Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power.
@cite_11 proposed a model for automatically creating site-specific prediction models for solar power generation using weather forecast data as input to the model. They used observational solar intensity data from an extended weather station rooftop deployment to build the model. Their study compared the performance of Linear least squares regression and support vector machines using linear kernel, polynomial kernel, and RBF kernel and found the SVM with RBM kernel to be the most accurate. @cite_5 attempted to forecast PV power by developing an algorithm based on rainfall patterns in the past 5 years' data. They clustered this dataset and ultimately matched the same date over 5 years to find a pattern.
@cite_5 @cite_11
1975626798 2013120098
In this paper, we develop an algorithm for PV(Photo Voltaic) generation forecasting on the day before. In this algorithm, we build database of solar radiation patterns by analyzing past 5 years data from the weather center but deal with February data only. Therefore, we can estimate the amount of PV power generation forecasting in February from this algorithm. This algorithm will facilitate the renewable energy development and give useful information for RES providers to manage their RES power generation schedule. We hope our proposal could help the PV module providers effectively manage their PV generation plan. A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27 more accurate for our site than existing forecast-based models.