In the case of building facades, introducing weighting factor in SVF calculation leads to outputs close to those obtained by PVSyst. Such good validation results make the proposed model a reliable tool to: (i) automatically process solar cadaster on building rooftops and facades at large urban scales and (ii) support solar energy planning and energy transition policies.
Cities play an increasingly important role with regards to energy transition. Main goal is to reach international and national (Swiss) targets related to energy efficiency and CO2 emission reduction. As a contribution to these global challenges, during the last 6 years the State of Geneva has been producing a detailed solar cadaster. In order to facilitate periodical updates of this solar cadaster, the iCeBOUND project was launched. Around 10 public and private stakeholders, all linked within the Geneva Territorial Information System (SITG), collaborated on the project. Its aim was to design and develop a cloud-based Decision Support System (DSS) that leverages 3D digital urban data with high computing performance, hence facilitating environmental analyses in large built areas, like solar energy potential assessment. As result of the project, an official geoportal and a newfangled public web interface were made widely available early 2017, so as to strengthen decision making with regards to solar installation investment
Abstract-Parallel applications are highly irregular and high performance computing (HPC) infrastructures are very complex. The HPC applications of interest herein are timestepping scientific applications (TSSA). Often, TSSA involve the repeated execution of multiple parallel loops with thousands of iterations and irregular behavior. Dynamic loop scheduling (DLS) techniques were developed over time and have proven to be effective in scheduling parallel loops for achieving load balancing of TSSA. Using a single particular DLS technique throughout the entire execution of a time-step, or even over the entire application, does not guarantee optimal performance due to the unpredictable variations in problem and algorithmic characteristics as well as those of the infrastructure capabilities. For that reason, an autonomic selection of DLS techniques as function of the parallel loop execution time has shown to improve application performance. Recently, a robustness metric of DLS techniques, named "flexibility", has been proposed to estimate the capability of a DLS technique to resist to variations in the loop iterations execution time. To improve the performance of TSSA, we propose in this work an approach that involves the autonomic selection of DLS techniques as function of the flexibility of DLS techniques. The first major novelty of our approach lies in the use of state-of-the-art reinforcement learning (RL) algorithms as smart agents. The second novelty lies in the design of a modified flexibility metric. The third major novelty resides in using the new modified flexibility metric as a reward for the smart agents. The fourth novelty is the evaluation of the proposed approach within a simulated environment, in particular using the SimGrid-SMPI interface to execute DLS algorithms. We discuss the advantages and the limitations of the new proposed flexibility metric as a reward.
In the context of encouraging the development of renewable energy, this paper deals with the description of a software solution for mapping out solar potential in a large scale and in high resolution. We leverage the performance provided by Graphics Processing Units (GPUs) to accelerate shadow casting procedures (used both for direct sunlight exposure and the sky view factor), as well as use off-the-shelf components to compute an average weather pattern for a given area. Application of the approach is presented in the context of the solar cadaster of Greater Geneva (2000 km2). The results show that doing the analysis on a square tile of 3.4 km at a resolution of 0.5 m takes up to two hours, which is better than what we were achieving with the previous work. This shows that GPU-based calculations are highly competitive in the field of solar potential modeling.
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