Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates
TLDR
In this article , the authors present a compendium of references published since 2011 on spatio-temporal methods for global horizontal irradiance and photovoltaic generation, categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon).Abstract:
To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.read more
Citations
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Solar radiation forecasting with deep learning techniques integrating geostationary satellite images
TL;DR: In this paper , the authors proposed two distinct deep learning models, a 3D-CNN and a ConvLSTM, to forecast solar radiation in terms of GHI values, up to 6-h ahead with a temporal granularity of 15 min, over a test study area, the city of Turin, Piedmont, Italy.
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CyL-GHI: Global Horizontal Irradiance Dataset Containing 18 Years of Refined Data at 30-Min Granularity from 37 Stations Located in Castile and León (Spain)
TL;DR: In this article , the authors presented the methodology applied to introduce a large-scale, public, and solar irradiance dataset, CyL-GHI, containing refined data from 37 stations found within the Spanish region of Castile and León (Spanish: Castilla y León, or CyL), in addition to the data cleaning steps, the procedure also features steps that enable the addition of meteorological and geographical variables that complement the value of the initial data.
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Spatio-temporal reconciliation of solar forecasts
TL;DR: In this article , the spatio-temporal point forecast reconciliation approach is applied to generate nonnegative, fully coherent (both in space and time) forecasts for hierarchical photovoltaic (PV) power generation.
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Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
Gamal Alkawsi,Ammar Ahmed Alkahtani,Chen Chai Phing,Yahia Baashar,Luiz Fernando Capretz,Ali Q. Al-Shetwi,Sieh Kiong Tiong +6 more
TL;DR: In this article , the authors investigated LSTM models for forecasting solar energy by using time-series data, and they found that LSTMs outperformed the other standalone models despite their longer data training time requirement.
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A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation
TL;DR: In this article, the authors used predictive algorithms for the output power of a solar PV power generation system, which can be verified in the daily planning and operation of a smart grid system.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
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Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
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Review of photovoltaic power forecasting
J. Antonanzas,Natalia Osorio,Rodrigo Escobar,Ruben Urraca,Francisco Javier Martinez-de-Pison,F. Antonanzas-Torres +5 more
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.