René Buffat

Feature-aware surface interpolation of rooftops using low-density Lidar data for photovoltaic applications

Digital surface models (DSM) are used to estimate the solar irradiation on rooftops. Estimates are more accurate when the precise geometrical characteristics of roofs are well represented in the DSM. The existing DSM covering Switzerland has a low accuracy for buildings. It was derived from a low density Lidar dataset with an average point density of 0.5 points per square meter. In this paper, we present a method to interpolate a DSM from point cloud data focusing on geometric modelling of rooftops. The method uses a combination of robustly fitted planes to local point clouds and inverse distance weighting interpolation. It was applied to roughly 3 million buildings, and compared to a reference DSM from a high density point cloud, which revealed a significant reduction of error compared to the existing DSM.

Assessing Accuracy and Geographical Transferability of Machine Learning Algorithms for Wind Speed Modelling

Machine learning is very popular in the environmental modelling community and has recently been demonstrated to be a useful tool for wind resource assessment as well. Despite the popularity of wind resource assessment, research in the field of machine learning for this purpose is in its infancy. Only few algorithms have been tested and only for specific areas, making it difficult to draw any conclusions in regards to the best wind estimation method at the global scale. In this study, we compared several machine learning algorithms with validation techniques specifically employed to not only assess their accuracy but also their transferability. In particular, we tested cross-validation techniques designed to test the accuracy of the estimation in the context of autocorrelation. This way we performed a benchmarking experiment that should provide end users with practical rules of application for each algorithm. We tested three families of popular algorithms, namely linear models, decision trees and support vector machines; each was tested using wind mean speed data and several environmental covariates as predictors. The results demonstrated that no single algorithm could consistently be used to estimate wind globally, even though decision-tree based methods seemed to be often the best estimators.

Captcha Your Location Proof—A Novel Method for Passive Location Proofs in Adversarial Environments

A large number of online rating and review platforms allow users to exchange their experiences with products and locations. These platforms need to implement appropriate mechanisms to counter malicious content, such as contributions which aim at either wrongly accrediting or discrediting some product or location. For ratings and reviews of locations, the aim of such a mechanism is to ensure that a user actually was at said location, and did not simply post a review from another, arbitrary location. Existing solutions usually require a costly infrastructure, need proof witnesses to be co-located with users, or suggest schemes such as users taking pictures of themselves at the location of interest. This paper introduces a method for location proofs based on visual features and image recognition, which is cheap to implement yet provides a high degree of security and tamper-resistance without placing a large burden on the user.

Using locally produced photovoltaic energy to charge electric vehicles

Mobility in Switzerland currently consumes about 35% of the total energy demand. While internal combustion engines still generate most of it, the increasing number of electric vehicles changes the landscape by decoupling energy production from consumption. This allows using more sustainable energy sources, such as photovoltaics (PV), hydroelectric power plants or wind turbines. In the past years, the number of PV installations has grown rapidly in Switzerland. It is expected that PV has the highest growth potential of all renewable energy sources. Solar panels are especially interesting, as they can be installed on most buildings, which distributes the electricity production. However, due to frequent fluctuations in production, PV poses a challenge for the existing power grid. It is unclear to what extent PV production can be increased without the need for extensions of the power grid, such as additional transmission lines or storage capabilities. Electric vehicles could be used to consume fluctuating electricity production. In this paper, we study the effects of using locally produced photovoltaic power to recharge electric vehicles of commuters in individual Swiss municipalities. Such an analysis not only gives us indications of the potentials and limits of using photovoltaics to satisfy mobility energy demands, but can also be used to better direct subsidies and plan the electrical grid.

Validation of CM SAF SARAH solar radiation datasets for Switzerland

The CMSAF SARAH dataset is currently the only publicly solar radiation dataset with a spatial resolution of 0.05 degree and an hourly temporal resolution covering Switzerland over a time period of more than 30 years. The dataset has a higher bias in mountainous regions. Large parts of Switzerland, especially in the Jura and Alps, are mountainous. We compared the daily global and diffuse irradiance with measured irradiance data of ground stations at different elevations. The mean absolute bias increases with higher elevation. We investigated the correlation of the elevation of buildings with the error of the ground stations. Stations below 1000 meter elevation have an average mean daily bias of -0.18 ± 8.54 W/m2 representing 87% of Swiss building footprints.