Open AccessPosted Content
Forecasting solar radiation during dust storms using deep learning.
TLDR
In this paper, the authors deal with the analysis of solar radiation and power output of a rooftop photovoltaic plant during a dust storm and propose a forecasting methodology using deep learning network.Abstract:
Dust storms are common in arid zones on the earth and others planets such as Mars. The impact of dust storms on solar radiation has significant implications for solar power plants and autonomous vehicles powered by solar panels. This paper deals with the analysis of solar radiation and power output of a rooftop photovoltaic plant during a dust storm and proposes a forecasting methodology using deep learning network. The increased aerosol content due to dust storms increases the diffuse component of the solar radiation. This effect persists for a long duration and can impact the quality of forecasting of solar radiation. Deep learning networks that capture long range structure can improve the quality of solar radiation forecasting during dust storms. These results can help explain the sudden drop in power output of solar plants due to dust storms originating in another continent. They can shed light on mysterious cleaning events in autonomous vehicles powered by solar panels to be used in space missions.read more
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Journal Article
The impact of climate change on photovoltaic power generation in Europe
Sonia Jerez,Isabelle Tobin,Robert Vautard,Juan Pedro Montávez,Jose María López-Romero,Françoise Thais,Blanka Bartok,Ole Bøssing Christensen,Augustin Colette,Michel Déqué,Grigory Nikulin,Sven Kotlarski,Erik van Meijgaard,Claas Teichmann,Martin Wild +14 more
TL;DR: Climate change impacts on solar photovoltaic (PV) power in Europe using the recent EURO-CORDEX ensemble of high-resolution climate projections together with a PV power production model and assuming a well-developed European PV power fleet is evaluated.
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Nano-Iron Oxide-Ethylene Glycol-Water Nanofluid Based Photovoltaic Thermal (PV/T) System with Spiral Flow Absorber: An Energy and Exergy Analysis
Amged Al Ezzi,Miqdam T. Chaichan,Hasan Sh. Majdi,Ali H. Al-Waeli,Hussein A. Kazem,Kamaruzzaman Sopian,Mohammed A. Fayad,Hayder A. Dhahad,Talal Yusaf +8 more
TL;DR: In this article , two PV/T systems consisting of poly and monocrystalline PV panels were used, which are connected from the bottom by a heat exchanger consisting of a spiral tube through which a nanofluid circulates.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Generating Sequences With Recurrent Neural Networks
TL;DR: This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.
Posted Content
A Critical Review of Recurrent Neural Networks for Sequence Learning
TL;DR: The goal of this survey is to provide a selfcontained explication of the state of the art of recurrent neural networks together with a historical perspective and references to primary research.
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The physics of wind-blown sand and dust
TL;DR: The physics of aeolian saltation, the formation and development of sand dunes and ripples, the physics of dust aerosol emission, the weather phenomena that trigger dust storms, and the lifting of dust by dust devils and other small-scale vortices are reviewed.
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The physics of wind-blown sand and dust
TL;DR: In this article, an extensive review of the physics of wind-blown sand and dust on Earth and Mars is presented, including a review of aeolian saltation, the formation and development of sand dunes and ripples, dust aerosol emission, weather phenomena that trigger dust storms, and the lifting of dust by dust devils and other small-scale vortices.