Jan Kühnert

Chapter 11 – Satellite-Based Irradiance and Power Forecasting for the German Energy Market

Irradiance forecasts are fundamental to the prediction of power production from photovoltaic (PV) plants. Prediction is necessary to perform effective balancing of electricity demand and the variable and weather-dependent supply. Images from Meteosat Second Generation (MSG) satellites provide valuable information for forecasting clouds and solar irradiance several hours ahead using cloud-motion vectors (CMV). In this chapter we present our approach to deriving irradiance information from MSG images and to predicting the cloud situation by applying CMV. An evaluation of the irradiance forecast for single sites and regional averages on the basis of a 1 y dataset is presented. The CMV forecast shows superior performance in comparison to other methods such as numerical weather prediction (NWP) up to 5 h ahead. Additionally, an introduction to PV-power-prediction techniques is presented.

Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index

The cloud index is a key parameter of the Heliosat method. This method is widely used to calculate solar irradiance on the Earth’s surface from Meteosat visible channel images. Moreover, cloud index images are the basis of short-term forecasting of solar irradiance and photovoltaic power production. For this purpose, cloud motion vectors are derived from consecutive images, and the motion of clouds is extrapolated to obtain forecasted cloud index images. The cloud index calculation is restricted to the daylight hours, as long as SEVIRI HR-VIS images are used. Hence, this forecast method cannot be used before sunrise. In this paper, a method is introduced that can be utilized a few hours before sunrise. The cloud information is gained from the brightness temperature difference (BTD) of the 10.8 µm and 3.9 µm SEVIRI infrared channels. A statistical relation is developed to assign a cloud index value to either the BTD or the brightness temperature T10:8, depending on the cloud class to which the pixel belongs (fog and low stratus, clouds with temperatures less than 232 K, other clouds). Images are composed of regular HR-VIS cloud index values that are used to the east of the terminator and of nighttime BTD-derived cloud index values used to the west of the terminator, where the Sun has not yet risen. The motion vector algorithm is applied to the images and delivers a forecast of irradiance at sunrise and in the morning. The forecasted irradiance is validated with ground measurements of global horizontal irradiance, and the advantage of the new approach is shown. The RMSE of forecasted irradiance based on the presented nighttime cloud index for the morning hours is between 3 and 70 W/m2, depending on the time of day. This is an improvement against the previous precision range of the forecast based on the daytime cloud index between 70 and 85 W/m2.

Overview of Irradiance and Photovoltaic Power Prediction

Power generation from solar and wind energy systems is highly variable due to its dependence on meteorological conditions. With the constantly increasing contribution of photovoltaic (PV) power to the electricity mix, reliable predictions of the expected PV power production are getting more and more important as a basis for management and operation strategies. We give an overview of different approaches for solar irradiance and PV power prediction, including numerical weather predictions for forecast horizons of several days, very short-term forecasts based on the detection of cloud motion in satellite or ground-based sky images, and statistical methods to optimize and combine different data sources as well as methods for PV simulation and upscaling to regional PV power predictions. Evaluation results for selected irradiance and power prediction schemes show the benefit of different approaches for different timescales.

Correction: Hammer, J., et al. Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index. Remote Sens. 2015, 7, 9070–9090

Due to an oversight by the authors, the following correction is necessary in this publication [1].[...]