Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Konstantinos Ioannou,Dimitrios Myronidis

doi:10.3390/su13095323

Konstantinos Ioannou, Dimitrios Myronidis

Open Access

https://doi.org/10.3390/su13095323

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Journal: Sustainability	Publication Date: May 10, 2021
Citations: 9	License type: CC BY 4.0

Affiliation: Aristotle University of Thessaloniki

Abstract

The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.

Highlights

During the last three decades mankind is witnessing an evolution in the energy sector as we notice a shift in energy production methods, from the usage of fossil fuels to more environmentally friendly methods
Image recognition can provide a valuable tool for monitoring the adaption rate of renewable energy sources
Modern deep learning methods are unaware of the processing data and can be used in order to recognize the various forms of renewable energy sources (RES)

Summary

Introduction

During the last three decades mankind is witnessing an evolution in the energy sector as we notice a shift in energy production methods, from the usage of fossil fuels (petroleum, natural gas, coal, etc.) to more environmentally friendly methods. Renewable energy methods can be considered as a viable solution for energy production and the reduction of CO2 emissions These methods include the usage of sustainable sources based on wind, water, biomass, solar and geothermal energy for energy production which are in general called renewable energy sources (RES) [8]. Massive arrays of PV panels (in the form of solar or PV farms) are used for energy production throughout the world. These farms energy production capability ranges from 1 to 2000 MW, in the case of mega projects covering thousands of hectares [10]

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