Preprint Article Version 1 This version is not peer-reviewed

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Version 1 : Received: 27 April 2018 / Approved: 28 April 2018 / Online: 28 April 2018 (11:32:20 CEST)

A peer-reviewed article of this Preprint also exists.

Urbich, I.; Bendix, J.; Müller, R. A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo. Remote Sens. 2018, 10, 955. Urbich, I.; Bendix, J.; Müller, R. A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo. Remote Sens. 2018, 10, 955.

Journal reference: Remote Sens. 2018, 10, 955
DOI: 10.3390/rs10060955

Abstract

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05°. In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

Subject Areas

effective cloud albedo; solar surface irradiance; optical flow; cloud motion vectors; renewable energies

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