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time-series-9

Solar Irradiance Forecasting

3260 papers • 126 benchmarks • 313 datasets

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An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

Sakshi Mishra, Praveen Palanisamy•Mon May 06 2019

The predictions demonstrate that the proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature.

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Solar Irradiance Forecasting | State-of-the-Art

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Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images

Guillermo Terr'en-Serrano, Manel Mart'inez-Ram'on•Sat Dec 12 2020

Generative and discriminative models are comparable when preprocessing is applied to the infrared images and Markov Random Fields achieve the best performance in both unsupervised and supervised generative models.

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Detection of clouds in multiple wind velocity fields using ground-based infrared sky images

Guillermo Terr'en-Serrano, Manel Mart'inez-Ram'on•Thu May 06 2021

This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images using an innovative infrared sky imager mounted on a solar tracker and finds that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.

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Solar Irradiance Forecasting with Transformer Model

Jiri Pospíchal, Martin Kubovčík, Iveta Dirgová Luptáková•Thu Sep 01 2022

The Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems is extended by combining features based on their spatiotemporal properties in solar irradiance prediction, which gave better results than the directly competing methods.

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Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

Hyeon-Ju Jeon, Min-Woo Choi, O-Joun Lee•Tue Sep 20 2022

A novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models is proposed.

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Solar Irradiance Anticipative Transformer

T. Mercier, Tasmiat Rahman, Amin Sabet•Sun May 28 2023

The proposed anticipative transformer-based model effectively learns to attend only to relevant features in images in order to forecast irradiance, and captures long-range dependencies between sky images to achieve a forecasting skill of 21.45 % on a 15 minute ahead prediction.

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Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks

Zihao Wang, P. S. Pravin, Zhen Wu•Sun Jan 14 2024

This work introduces a novel network architecture, termed Input Convex Lipschitz Recurrent Neural Networks (ICLRNNs), which seamlessly integrates the benefits of convexity and Lipschitz continuity, enabling fast and robust neural network-based modeling and optimization.

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