Artificial Intelligence Techniques For Solar Irradiance And Pv Modeling And Forecasting PDF Download

The world's energy system will increasingly depend upon renewable energy sources, including solar power, due to the limitation of fossil fuel resources and their influence on global pollution and climate change. Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source. Changes in weather conditions, i.e. clouds, can cause rapid changes in solar power output, thus creating a challenge for utility companies to effectively use these renewable energy resources. The energy grid, which manages and distributes the energy, requires energy generation to meet the energy demand for an efficient system. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the energy load, direct power generation from utilities, define operating limits and create contingency plans. ISOs, RTOs and utilities will require solar irradiance forecasts to effectively and efficiently balance the energy grid as the penetration of solar power increases. This study presents multiple nonlinear forecasting techniques to predict both the magnitude of the solar irradiance and its expected variability.The temporal irradiance variability is forecast for the temporal standard deviation of the Global Horizontal Irradiance (GHI) at eight sites in the Sacramento Valley of California and the spatial irradiance variability is forecast for the standard deviation across those same sites. A model tree with a nearest neighbor option was trained to predict the irradiance variability. The resulting artificial intelligence model reduces the mean absolute error between 10% and 55% compared to using climatological average values of the temporal and spatial GHI standard deviation. A data denial experiment shows including surface weather observations improves forecasting skill by approximately 10%. These results indicate the model tree technique can be applied in real time to produce solar variability forecasts.iiiiv Next, a cloud regime-dependent short-range solar irradiance forecasting system isdeveloped to provide 15-min average clearness index forecasts for 15-min, 60-min, 120-min and 180-min lead-times. A k-means algorithm identifies the cloud regime based on surface weather observations and irradiance observations. Then, Artificial Neural Networks (ANNs) are trained to predict the clearness index. This regime-dependent system makes a more accurate deterministic forecast than a global ANN or clearness index persistence and produces more accurate predictions of expected irradiance variability than assuming climatological average variability.Lastly, regime-identification methods that also incorporate GOES-East satellite data both as inputs to the k-means regime algorithm and as predictors to the ANNs are explored. Several cloud-regime dependent short-range solar irradiance forecasting systems (RD-ANN) are tested to make 15-min average clearness index predictions for 15-min, 60-min, 120-min and 180-min forecast lead-times. The RD-ANN system that shows the lowest forecast error on independent test data classifies cloud regimes with a k-means algorithm based on a combination of surface weather observations, irradiance observations and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness index persistence predictions by 1.0%, 21.0%, 26.4% and 27.4% at the 15-min, 60-min, 120-min and 180-min forecast lead-times. Additionally, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than a smart persistence technique.Using statistical techniques allows for improved deterministic solar irradiance predictions as well as improved spatial and temporal solar irradiance variability predictions. The combination of deterministic predictions of irradiance and irradiance variability may offer utilityv companies and systems operators the necessary information to deliver services to clients on theevolving power grid.