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Predicting Solar Radiation using a Deep Neural Network
KTH, School of Information and Communication Technology (ICT).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Simulating the global climate in fine granularity is essential in climate science research. Current algorithms for computing climate models are based on mathematical models that are computationally expensive. Climate simulation runs can take days or months to execute on High Performance Computing (HPC) platforms. As such, the amount of computational resources determines the level of resolution for the simulations. If simulation time could be reduced without compromising model fidelity, higher resolution simulations would be possible leading to potentially new insights in climate science research. In this project, broadband radiative transfer modeling is examined, as this is an important part in climate simulators that takes around 30% to 50% time of a typical general circulation model. This thesis project presents a convolutional neural network (CNN) to model this most time consuming component. As a result, swift radiation prediction through the trained deep neural network achieves a 7x speedup compared to the calculation time of the original function. The average prediction error (MSE) is around 0.004 with 98.71% of accuracy.

Abstract [sv]

Högupplösta globala klimatsimuleringar är oumbärliga för klimatforskningen.De algoritmer som i dag används för att beräkna klimatmodeller baserar sig på matematiska modeller som är beräkningsmässigt tunga. Klimatsimuleringar kan ta dagar eller månader att utföra på superdator (HPC). På så vis begränsas detaljnivån av vilka datorresurser som finns tillgängliga. Om simuleringstiden kunde minskas utan att kompromissa på modellens riktighet skulle detaljrikedomen kunna ökas och nya insikter göras möjliga. Detta projekt undersöker Bredband Solstrålning modellering eftersom det är en betydande del av dagens klimatsimulationer och upptar mellan 30-50% av beräkningstiden i en typisk generell cirkulationsmodell (GCM). Denna uppsats presenterar ett neuralt faltningsnätverk som ersätter denna beräkningsintensiva del. Resultatet är en sju gångers uppsnabbning jämfört med den ursprungliga metoden. Genomsnittliga uppskattningsfelet är 0.004 med 98.71 procents noggrannhet.

Place, publisher, year, edition, pages
2017. , p. 66
Series
TRITA-ICT-EX ; 2017:105
Keywords [en]
Deep Learning; Climate Science Prediction; Regression; Convolutional Neural Network; Solar Radiation; Tensorflow.
Keywords [sv]
Djupinlärning; klimatprediktion; regression; neurala faltningsnätverk; solstrålning; Tensorflow.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-215715OAI: oai:DiVA.org:kth-215715DiVA, id: diva2:1149096
External cooperation
SICS - Swedish Institute of Computer Science
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Examiners
Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2018-01-13Bibliographically approved

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