The weather in the coastal area of Southern California is highly influenced by the occurrence of low marine layer clouds during the summer season. An extensive marine layer system forms over the Pacific Ocean and is frequently propelled inland, causing overcast meteorological conditions on distributed and utility scale solar farms, which in turn cause ramping events and fluctuations in power amplitude and frequency.
The formation, movement and behavior of marine layer clouds is modeled as a multivariable system that is governed by factors such as local topography, pressure gradients, coastal winds, solar radiation, etc. Multiple influences on the development of the Marine Layer Clouds have been investigated and are discussed in this work (e.g. atmospheric pressure, subsidence inversion height and strength, temperature gradients, etc.). We identify the basic parameters that can be measured or forecasted and use them as inputs for implementation in a basic operational forecast system for the marine layer. This system will cover day-ahead forecasts as well as intra-day forecasts reaching from 30 minutes to 4 hours ahead.
To increase the overall accuracy of the forecast, these inputs are combined with different machine learning techniques to form a hybrid forecasting algorithm. A database containing 10 years of binary cloud information was created using GOES-West satellite images. This database contains 15000 images and is used for pattern recognition and classification, using the k-Nearest Neighbour (kNN) algorithm to build the forecast. Identification methods for changing patterns between the first two post-sunrise images are also explored.
Results indicate that a hybrid approach is effective in forecasting marine layer clouds in Southern California, and that this hybrid approach holds promise in the development of an operational solar forecasting system for the region.