A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, Richard G. Newell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

47 Scopus citations

Abstract

Power generation from distributed solar photovoltaic PV arrays has grown rapidly in recent years. As a result, there is interest in collecting information about the quantity, power capacity, and energy generated by such arrays; and to do so over small geo-spatial regions (e.g., counties, cities, or even smaller regions). Unfortunately, existing sources of such information are dispersed, limited in geospatial resolution, and otherwise incomplete or publically unavailable. As result, we recently proposed a new approach for collecting such distributed PV information that relies on computer algorithms to automatically detect PV arrays in high resolution aerial imagery [1], Here we build on this work by investigating two machine learning algorithms for PV array detection: a Random Forest classifier (RF) [2] and a deep convolutional neural network (CNN) [3]. We use the RF algorithm as a benchmark, or baseline, for comparison with a CNN model. The two models are developed and tested using a large collection of publicly available [4] aerial imagery, covering 135 km2, and including over 2,700 manually annotated distributed PV array locations. The results indicate that the CNN substantially improves over the RF. The CNN is capable of excellent performance, detecting nearly 80% of true panels with a precision measure of 72%.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages650-654
Number of pages5
ISBN (Electronic)9781509033881
DOIs
StatePublished - 2016
Event5th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016 - Birmingham, United Kingdom
Duration: Nov 20 2016Nov 23 2016

Publication series

Name2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016

Conference

Conference5th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
Country/TerritoryUnited Kingdom
CityBirmingham
Period11/20/1611/23/16

Keywords

  • Convolutional neural networks
  • Deep learning
  • Detection
  • Energy
  • Photovoltaic
  • Solar

Fingerprint

Dive into the research topics of 'A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery'. Together they form a unique fingerprint.

Cite this