Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting
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This article investigates how to infer multiple wind velocity fields using consecutive longwave infrared (IR) images of clouds to forecast the occlusion of the Sun by clouds.About:
This article is published in Applied Energy.The article was published on 2021-04-15 and is currently open access. It has received 18 citations till now. The article focuses on the topics: Wind speed & Solar irradiance.read more
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Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
TL;DR: In this paper , a study on data-driven probabilistic machine learning (ML) techniques and their real-time applications to smart energy systems and networks was conducted to highlight the urgency of this area of research.
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Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest
TL;DR: In this article , an end-to-end short-term forecasting model is proposed to take satellite images as inputs, and it can learn the cloud motion characteristics from stacked optical flow maps.
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Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy
Linfei Yin,Yun Xia Wu +1 more
TL;DR: In this paper , a mode-decomposition memory reinforcement network strategy is proposed to reduce the impact of random fluctuations and uncertainties on power systems, which combines the traditional methods and intelligent algorithms for smart generation control.
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Comparative analysis of methods for cloud segmentation in ground-based infrared images
TL;DR: In this paper, a comparison between discriminative and generative models for cloud segmentation is presented, where both unsupervised and supervised learning methods are evaluated using the j-statistic.
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Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning
TL;DR: Wang et al. as discussed by the authors proposed an innovative framework that integrates ground and satellite observations through deep learning to enhance PV output forecasts, where cloud motion patterns are captured from satellite observations using convolutional neural networks, and the long-range spatio-temporal cloud impacts on subsequent PV outputs are established by LSTM network.
References
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Interannual variability of the tropical tropopause derived from radiosonde data and NCEP reanalyses
TL;DR: In this article, the authors studied the interannual variability of the tropical Tropopause using long time series of radiosonde data, together with global tropopause analyses from the National Centers for Environmental Prediction (NCEP) reanalyses over 1957-1997.
MonographDOI
Physics and Chemistry of Clouds
Dennis Lamb,Johannes Verlinde +1 more
TL;DR: The Physics and Chemistry of Clouds (PCC) as mentioned in this paper is a textbook for advanced students in atmospheric science, meteorology, environmental sciences/engineering and atmospheric chemistry, which provides students with a quantitative yet approachable path to learning the inner workings of clouds.
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Electric Load Forecasting Based on Locally Weighted Support Vector Regression
TL;DR: A modified version of the support vector regression (SVR) is presented to solve the load forecasting problem and exhibits superior performance compare to that of LWR, local SVR, and other published models.
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Short-term forecasting of solar radiation: a statistical approach using satellite data
TL;DR: In this paper, the authors use satellite images as a data source for short-term forecasting of solar irradiance, which is an important issue for many fields of solar energy applications.
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Solar Power Prediction Based on Satellite Images and Support Vector Machine
TL;DR: In this article, a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme was proposed to forecast the motion vectors of clouds by utilizing satellite images of atmospheric motion vectors (AMVs).