An international research team has developed a new approach for solar power forecasting that combines neural networks and pattern sequences for the first time. The performance of the new Pattern Sequence Neural Network (PSNN) was tested on an Australian data set that includes information from two years of forecasts. It can be used with different clustering and cluster-sequence extraction algorithms, and can be applied to multiple related time sequences
The algorithm is said to be able to examine the relationship between weather forecast data and the projection of electric circuit parameters. Through this innovation, Purdue University researchers claim they can interpret the routinely collected maximum power point (MPP) time-series data, to assess the time-dependent “health” of installed solar modules.
The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.