Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests

[1]  Ali Al-Alili,et al.  A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks , 2017 .

[2]  Weiyi Li,et al.  Typical solar radiation year construction using k-means clustering and discrete-time Markov chain , 2017 .

[3]  Muhammed A. Hassan,et al.  Exploring the potential of tree-based ensemble methods in solar radiation modeling , 2017 .

[4]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[5]  Ursula Eicker,et al.  Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach , 2017 .

[6]  Danny H.W. Li,et al.  Prediction of diffuse solar irradiance using machine learning and multivariable regression , 2016 .

[7]  Yusuf Al-Turki,et al.  Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study , 2015 .

[8]  L. D. Monache,et al.  An analog ensemble for short-term probabilistic solar power forecast , 2015 .

[9]  Alison S. Tomlin,et al.  Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method , 2015 .

[10]  T. Soubdhan,et al.  A benchmarking of machine learning techniques for solar radiation forecasting in an insular context , 2015 .

[11]  Shahaboddin Shamshirband,et al.  Potential of radial basis function based support vector regression for global solar radiation prediction , 2014 .

[12]  Bikash Joshi,et al.  Rooftop Detection for Planning of Solar PV Deployment: A Case Study in Abu Dhabi , 2014, DARE.

[13]  Alison S. Tomlin,et al.  Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK) , 2014 .

[14]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[15]  G. Štumberger,et al.  Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data , 2014 .

[16]  A. Massi Pavan,et al.  A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant , 2013 .

[17]  Jinqing Peng,et al.  Investigation on the development potential of rooftop PV system in Hong Kong and its environmental benefits , 2013 .

[18]  G. Štumberger,et al.  Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data , 2013 .

[19]  Volker Coors,et al.  Large scale integration of photovoltaics in cities , 2012 .

[20]  Joshua M. Pearce,et al.  Incorporating Shading Losses in Solar Photovoltaic Potential Assessment at the Municipal Scale , 2012, Solar Energy.

[21]  Sonia Martínez,et al.  Storage Size Determination for Grid-Connected Photovoltaic Systems , 2011, IEEE Transactions on Sustainable Energy.

[22]  L. Bergamasco,et al.  Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: application to Piedmont Region (Italy) , 2011 .

[23]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[24]  Germán Martínez,et al.  Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain) , 2010 .

[25]  Joshua M. Pearce,et al.  Quantifying Rooftop Solar Photovoltaic Potential for Regional Renewable Energy Policy , 2010, Comput. Environ. Urban Syst..

[26]  Melvin Pomerantz,et al.  Solar access of residential rooftops in four California cities , 2009 .

[27]  J. Kaňuk,et al.  Assessment of photovoltaic potential in urban areas using open-source solar radiation tools , 2009 .

[28]  A. Rosenfeld,et al.  Global cooling: increasing world-wide urban albedos to offset CO2 , 2009 .

[29]  N. Fueyo,et al.  A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations , 2008 .

[30]  Nicolai Meinshausen,et al.  Quantile Regression Forests , 2006, J. Mach. Learn. Res..

[31]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[32]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[33]  Michael Geiger,et al.  A Web service for controlling the quality of measurements of global solar irradiation , 2002 .

[34]  Gerard B. M. Heuvelink,et al.  Propagation of errors in spatial modelling with GIS , 1989, Int. J. Geogr. Inf. Sci..

[35]  S. Klein.  Calculation of monthly average insolation on tilted surfaces , 1976 .

[36]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[37]  D. Assouline,et al.  Quantifying rooftop photovoltaic solar energy potential: A machine learning approach , 2017 .

[38]  J. Byrne,et al.  A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of Seoul , 2015 .

[39]  Volker Quaschning,et al.  Sizing of Residential PV Battery Systems , 2014 .

[40]  T. Santos,et al.  Photovoltaic potential in a Lisbon suburb using LiDAR data , 2012 .

[41]  BMC Bioinformatics BioMed Central Methodology article , 2007 .

[42]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[43]  L. Breiman.  Random Forests , 2001, Machine Learning.

[44]  W. Beckman,et al.  Evaluation of hourly tilted surface radiation models , 1990 .

[45]  P. Andersen.  Comments on “Calculations of monthly average insolation on tilted surfaces” by S. A. Klein , 1980 .

[46]  Anthony Alan Clifford,et al.  Multivariate error analysis : a handbook of error propagation and calculation in many-parameter systems , 1973 .