Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants

Abstract

Solar photovoltaic (PV) energy has emerged as a potential alternative to carbon-based energies to meet the Paris agreement commitment. This study investigates the effect of environmental variables on the efficiency of solar PV panels. Data Envelopment Analysis (DEA) is used to estimate efficiencies of 91 solar PV panels located in Australia during the time period 2010–2020. The effects of environmental variables on the estimated efficiencies are quantified using the truncated regression model. Random forest is then used to predict efficiency of solar PV panel in every city of Australia. The results allow to determine the most suitable location and regions for solar PV energy production in Australia. This study provides an interesting and easily interpretable tool for policy decision makers.

Keywords

• Solar energy
• Location selection
• Australia
• Data envelopment analysis
• Random forest