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

Dan Assouline, Andreas Bill, Bérénice Guiboud, Nahid Mohajeri Pour Rayeni, Jean-Louis Scartezzini
2018
Article

Résumé

Solar energy deployment through PV installations in urban areas depends strongly on the shape, size, and orientation of available roofs. Here we use a machine learning approach, Support Vector Machine (SVM) classification, to classify 10,085 building roofs in relation to their received solar energy in the city of Geneva in Switzerland. The SVM correctly identifies six types of roof shapes in 66% of cases, that is, flat & shed, gable, hip, gambrel & mansard, cross/corner gable & hip, and complex roofs.We classify the roofs based on their useful area for PV installations and potential for receiving solar energy. For most roof shapes, the ratio between useful roof area and building footprint area is close to one, suggesting that footprint is a good measure of useful PV roof area. The main exception is the gable where this ratio is 1.18. The flat and shed roofs have the second highest useful roof area for PV (complex roof being the highest) and the highest PV potential (in GWh). By contrast, hip roof has the lowest PV potential. Solar roof-shape classification provides basic information for designing new buildings, retrofitting interventions on the building roofs, and efficient solar integration on the roofs of buildings.

Official source

https://infoscience.epfl.ch/record/233794?ln=fr

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

Publications associées (36)

Does roof shape matter? Solar photovoltaic (PV) integration on building roofs

Dan Assouline, Bérénice Guiboud, Nahid Mohajeri Pour Rayeni, Jean-Louis Scartezzini

This paper focuses on roof-shape classification and solar potential for integrating photovoltaics (PV) on roofs. A machine-learning approach, Support Vector Machine (SVM), is used to classify the roof shapes in the city of Geneva. The impact of various roof characteristics on the solar potential is assessed and analyzed. Monthly solar irradiation on rooftops, freely accessible from www.ge.ch/sitg using GIS LiDAR data (resolution 50cm by 50cm), is used in the analysis. The SVM identifies 6 types of roof shapes correctly in 66% of cases, that is, flat, gable, hip, gambrel & mansard, cross/corner gable & hip, and complex roof. We rank the roofs based on the complexity of their shape, useful area for PV, and potential for receiving solar energy. The results show that for most roof shapes the ratio between useful roof areas and building footprint area is close to one, the main exception being gable where the ratio is 1.18, suggesting that footprint is a good measure of useful PV roof area. The flat roof has the second highest useful roof area for PV (complex roof being the highest) and the highest PV potential (in GWh). By contrast, hip roof has the lowest PV potential. Solar roof-shape classification provides basic information for designing new buildings, retrofitting interventions on the building roofs and efficient solar integration on rooftops. The results for the city of Geneva suggest that data-driven approach is very useful for roof-shape classification which can be expanded to the national scale.

2016

Chargement

Optimisation of Urban Form by the Evaluation of the Solar Potential

Marylène Montavon

What will the cities of tomorrow look like? This is a crucial question because in the four and a half billion years of its existence, the Earth has never been so threatened, as it is today. Since 2008 half of the world's population has lived in towns – more than 70% in rich nations. Three quarters of CO2 emissions are produced in towns, 75% of the world's energy is consumed in towns. The battle for our climate, for the continuation of our species and all other life, will be largely waged in cities. Will it be possible to create the ideal ecological town? The objective of this study is to compare actual and theoretical urban forms in order to explore the diverse effects of daylighting and solar potential on densely built-up sites. Indeed, the search for ideal solar orientations has been a persistent problem from the 19th century until the beginning of the 20th. Even today, for all our scientific knowledge and empirical experience of solar energy, making the correct choice has still not crossed the boundary from an enigmatic, tricky art into the field of an exact science. As a first step, six existing urban sites in Switzerland and Brazil, as well as two projects in the United Kingdom, a few generic models and a utopian city, were modelled and analyzed. The goal was not merely the construction of new, exemplary districts but also the transformation of existing towns. The findings have been used to assess the potential of façades and roofs located in urban areas for active and passive solar heating, photovoltaic electricity generation and daylighting. The results obtained from these different case studies revealed large variations of the potential for solar energy collection on the buildings' façades and roofs. Ideally, these investigations are expected to help architects and town planners understand how to optimise solar collection on buildings design. In addition, a study was made of the Contemporary City of Three Million Inhabitants created by Le Corbusier. According to the latter, the Contemporary City could increase the urban capacity and at the same time improve the urban environment and the efficiency of the city. Therefore the present study extracted the solar potential of this utopian city in order to assess Le Corbusier's propositions. Since 1922, Le Corbusier's studies have influenced architects and town-planners in their choice of orientations and urban forms: it was therefore crucial to discover if this reliance on Le Corbusier was justified. Solar irradiation and illuminance values obtained through numerical simulations form the core part of the method. The method adopted for the entire set of computer simulations consisted in modelling — via a computer-aided drawing piece of software, namely AutoCAD — the digital model of each building or group of buildings prior to exporting them to an urban performance analysis software (PPF/RADIANCE software package). Spatial distributions of solar irradiation and daylight fluxes over the overall building façades and roofs were calculated using ray-tracing simulation techniques to determine the appropriate placement of different solar technologies (passive and active solar, photovoltaic and daylighting). Performance indicators were used to assess the solar utilisation potential of the urban sites (e.g. statistics of sky view factors and daylight factors) in order to determine the optimal solar strategies for a given urban context. In addition, various works and sun-related recommendations that have appeared mainly since the end of the nineteenth century, together with the various scientific models and software tools pertaining to this topic, are presented in this study.

EPFL2010

Chargement

A promising pathway to follow in order to reach sustainable development goals is an increased reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the demand loads in locations of interest. In particular, large-scale supply estimation studies are needed in order to evaluate areas of high potential for each type of energy source for a particular region, and allow for the elaboration of efficient global energy strategies. In Switzerland, the "Energy Strategy 2050", initiated in 2011 by the Swiss Federal Council, sets an example with the ambitious goal of reaching a 50-80% reduction of CO2 emissions by the year 2050, with a clear course of action: phasing-out nuclear power, improving energy efficiency, and greatly increasing the use of renewables. This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using of horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). For each of the three considered energy sources, an adapted methodology is suggested to assess its large-scale potential, by estimating multiple variables of interest (with a suitable time resolution, e.g. monthly or yearly), using widely available data, and combining these variables into potential values. These latter estimated variables, dictating the potential, include: (i) the monthly wind speed, and rural and urban topographic/obstacle configuration for wind energy, (ii) the ground thermal conductivity, volumetric heat capacity and monthly temperature gradient for geothermal energy, (iii) the monthly solar radiation, available area for PV panels over rooftops, geometrical characteristics of rooftops and monthly shading factors over rooftops for solar energy. The use of Machine Learning algorithms (notably Support Vector Machines and Random Forests) allows, given adequate features and training data (examples for some locations), for the prediction of the latter variables at unknown locations, along with the uncertainty attached to the predictions. In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies. Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for Rooftop-Mounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.

EPFL2019

Chargement

Afficher plus

Optimisation of Urban Form by the Evaluation of the Solar Potential

Marylène Montavon

EPFL2010

EPFL2019