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This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values. [ABSTRACT FROM AUTHOR]

Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems. [Display omitted] • Classification of sky conditions. • Merging different predicted classes of sky conditions to reduce the prediction error. • Inclusion of optical flow to incorporate temporal information. • Classification reduced the amount of data needed to train the models. [ABSTRACT FROM AUTHOR]

• Blending of all sky imager, persistence, and satellite based nowcasts to a hybrid model for improved high-resolution nowcasting. • Novel image-based irradiance retrieval for all sky imager (ASI) model using machine learning which clearly outperforms established statistical ASI model. • Training and validation on quality-controlled data from a network of spatially distributed irradiance measuring stations. • Analysis under different meteorological conditions using variability of clear-sky index. High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts). In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy. ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10 k m around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods. Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT ≤ 11 m i n ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5 - 13 % over the respective optimal individual method. [ABSTRACT FROM AUTHOR]

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants' location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky. [ABSTRACT FROM AUTHOR]

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.

In this work, we propose a system to track the clouds and predict relevant events based on all-sky images. To deal with the nature of variable appearance of clouds, we use clusters of feature points to perform tracking. We propose an enhanced clustering algorithm that does not require prior knowledge of number of clusters. The proposed clustering algorithm can successfully separate feature points into groups with reasonable sizes and ranges. In the tracking process, merging and splitting of clouds are handled via checking matched pairs of feature points among different clusters. Afterwards, the tracking information is utilized to predict if the sun will be covered or obscured by clouds within the prediction horizon. Features are extracted from the tracked feature points and a Markov chain model is designed to perform ramp-down event prediction. The obscuration and ramp-down events have an important impact on solar irradiance. The experiments have shown that the proposed system can substantially enhance the accuracy of solar irradiance nowcasting on a challenging dataset. [ABSTRACT FROM AUTHOR]