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2. Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

Author

Betti, Alessandro, Blanc, Philippe, David, Mathieu, Saint-Drenan, Yves-Marie, Driesse, Anton, Freeman, Janine, Fritz, Rafael, Gueymard, Christian A., Habte, Aron, Huang, Jing, Kazantzidis, A., Kleissl, Jan, Köhler, Carmen, Landelius, Tomas, Lara-Fanego, Vicente, Lauret, Philippe, Lorenz, Elke, Martin, Luis, Mehos, Mark, Meyer, Richard, Meyers, Daryl, Pagh Nielsen, Kristian, Perez, Richard, Fernandez Peruchena, Carlos. M., Polo, Jesus, Renne, D., Ramirez, Lourdes, Remund, Jan, Ruiz Arias, J.A., Sengupta, Manajit, Silva, Manuel, Spieldenner, David, Stoffel, Thomas, Suri, Marcel, Wilbert, Stefan, Wilcox, Stephen, Vignola, Frank, Wang, Ping, Xie, Yu, and Zarzalejo, L.F.

Subjects
irradiance, solar energy, forecasting, resource assessment, energy meteorology
Published
2021

3. Applications for Solar Resource Products (Chapter 9)

Author

Höller, Robert, Pagh Nielsen, Kristian, Zarzalejo, L.F., Freeman, Janine, Gueymard, Christian A., Wilbert, Stefan, Silva, Manuel, Renne, David, Ramirez, Lourdes, Spieldenner, David, Fritz, Rafael, Mehos, Mark, Perez, Richard, and Habte, Aron

Subjects
Solar energy, forecasting, resource assessment, solar resource, energy meteorology
Published
2021

5. Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Author

Ukkonen, Peter, Pincus, Robert, Hogan, Robin J., Pagh Nielsen, Kristian, Kaas, Eigil, Ukkonen, Peter, Pincus, Robert, Hogan, Robin J., Pagh Nielsen, Kristian, and Kaas, Eigil

Abstract

Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In this work, neural networks (NNs) were developed to replace the gas optics computations in a modern radiation scheme (RTE+RRTMGP) by using carefully constructed models and training data. The NNs, implemented in Fortran and utilizing BLAS for batched inference, are faster by a factor of 1-6, depending on the software and hardware platforms. We combined the accelerated gas optics with a refactored radiative transfer solver, resulting in clear-sky longwave (shortwave) fluxes being 3.5 (1.8) faster to compute on an Intel platform. The accuracy, evaluated with benchmark line-by-line computations across a large range of atmospheric conditions, is very similar to the original scheme with errors in heating rates and top-of-atmosphere radiative forcings typically below 0.1 K day(-1) and 0.5 W m(-2), respectively. These results show that targeted machine learning, code restructuring techniques, and the use of numerical libraries can yield material gains in efficiency while retaining accuracy.

Published
2020

9. 2001 – 2010 Danish Design Reference Year:Reference Climate Dataset for Technical Dimensioning in Building, Construction and other Sectors

Author

Grunnet Wang, Peter, Scharling, Mikael, Pagh Nielsen, Kristian, Kern-Hansen, Claus, and Wittchen, Kim Bjarne

Subjects
reference year, climate data
Abstract

This report presents the Danish Design Reference Year based on observed data from 2001 – 2010. In various sectors - i.e. building and construction, energy, etc. - the climate and weather usually plays a part in a given project. The Danish Design Reference Year dataset is a collection of data series for eleven specific parameters, that each represents a typical year in Denmark. The uses of the dataset may vary from simulations to statistical analysis, graphical overviews etc.The Danish land areas have been sectionalized into five to six climatological zones depending on the parameter, each characterized by distinct diurnal and yearly variations. The dataset consists of observed data from one station located within and representing each zone.In addition to the complete Danish Design Reference Year dataset, a subset specifically selected to be used for energy performance calculations for obtaining a building permit is included.

Published
2013

10. Solar/electric heating systems for the future energy system

Author

Furbo, Simon, Dannemand, Mark, Perers, Bengt, Andersen, Elsa, Fan, Jianhua, Bacher, Peder, Madsen, Henrik, Halvgaard, Rasmus, Nielsen, Henrik Aalborg, Pagh Nielsen, Kristian, Lundholm , Sisse Camilla, Hansen Sass, Bent, Engberg Pedersen, Thomas, Nymann Rud, Jakob, and Harley Hansen, Kristian

Abstract

The project “Solar/electric heating systems in the future energy system” was carried out in the period 2008‐2013. The project partners were DTU Byg, DTU Informatics (now DTU Compute), DMI, ENFOR A/S and COWI A/S. The companies Ajva ApS, Ohmatex ApS and Innogie ApS worked together with the project partners in two connected projects in order to develop solar/electric heating systems for laboratory tests. The project was financed by the Danish Agency for Science, Technology and Innovation under the Danish Council for Strategic Research in the program Sustainable Energy and Environment. The DSF number of the project is 2104‐07‐0021/09‐063201/DSF. This report is the final report of the project.The aim of the project is to elucidate how individual heating units for single family houses are best designed in order to fit into the future energy system. The units are based on solar energy, electrical heating elements/heat pump, advanced heat storage tanks and advanced control systems.Heat is produced by solar collectors in sunny periods and by electrical heating elements/heat pump. The electrical heating elements/heat pump will be in operation in periods where the heat demand cannot be covered by solar energy. The aim is to use the auxiliary heating units when the electricity price is low, e.g.due to large electricity production by wind turbines.The unit is equipped with an advanced control system where the control of the auxiliary heating is based on forecasts of the electricity price, the heat demand and the solar energy production. Consequently, the control is based on weather forecasts.Three differently designed heating units are tested in a laboratory test facility. The systems are comparedon the basis of:- energy consumption for the auxiliary heating- energy cost for the auxiliary heating- net utilized solar energy

Published
2013

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