The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. The Random Forest Machine Learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky-camera images and 7 from the ceilometer. All rights reserved.Ī methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. ![]() Given the electricity production of the similar plant of Crescent Dunes, one third of the planned, despite more than double the cost, it has been impossible to secure the investments needed even at a cost of AU$ 158/MWh. In a state with peak power prices already up to AU$14,000/MWh, the peak power costs are indeed expected to rise further, if the PPA does not specify any minimum amount of electricity to be produced. The larger costs are due to the large‐scale generation certificates (LGCs), valued at around AU$80/MWh, the low interest loan of 110m$AU, and the indirect costs of the larger share of intermittent and unreliable electricity. The costs of the electricity produced by this technology is larger than the AU$ 78/MWh of the power purchase agreement (PPA). The dispatchability of this electricity production is also less than the expected. For a 135 MW net nominal capacity, the likely electricity production is less than the planned 495 GWh/year, corresponding to a capacity factor (CF) of 42%. The costs and electricity production of concentrated solar power (CSP) solar tower (ST) with molten salt (MS) thermal energy storage (TES) technology are here analyzed for the latest, recently dismissed, Aurora project. The variability classification method allows the comparison of different project sites in a statistical and automatic manner to quantify short-term variability impacts on solar power production. Up to 77 % of all class members are identified correctly by this automatic scheme. Variability indices as previously published or newly suggested are used as classifiers to detect the class members automatically. Each variability class is represented by 16 to 63 members. They combine high, medium, and low irradiance conditions with small, medium, and large scale variations from one minute to the next minute.A reference data base of 333 individual hours with ground-based 1 minute DNI observations was created by expert review from one year of observations at the BSRN station in Carpentras, France. Eight variability classes are defined for the 1 minute resolved direct normal irradiance (DNI) variability inside an hour. Variability of solar surface irradiances in the 1 minute range is of interest especially for solar energy applications. Examples of the use of these statistical results are presented to better understand the type of sky patterns at the location. In the main section, the statistics at the pixel location and in a 49x49 pixel zone around the point are described for a one year time series. Together with state of the art solar irradiation estimations, these statistical results can be used to determine several important factors for the choice of the best suited solar technology to use. ![]() Based on a long term result of APOLLO at a given point, we introduce a new use of this data, the APOLLO Cloud Product Statistics, to determine the typical cloud situations and spatio-temporal patterns at the location of interest. The APOLLO (AVHRR Processing scheme Over cLouds Land and Ocean, originally developed for the AVHRR instrument) methodology delivers cloud mask, cloud classification, cloud optical depth, and cloud top temperature as cloud physical parameters for all MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) pixels with a temporal resolution of 15 minutes during daytime since 1st February 2004. As the clouds are one of the main influencing parameters to the solar irradiation, this additional information can be very valuable to understand location-dependent characteristics when selecting a solar generator's location and to decide on the type of technology most appropriate for the site. In addition to this, it is possible to obtain from long term satellite images a statistical description of the clouds in the zone of interest. In order to assess the potential electricity production of a solar plant, industry usually uses long term time series of irradiation data.
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