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

Sustainability

Dimitrios Myronidis¹  Konstantinos Ioannou² 

[1] Department of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Hellenic Agricultural Organization “DEMETER”, Forest Research Institute, Vasilika, 57006 Thessaloniki, Greece;

关键词: PV farms;    deep learning;    satellite imagery;    CNN;    automatic detection;   

DOI  :  10.3390/su13095323

来源: DOAJ

【 摘 要 】

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 However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production).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.

【 授权许可】

Unknown