Capacity Estimation of Solar Farms Using Deep Learning on High-Resolution Satellite Imagery

Abstract

Global solar photovoltaic capacity has consistently doubled every 18 months over the last two decades, going from 0.3 GW in 2000 to 643 GW in 2019, and is forecast to reach 4240 GW by 2040. However, these numbers are uncertain, and virtually all reporting on deployments lacks a unified source of either information or validation. In this paper, we propose, optimize, and validate a deep learning framework to detect and map solar farms using a state-of-the-art semantic segmentation convolutional neural network applied to satellite imagery. As a final step in the pipeline, we propose a model to estimate the energy generation capacity of the detected solar energy facilities. Objectively, the deep learning model achieved highly competitive performance indicators, including a mean accuracy of 96.87%, and a Jaccard Index (intersection over union of classified pixels) score of 95.5%. Subjectively, it was found to detect spaces between panels producing a segmentation output at a sub-farm level that was better than human labeling. Finally, the detected areas and predicted generation capacities were validated against publicly available data to within an average error of 4.5% Deep learning applied specifically for the detection and mapping of solar farms is an active area of research, and this deep learning capacity evaluation pipeline is one of the first of its kind. We also share an original dataset of overhead solar farm satellite imagery comprising 23,000 images (256 × 256 pixels each), and the corresponding labels upon which the machine learning model was trained.

Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images

“…For instance, data sets such as thermal images and their respective segmentation masks could be expanded with new synthetic data, especially when expertise in the field of photovoltaic farm fault detection is needed [26]. Furthermore, this can also be applied to the improvement of solar farm capacity estimation, either as an alternative or an additional solution to exploring other data sources [27]. Although classic data augmentations such as contrast adjustments, random rotations, and flips are utilized, brand-new remote sensing images may benefit the process of solar farm detection and energy generation capacities with even more potential in terms of accuracy.…”

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

“…These techniques use overhead imagery and deep neural networks to detect and map solar PV capacity using computer vision. For instance, Ravishankar et al [29] devised a deep learning framework to estimate the global capacity of solar farms from high-resolution satellite imagery, achieving an average error rate of 4.5% when validated against publicly available data; while this method effectively detects largescale solar installations, it can be computationally expensive. Detecting small-scale solar PV installations is more complicated as it necessitates high-resolution imagery to maintain model performance [30,31].…”

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

“…In the contribution by Ravishankar et al, published under the name of "Capacity Estimation of Solar Farms Using Deep Learning on High-Resolution Satellite Imagery", the authors propose a deep learning framework for detecting solar power plants via the application of semantic segmentation convolutional neural networks to satellite images [5]. They also propose a model that predicts the energy generation capacity of the detected solar power plant facility.…”