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This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated against manually annotated images. Results show that SACI achieves overall classification accuracy higher than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance (GHI), 5-, 10-, and 15-min ahead of time. The performance of the ANN forecasting model is assessed in terms of common error statistics (mean bias and root-mean-square error) and in terms of forecasting skill over persistence. The model proposed in this work achieves forecasting skills above 14%, 18%, and 19% over the persistence forecast for 5-, 10-, and 15-min forecasts, respectively.

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Clear-sky modeling is of critical importance for the accurate determination of Direct Normal Irradiance (DNI), which is the relevant component of the solar irradiance for concentrated solar energy applications. Accurate clear-sky modeling of DNI is typically best achieved through the separate consideration of water vapor and aerosol concentrations in the atmosphere. Highly resolved temporal measurements of such quantities is typically not available unless a meteorological station is located in close proximity. When this type of data is not available, attenuating effects on the direct beam are modeled by Linke turbidity-equivalent factors, which can be obtained from broadband observations of DNI under cloudless skies. We present a novel algorithm that allows for a time-resolved estimation of the average daily Linke turbidity factor from ground-based DNI observations under cloudless skies. This requires a method of identifying clear-sky periods in the observational time series (in order to avoid cloud contamination) as well as a broadband turbidity-based clear-sky model for implicit turbidity calculations. While the method can be applied to the correction of historical clear-sky models for a given site, the true value lies in the forecasting of DNI under cloudless skies through the assumption of a persistence of average daily turbidity. This technique is applied at seven stations spread across the states of California, Washington, and Hawaii while using several years of data from 2010 to 2014. Performance of the forecast is evaluated by way of the relative Root Mean Square Error (rRMSE) and relative Mean Bias Error (rMBE), both as a function of solar zenith angle, and benchmarked against monthly climatologies of turbidity information. Results suggest that rRMSE and rMBE of the method are typically smaller than 5% for both historical and forecasted CSMs, which compare favorably against the 10-20% range that is typical for monthly climatologies.

Clouds significantly attenuate ground-level solar irradiance causing substantial reduction in photovoltaic power output capacity. However, partly cloudy skies may lead to temporary enhancement of local Global Horizontal Irradiance (GHI) above the clearsky ceiling and, at times, the extraterrestrial irradiance. Such enhancements are referred to here as Cloud Enhancement Events (CEEs). In this work we study these CEEs and assess quantitatively the occurrence of resulting coherent Ramp Rates (RRs). We analyze a full year of ground irradiance data recorded at the University of California, Merced, as well as nearly five months of