Predicting solar irradiance with all-sky image features via regression
TL;DR: The experiments have shown that the designed clearness index prediction mechanism yields better prediction results than predicting solar irradiance directly, and could provide very useful information for grid operators to ensure greater efficiency of the renewable energy supply.
About: This article is published in Solar Energy.The article was published on 2013-11-01. It has received 105 citations till now. The article focuses on the topics: Solar irradiance & Irradiance.
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TL;DR: It is ascertained that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network.
223 citations
TL;DR: The state-of-the-art in the accuracy of solar resources forecasting is obtained by using results reported in 1705 accuracy tests reported in several geographic regions and the hybrid models have the best performance.
126 citations
TL;DR: A novel approach for feature selection based on a novel meta-heuristic, the Coral Reefs Optimization algorithm with Substrate Layer is introduced, able to combine different search mechanisms into a single algorithm, providing a global search procedure of high quality.
Abstract: This paper focuses on feature selection problems that arise in renewable energy applications. Feature selection is an important problem in machine learning, both in classification and regression problems. In renewable energy systems, feature selection appears related to prediction systems in the most important sources such as wind, solar and marine resources. The objective of the paper is twofold: first, a review of the most important prediction systems for renewable energy applications involving feature selection is carried out. Analysis and discussion of different feature selection problems in prediction systems are considered. We show that wrapper FSP approaches are those mostly used due to their higher performance. They include a diversity of algorithms, prevailing fast-training approaches. The lack of an uniform framework for FSP and the diversity of tackled problems impede a systematic assessment of the performance and properties of the applied methods. Thus, the simultaneously use of several global search mechanisms should be the preferred option. In a second part of the paper, we explore this possibility, by introducing a novel approach for feature selection based on a novel meta-heuristic, the Coral Reefs Optimization algorithm with Substrate Layer. This approach is able to combine different search mechanisms into a single algorithm, providing a global search procedure of high quality. We use an Extreme Learning Machine for prediction within this novel approach. The performance of the system is evaluated in a problem of wind speed prediction from numerical models input, using real data from a wind farm in Spain, where comparison with alternative regression algorithms is carried out. Improvements up to 20% in hourly and daily wind speed prediction are obtained with the proposed system versus the algorithms without the feature selection process considered.
98 citations
TL;DR: The proposed algorithm and its efficiency for selecting the best set of features from the WRF are analyzed and described, and the performance of the system with these different characteristics in a real problem of solar radiation prediction at Toledo's radiometric observatory has shown an excellent performance.
95 citations
TL;DR: A novel 3D-CNN method is proposed by processing multiple consecutive ground-based cloud images in order to extract cloud features including texture and temporal information that are then used to establish a DNI forecasting model.
95 citations
References
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Book•
[...]
02 Feb 2001
TL;DR: Computer Vision presents the necessary theory and techniques for students and practitioners who will work in fields where significant information must be extracted automatically from images, a useful resource book for professionals and a core text for both undergraduate and beginning graduate computer vision and imaging courses.
Abstract: From the Publisher:
Computer Vision presents the necessary theory and techniques for students and practitioners who will work in fields where significant information must be extracted automatically from images. It will be a useful resource automatically from images. It will be a useful resource book for professionals and a core text for both undergraduate and beginning graduate computer vision and imaging courses.
Features
Topics include image databases an virtual and augmented reality in addition to classical topics.
Offers a complete view of two real-world systems that use computer vision.
Contains applications from industry, medicine, land use, multimedia, and computer graphics.
Includes over 250 exercises and programming projects, 48 separately defined algorithms, and 360 figures.
The companion website features include image archive, sample
1,880 citations
TL;DR: In this paper, a step-by-step procedure for implementing an algorithm to calculate the solar zenith and azimuth angles in the period from the year −2000 to 6000, with uncertainties of ± 0.0003°.
1,053 citations
TL;DR: In this article, a composite time series of total solar irradiance spaceborne measurements is used to predict the sun's irradiance within 0.1% on average, as accurately as current measurements.
843 citations