DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

[1]  A. Muhar,et al.  Individual and collective socio-psychological patterns of photovoltaic investment under diverging policy regimes of Austria and Italy , 2017 .

[2]  R. Margolis,et al.  Terawatt-scale photovoltaics: Trajectories and challenges , 2017, Science.

[3]  Kimberly S. Wolske,et al.  Explaining interest in adopting residential solar photovoltaic systems in the United States: Toward an integration of behavioral theories , 2017 .

[4]  Jordan M. Malof,et al.  Distributed solar photovoltaic array location and extent dataset for remote sensing object identification , 2016, Scientific Data.

[5]  Jiangye Yuan,et al.  Large-scale solar panel mapping from aerial images using deep convolutional networks , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[6]  Jordan M. Malof,et al.  A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery , 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

[7]  Jordan M. Malof,et al.  Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier , 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

[8]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[9]  Jordan M. Malof,et al.  Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery , 2016, ArXiv.

[10]  R. Margolis,et al.  Overcoming barriers and uncertainties in the adoption of residential solar PV , 2016 .

[11]  A. Palm Local factors driving the diffusion of solar photovoltaics in Sweden: A case study of five municipalities in an early market , 2016 .

[12]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Alfred Posch,et al.  Photovoltaic diffusion from the bottom-up: Analytical investigation of critical factors , 2015 .

[15]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Axel J. Schaffer,et al.  Beyond the sun—Socioeconomic drivers of the adoption of small-scale photovoltaic installations in Germany , 2015 .

[17]  C. Mellander,et al.  Night-Time Light Data: A Good Proxy Measure for Economic Activity? , 2015, PloS one.

[18]  Olivier De Groote,et al.  Heterogeneity in the Adoption of Photovoltaic Systems in Flanders , 2015 .

[19]  Kenneth Gillingham,et al.  Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment , 2015 .

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Scott Agnew,et al.  Effect of residential solar and storage on centralized electricity supply systems , 2015 .

[23]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[25]  Robert Margolis,et al.  Modeling photovoltaic diffusion: an analysis of geospatial datasets , 2014 .

[26]  Yevgeniy Vorobeychik,et al.  Individual Household Modeling of Photovoltaic Adoption , 2014, AAAI Fall Symposia.

[27]  Ilya Chernyakhovskiy,et al.  Solar PV Technology Adoption in the United States: An Empirical Investigation of State Policy Effectiveness , 2014 .

[28]  Hui Li,et al.  Multilevel governance and deployment of solar PV panels in U.S. cities , 2014 .

[29]  Chelsea Schelly Residential solar electricity adoption: What motivates, and what matters? A case study of early adopters , 2014 .

[30]  N. Meade,et al.  The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation , 2013 .

[31]  Varun Rai,et al.  Decision-making and behavior change in residential adopters of solar PV , 2012 .

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  Kenneth Gillingham,et al.  Peer Effects in the Diffusion of Solar Photovoltaic Panels , 2012, Mark. Sci..

[34]  A. Majumdar,et al.  Opportunities and challenges for a sustainable energy future , 2012, Nature.

[35]  Calvin Lee Kwan,et al.  Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States , 2012 .

[36]  G. Shrimali,et al.  The impact of state financial incentives on market deployment of solar technology , 2012 .

[37]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[38]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[39]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[40]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[42]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[43]  Samdruk Dharshing Household dynamics of technology adoption: A spatial econometric analysis of residential solar photovoltaic (PV) systems in Germany , 2017 .

[44]  Véronique Vasseur,et al.  The adoption of PV in the Netherlands: A statistical analysis of adoption factors , 2015 .

[45]  Victor S. Sheng,et al.  Cost-Sensitive Learning and the Class Imbalance Problem , 2008 .

[46]  R. Porter The United States Census , 1890, Nature.