365 research outputs found
A regional solar forecasting approach using generative adversarial networks with solar irradiance maps
The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region
Cloud Segmentation and Classification from All-Sky Images Using Deep Learning
For transforming the energy sector towards renewable energies, solar power is regarded as one of the major resources. However, it is not uniformly available all the time, leading to fluctuations in power generation. Clouds have the highest impact on short-term temporal and spatial variability. Thus, forecasting solar irradiance strongly depends on current cloudiness conditions. As the share of solar energy in the electrical grid is increasing, so-called nowcasts (intra-minute to intra-hour forecasts) are beneficial for grid control and for reducing required storage capacities. Furthermore, the operation of concentrating solar power (CSP) plants can
be optimized with high resolution spatial solar irradiance data.
A common nowcast approach is to analyze ground-based sky images from All-Sky Imagers. Clouds within these images are detected and tracked to estimate current and immediate
future irradiance, whereas the accuracy of these forecasts depends primarily on the quality of pixel-level cloud recognition. State-of-the-art methods are commonly restricted to binary segmentation, distinguishing between cloudy and cloudless pixels. Thereby the optical properties of different cloud types are ignored. Also, most techniques rely on threshold-based detection showing difficulties under certain atmospheric conditions. In this thesis, two deep learning approaches are presented to automatically determine
cloud conditions. To identify cloudiness characteristics like a free sun disk, a multi-label classifier was implemented assigning respective labels to images. In addition, a segmentation model was developed, classifying images pixel-wise into three cloud types and cloud-free sky. For supervised training, a new dataset of 770 images was created containing ground truth labels and segmentation masks. Moreover, to take advantage of large amounts of raw data, self-supervised pretraining was applied. By defining suitable pretext tasks, representations of image data can be learned facilitating the distinction of cloud types. Two successful techniques were chosen for self-supervised learning: Inpainting- uperresolution and DeepCluster. Afterwards, the pretrained models were fine-tuned on the annotated dataset. To assess the effectiveness of self-supervision, a comparison with random initialization and pretrained ImageNet weights was conducted. Evaluation shows that segmentation in particular benefits from self-supervised learning, improving accuracy and IoU about 3% points compared to ImageNet pretraining. The best segmentation model was also evaluated on binary segmentation. Achieving an overall accuracy of 95.15%, a state-of-the art Clear-Sky-Library (CSL) is outperformed significantly by over 7% points
Deep Dynamic Cloud Lighting
Sky illumination is a core source of lighting in rendering, and a substantial
amount of work has been developed to simulate lighting from clear skies.
However, in reality, clouds substantially alter the appearance of the sky and
subsequently change the scene's illumination. While there have been recent
advances in developing sky models which include clouds, these all neglect cloud
movement which is a crucial component of cloudy sky appearance. In any sort of
video or interactive environment, it can be expected that clouds will move,
sometimes quite substantially in a short period of time. Our work proposes a
solution to this which enables whole-sky dynamic cloud synthesis for the first
time. We achieve this by proposing a multi-timescale sky appearance model which
learns to predict the sky illumination over various timescales, and can be used
to add dynamism to previous static, cloudy sky lighting approaches.Comment: Project page: https://pinarsatilmis.github.io/DDC
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning
ABSTRACT
Solar energy is now the cheapest form of electricity in history. Unfortunately,
signi.cantly increasing the electric grid’s fraction of
solar energy remains challenging due to its variability, which makes
balancing electricity’s supply and demand more di.cult. While
thermal generators’ ramp rate—the maximum rate at which they
can change their energy generation—is .nite, solar energy’s ramp
rate is essentially in.nite. Thus, accurate near-term solar forecasting,
or nowcasting, is important to provide advance warnings to
adjust thermal generator output in response to variations in solar
generation to ensure a balanced supply and demand. To address the
problem, this paper develops a general model for solar nowcasting
from abundant and readily available multispectral satellite data
using self-supervised learning.
Speci.cally, we develop deep auto-regressive models using convolutional
neural networks (CNN) and long short-term memory
networks (LSTM) that are globally trained across multiple locations
to predict raw future observations of the spatio-temporal spectral
data collected by the recently launched GOES-R series of satellites.
Our model estimates a location’s near-term future solar irradiance
based on satellite observations, which we feed to a regression model
trained on smaller site-speci.c solar data to provide near-term solar
photovoltaic (PV) forecasts that account for site-speci.c characteristics.
We evaluate our approach for di.erent coverage areas and
forecast horizons across 25 solar sites and show that it yields errors
close to that of a model using ground-truth observations
Extending intraday solar forecast horizons with deep generative models
Surface solar irradiance (SSI) plays a crucial role in tackling climate
change - as an abundant, non-fossil energy source, exploited primarily via
photovoltaic (PV) energy production. With the growing contribution of SSI to
total energy production, the stability of the latter is challenged by the
intermittent character of the former, arising primarily from cloud effects.
Mitigating this stability challenge requires accurate, uncertainty-aware, near
real-time, regional-scale SSI forecasts with lead times of minutes to a few
hours, enabling robust real-time energy grid management. State-of-the-art
nowcasting methods typically meet only some of these requirements. Here we
present SHADECast, a deep generative diffusion model for the probabilistic
spatiotemporal nowcasting of SSI, conditioned on deterministic aspects of cloud
evolution to guide the probabilistic ensemble forecast, and based on near
real-time satellite data. We demonstrate that SHADECast provides improved
forecast quality, reliability, and accuracy in different weather scenarios. Our
model produces realistic and spatiotemporally consistent predictions
outperforming the state of the art by 15% in the continuous ranked probability
score (CRPS) over different regions up to 512 km x 512 km with lead times of
15-120 min. Conditioning the ensemble generation on deterministic forecasts
improves reliability and performance by more than 7% on CRPS. Our approach
empowers grid operators and energy traders to make informed decisions, ensuring
stability and facilitating the seamless integration of PV energy across
multiple locations simultaneously
Deep Learning Techniques in Extreme Weather Events: A Review
Extreme weather events pose significant challenges, thereby demanding
techniques for accurate analysis and precise forecasting to mitigate its
impact. In recent years, deep learning techniques have emerged as a promising
approach for weather forecasting and understanding the dynamics of extreme
weather events. This review aims to provide a comprehensive overview of the
state-of-the-art deep learning in the field. We explore the utilization of deep
learning architectures, across various aspects of weather prediction such as
thunderstorm, lightning, precipitation, drought, heatwave, cold waves and
tropical cyclones. We highlight the potential of deep learning, such as its
ability to capture complex patterns and non-linear relationships. Additionally,
we discuss the limitations of current approaches and highlight future
directions for advancements in the field of meteorology. The insights gained
from this systematic review are crucial for the scientific community to make
informed decisions and mitigate the impacts of extreme weather events
AI-generated Content for Various Data Modalities: A Survey
AI-generated content (AIGC) methods aim to produce text, images, videos, 3D
assets, and other media using AI algorithms. Due to its wide range of
applications and the demonstrated potential of recent works, AIGC developments
have been attracting lots of attention recently, and AIGC methods have been
developed for various data modalities, such as image, video, text, 3D shape (as
voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human
avatar (body and head), 3D motion, and audio -- each presenting different
characteristics and challenges. Furthermore, there have also been many
significant developments in cross-modality AIGC methods, where generative
methods can receive conditioning input in one modality and produce outputs in
another. Examples include going from various modalities to image, video, 3D
shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar),
and audio modalities. In this paper, we provide a comprehensive review of AIGC
methods across different data modalities, including both single-modality and
cross-modality methods, highlighting the various challenges, representative
works, and recent technical directions in each setting. We also survey the
representative datasets throughout the modalities, and present comparative
results for various modalities. Moreover, we also discuss the challenges and
potential future research directions
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