Modelling the Swedish wind power production using MERRA reanalysis data

Olauson, Jon; Bergkvist, Magnus

doi:10.1016/j.renene.2014.11.085

Cited by 122 publications

(91 citation statements)

References 15 publications

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“…A modelling of Swedish wind power production also using MERRA reanalysis data got an RMSE of 3.8% [18]. Two factors explaining the good results for the last study were the …”

Section: Cosmo(j)mentioning

confidence: 76%

Developing a wind and solar power data model for Europe with high spatial-temporal resolution

Graabak

Svendsen

Korpås

2016

2016 51st International Universities Power Engineering Conference (UPEC)

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Abstract-This paper describes a wind and solar power production model for Europe based on the numerical weather prediction model COSMO-EU. The COSMO-EU model has hourly time resolution and a spatial resolution of 7 km x 7 km for Europe. The model is validated against power production information from the system operators in Denmark, Germany and Spain. Mean Average Error (MAE) (hourly error averaged for a year) relative to the wind installed capacity is in the range 4.9% -5.9% for wind power production and 2.4%-5.5% for PV (photovoltaic) power production. Root Mean Square Error is in the range 6.2%-7.6% and 4.5%-9.3% for wind and PV power production respectively. The results are compared with similar modelling based on wind and radiation data from the NCEP reanalysis model. This model has six hourly time resolution for wind resources and daily resolution for radiation data. Modelling of wind power production in Denmark, German and Spain has a MAE in the range 5.6%-8.5% and solar PV production 4.9%-6.4% for the NCEP reanalysis model. Index Terms-Wind power generation, solar power generation, power system simulation, power system planning.

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“…A modelling of Swedish wind power production also using MERRA reanalysis data got an RMSE of 3.8% [18]. Two factors explaining the good results for the last study were the …”

Section: Cosmo(j)mentioning

confidence: 76%

Developing a wind and solar power data model for Europe with high spatial-temporal resolution

Graabak

Svendsen

Korpås

2016

2016 51st International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

show abstract

“…This reanalysis dataset has been widely used to assess the integration of renewable energies [6,41,42] because of its time resolution of 1 hour in a spatial grid with 0.5 o latitude by 0.625 o longitude resolution. MERRA2 provides short wave ground solar irradiance and top of the atmosphere short wave irradiance; and wind speed is available at 2, 10 and 50 m above the ground.…”

Section: Methodsmentioning

confidence: 99%

Compatibility of wind and solar energy with electricity demand in Argentina

Томас¹,

Bianchi²,

Solarte³

et al. 2018

Preprint

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One of the greatest obstacles in the exploitation of wind and solar resources is the uncertainty in their availability, usually known as intermittency effects. These effects can be greatly diminished by combining wind and solar resources from different locations. In this article we study the temporal variability of solar irradiance and wind speeds in Argentina, focusing on current projects and those included in GENREN and RENOVAR tenders. We also converted two year wind speeds and irradiance time series into power production, analyzing its compatibility with demand. Finally we numerically search the best distribution of additional capacity that minimizes a basic measure of the intermittency effect. ResumenLa falta de certeza en la disponibilidad es uno de los mayores obstáculos que se presentan a la hora de aprovechar las energías eólica y solar. Esta característica, usualmente conocida como efectos de intermitencia, puede ser mitigada integrando ambas fuentes y en distintos sitios. En este trabajo estudiamos la variabilidad temporal del recurso eólico y solar en Argentina, enfocándonos en los proyectos ofrecidos en las licitaciones GENREN y REN-OVAR. Luego convertimos las series temporales horarias de viento y sol en potencia, durante el perído 2015-2016, * tguozden@unrn.edu.ar † ebianchi@unrn.edu.ar estudiando como hubiera sido el recurso de los parques eólicos y solares operativos y próximos a entrar en servicio. Porúltimo analizamos la mejor distribución de potencia adicional a la proyectada tal que su impacto sobre la intermitencia sea mínimo.

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“…Many other researchers concentrated only on the change regularity of the annual electricity consumption trend. Equations are often used to simulate the annual electricity consumption curve and obtain the forecasting results by trend extrapolation [6][7][8]. These methods can obtain the "natural" change results of future electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

A Small‐Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

Meng¹,

Fu²,

Shi³

et al. 2017

Mathematical Problems in Engineering

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Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.

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Modelling the Swedish wind power production using MERRA reanalysis data

Cited by 122 publications

References 15 publications

Developing a wind and solar power data model for Europe with high spatial-temporal resolution

Developing a wind and solar power data model for Europe with high spatial-temporal resolution

Compatibility of wind and solar energy with electricity demand in Argentina

A Small‐Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

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