A city-scale roof shape classification using machine learning for solar energy applications

Renewable Energy

Volumn 121, Issue , 2018, Pages 81-93

  Mohajeri, Nahid ^a,b   Assouline, Dan ^a   Guiboud, Berenice ^a   Bill, Andreas ^a   Gudmundsson, Agust ^c   Scartezzini, Jean Louis ^a  

^a EPFL   (Switzerland)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Machine learning; PV potential; Roof shape classification; Support Vector Machine

Indexed keywords

BUILDINGS; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; SOLAR ENERGY; SUPPORT VECTOR MACHINES;

BUILDING ROOF; CITY SCALE; MACHINE-LEARNING; PV INSTALLATIONS; PV POTENTIAL; ROOF SHAPE; ROOF SHAPE CLASSIFICATION; SHAPE CLASSIFICATION; SOLAR ENERGY APPLICATIONS; SUPPORT VECTORS MACHINE;

ROOFS;

BUILDING; CLASSIFICATION; DESIGN; INSTALLATION; MACHINE LEARNING; PHOTOVOLTAIC SYSTEM; SOLAR POWER; SUPPORT VECTOR MACHINE;

GENEVA [SWITZERLAND]; SWITZERLAND;

EID: 85040325135     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2017.12.096     Document Type: Article

References (44)

1. Wiginton, L., Nguyen, H., Pearce, J., Quantifying rooftop solar photovoltaic potential for regional renewable energy policy. Comput. Environ. Urban 34 (2010), 345–357.
2. Nowak, S., Gutschner, M., Ruoss, D., Togweiler, P., Schoen, T., Potential of Building Integrated Photovoltaics. Report IEA-PVPS T7–4., 2002.
3. Goodling, J., Crook, R., Tomlin, A.S., Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighboring buildings method. Appl. Energy 148 (2015), 93–104.
4. Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P., Koehl, M., Model-driven and Data-driven Approaches Using LIDAR Data: Analysis and Comparison, Munich. 2007, 87–92.
5. Brito, M.C., Gomes, N., Santos, T., Tenedorio, J.A., Photovoltaic potential in a Lisbon suburb using LiDAR data. Sol. Energy 86 (2012), 283–288.
6. Carneiro, C., Morello, E., Desthieux, G.,. Assessment of Solar Irradiance on the Urban Fabric for the Production of Renewable Energy Using Lidar Data and Image Processing Techniques. 2009, Advances in GIScience Springer, 83–112.
7. Mohajeri, N., Gudmundsson, A., Upadhyay, G., Assouline, D., Kämpf, J., Scartezzini, J.L., Effects of urban compactness on solar energy potential. Renew. Energy 93 (2016), 469–482.
8. Carneiro, C., Morello, E., Voegtle, T., Golay, F., Digital urban morphometrics: automatic extraction and assessment of morphological properties of buildings. T. GIS 14 (2010), 497–531.
9. Lukac, N., Seme, S., Zlaus, D., Stumberger, G., Zalik, B., Buildings roofs photovoltaic potential assessment based on (Light Detection and Ranging) data. Energy 66 (2014), 598–609.
10. Assouline, D., Mohajeri1, N., Scartezzini, J.-L., Quantifying rooftop photovoltaic solar energy potential: a machine learning approach. Sol. Energy 141 (2017), 278–296.
11. Gooding, J., Crook, R., Tomlin, A.S., Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighboring buildings method. Appl. Energy 148 (2015), 93–104.
12. Kada, M., Scale-dependent simplification of 3D building models based on cell decomposition and primitive instancing. Proceedings of the International Conference on Spatial Information Theory. COSIT'07. Melbourne, Australia, 2007, 222–237.
13. Khan, J., Arsalan, M.H., Estimation of rooftop solar photovoltaic potential using geo-spatial techniques: a perspective from planned neighborhood of Karachi – Pakistan. Renew. Energy, 90, 2016 188e203.
14. Izquierdo, S., Rodrigues, M., Fueyo, N., A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Sol. Energy 82 (2008), 929–939.
15. Shapiro, I., The receptivity of roofs to solar panels. Proceedings of the 2nd World Sustain. Forum, 1–30 November 2012; Sciforum Electronic Conference Series, vol. 2, 2012.
16. E, Nault, G., Peronato, G., Andersen, Rey M., Review and critical analysis of early-design phase evaluation metrics for the solar potential of neighborhood designs. Build. Environ., 92, 2015 679e691.
17. Li, D., Liu, G., Liao, S., Solar potential in urban residential buildings. Sol. Energy, 111, 2015 225e235.
18. Konis, K., Games, A., Kensek, K., Passive performance and building form: an optimization framework for early-stage design support. Sol. Energy 125 (2016), 161–179.
19. Voyant, C., Notto, G., Kalogirou, S., Nivet, M.L., Paoli, C., Motte, F., Fouilloy, A., Machine learning methods for solar radiation forecasting: a review. Renew. Energy, 105, 2017 569e582.
20. Kanevski, M., Statistical learning in geoscience modelling: novel algorithms and challenging case studies. Comput. Geosci. 85 (2015), 1–2.
21. Kanevski, M., Pozdnukhov, A., Timonin, V., Machine Learning for Spatial Environmental Data: Theory, Applications, and Software. 2009, EPFL Press, Spain.
22. Kanevski, M., Maignan, M., Analysis and Modelling of Spatial Environmental Data. 2004, EPFL Press, Lausanne (.
23. Cortes, C., Vapnik, V., Support-vector networks. Mach. Learn. 20 (1995), 273–297.
24. Hur-Ben, A., Watson, J., A user's guide to Support Vector Machines. Meth. Mol. Biol. 609 (2010), 223–239, 10.1007/978-1-60327-241-4_13.
25. Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2013, Springer Series in Statistics, USA.
26. Fletcher, T., Support Vector Machines Explained. 2009, University College London.
27. Burges, C., A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2 (1998), 121–167.
28. Vanderbei, R., LOQO: an Interior Point Code for Quadratic Programming, Technical Report (Revised) SOR 94-15., 1998, Princeton University.
29. Vapnik, V., Statistical Learning Theory. 1998, John Wiley & Sons.
30. Scholkopf, B., Smola, A., Learning with Kernels: Support Vector Machines, Regularization, Optimization. 2002, MIT Press, Beyond.
31. Smola, A., Scholkopf, B., A tutorial on support vector regression. Stat. Comput. 14 (2004), 199–222.
32. Desthieux, G., Carneiro, C., Morello, E., Cadastre Solaire Du Canton De Geneve. Raport final. Etude réalisée par hepia, EPFL et Politenico di Milano pour le compte du Service de l’énergie-ScanE et des Services industriels genevois (SIG). 2011.
33. Desthieux, G., Gallinelli, P., Camponovo, R., Cadastre Solaire Du Canton De Geneve – Phase 2. Analyse du potentiel de production énergétique par les panneaux solaires thermiques et PV. Rapport final. Etude pour le compte de l'Office cantonal de l’énergie (OCEN) et des Services industriels genevois (SIG). 2014.
34. Li, D.H.W., Lam, T.N.T., Determining the optimum tilt angle and orientation for solar energy collection based on measured solar radiance data. Int. J. Photoenergy, 2007, 10.1155/2007/85402 85402.
35. Li, D.H.W., Cheung, G.H.W., Study of models for predicting the diffuse irradiance on inclined surfaces. Appl. Energy 81 (2005), 170–186.
36. Robinson, D., Scartezzini, J.L., Montavon, M., Compagnon, R., SOLURBAN Project: Solar Utilisation Potential of Urban Sites, Swiss Federal Office for Energy. 2005, Bundesamt für Energie BFE, Bern (.
37. NET, Nowak Energie & Technologie SA. Le Potentiel Solaire dans le Canton de Genève. Technical report, Waldweg 8, Switzerland, St. Ursen, 2004.
38. Desthieux, G., Gallinelli, P., Camponovo, R., Analyse du potentiel de production énergétique par les panneaux solaires thermique et PV. Final report. Etude pour le compte de l'Office cantonal de l’énergie (OCEN) et des Services industriels genevois (SIG), Geneva., 2014.
39. International Energy Agency (IEA). reportPotential for Building Integrated Photovoltaics. Achievable Levels of Electricity from Photovoltaic Roofs and Façades: Methodology, Case Studies, Rules of Thumb and Determination of the Potential of Building Integrated Photovoltaics for Selected Countries. Summary of Report IEA - PVPS T7–4.
40. Cheng, C.L., Sanchez Jimenez, C.S., Lee, M., Research of BIPV optimal tilted angle, use of latitude concept for south orientated plans. Renew. Energy 34 (2009), 1644–1650.
41. Gharakhani Siraki, A., Pillay, P., Study of optimum tilt angles for solar panels in different latitudes for urban applications. Sol. Energy 86 (2012), 1920–1928.
42. Lave, M., Kleissl, J., Optimum fixed orientations and benefits of tracking for capturing solar radiation in the continental United States. Renew. Energy 36 (2011), 1145–1152.
43. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12 (2011), 2825–2830.
44. Assouline, D., Mohajeri, N., Scartezzini, J.-L., Building rooftop classification using random Forests for large-scale PV deployment. Earth resources and environmental remote sensing/GIS applications VIII. Michel, Ulrich, (eds.) Karsten Schulz Proc. of SPIE, vol. 10428, 2017, 1042806, 10.1117/12.2277692.