Evaluation of model fidelity for solar analysis in the context of distributed PV integration at urban scale

This paper examines the level of model fidelity required to support design phases in the urban solar planning process.The two modelling features crucial to the fidelity of the photovoltaic(PV)yield prediction on urban surfaces are(1)a level of fidelity for modelling urban shading and solar reflection and(2)a level of fidelity for modelling PV system operation.The paper compares three different models for predicting urban shading and reflection and two different PV models for predicting PV system operation.The relevance of the model fidelities is investigated through a case study of an urban area in Wuhan,China under three decision-making contexts:setting a solar target,place-making,and economic assessment for urban-scale distributed PV integration.Predictions for the decision-makings are generated using the selected models through computational simulation under the same annual weather profile.The results show that the relatively less accurate canyon-based method tends to overpredict with 57 buildings identified as suitable for PV installation for walls in the studied urban area;the more accurate vector-based model predicts only 14 suitable buildings.The results predicted with additional consideration of dynamic PV system operation exhibit differences from those predicted by the static PV system model,with a difference of roughly 13 buildings on average within each payback-time category.The differences are noticeable but can be regarded as incremental for urban-scale economic assessment compared with the significant difference due to the fidelity level of modelling urban shading and reflection.

Observations of interplanetary scintillation with a single-station mode at Urumqi

The Sun affects physical phenomena on Earth in multiple ways. In particular, the material in interplanetary space comes from coronal expansion in the form of inhomogeneous plasma flow （solar wind）, which is the primary source of the interplanetary medium. Ground-based Interplanetary Scintillation （IPS） observations are an important and effective method for measuring solar wind speed and the structures of small diameter radio sources. We discuss one mode of ground-based single-station observations： Single-Station Single-Frequency （SSSF） mode. To study the SSSF mode, a new system has been established at Urumqi Astronomical Observatory （UAO）, China, and a series of experimental observations were successfully carried out from May to December, 2008.

DeepRadiation:An intelligent augmented reality platform for predicting urban energy performance just through 360 panoramic streetscape images utilizing various deep learning models

Urban energy simulation is critical for understanding and managing energy performance in cities.In this research,we design a novel framework called DeepRadiation,to enable automatic urban environmental performance prediction.By incorporating deep learning strategies,DeepRadiation predicts solar radiation on an urban scale using just panoramic streetscape images without any 3D modeling and simulation.New York City was chosen as the case study for this research.DeepRadiation is comprised of three different deep learning models organized into two stages.The first stage,named DeepRadiation modeling,serves as the framework's brain.At this stage,solar radiation analysis was performed using a Pix2Pix model,a type of conditional generative adversarial networks(GANs).After extracting GIS data and performing energy simulation analysis to prepare the dataset,the Pix2Pix model was trained on 10000 paired panoramic depth images of streetscapes with only building blocks and related panoramic images of streetscapes with only solar radiation analysis.Two GAN generator evaluation measures named qualitative evaluation and quantitative evaluation were used to validate the trained Pix2Pix model.Both demonstrated high levels of accuracy(qualitative evaluation:93%,quantitative evaluation:89%).DeepRadiation application as the DeepRadiation's sescond stage is the framework's eyes.At this stage,two convolutional neural network(CNN)models(DeepLabv3 and MiDaS)were used to perform computer vision tasks on panoramic streetscape images,such as semantic segmentation and depth estimation.The DeepRadiation application stage allows urban designers,architects,and urban policymakers to use the DeepRadiation framework and experience the final output via augmented reality.