farming occupation. The latter can threaten the future of agriculture in countries that are already facing the problem of rural population ageing. The aim of this paper is to examine the effect of crop type on farmers’ willingness to install photovoltaics on their farmland. To that end, this study applies four machine learning (ML) algorithms (categorical regression, decision trees and random forests, support vector machines) on a dataset obtained from a questionnaire survey on farmers in a Greek agricultural area. The results from the application of the algorithms allowed us to quantify and relate farmers’ willingness to invest in PVs with three major crop types (cotton, wheat, sunflower) which play a very important role in food security. Results also provide support for making policy interventions by defining the rate of productive farmland for photovoltaics and also for designing policies to support farmers to start and maintain farming operations.