Fundamentals: Quantities, Definitions, and Units

Polo, Jesús; Martín-Pomares, Luis; Gueymard, Christian A.; Balenzategui, J.L.; Fabero, F.; Silva, José P.

doi:10.1007/978-3-319-97484-2_1

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Similarly, the ceiling-aggregation scheme stamps the aggregated data with the last timestamp of an interval, and the round-aggregation scheme collapses the data to the center timestamp of an interval. Experimental irradiance data may follow any one of these conventions (Polo et al, 2019), which complicates matters. It is obvious that inappropriate data aggregation creates temporal misalignment between different datasets.…”

Section: Some Seemingly Trivial Implementation Issuesmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

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The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

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Section: Some Seemingly Trivial Implementation Issuesmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

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“…Radiative transfer that takes place in the atmosphere leads to the high spatial and temporal variability of solar radiation along the planet. This variability, which has important consequences in both climatic studies and in solar system performance analysis, has promoted the effort in improving the modeling capabilities for the determination of the solar irradiance components [1]. In the absence of clouds, the models for estimating solar irradiance are denoted as clear sky models, in which the accuracy mostly resides on the detail and quality knowledge of the atmospheric components that act as attenuators of the solar radiation traversing the atmosphere [2,3].…”

Section: Overview Of the Issue: Solar Radiation Modeling And Remote Smentioning

confidence: 99%

Editorial for the Special Issue “Solar Radiation, Modeling, and Remote Sensing”

Kaskaoutis

Polo

2019

Remote Sensing

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Surface-solar radiation is of vital importance for life on Earth, radiation–energy balance, photosynthesis, and photochemical reactions, meteorological and climatic conditions, and the water cycle. Solar radiation measurements are growing in quality and density but they are still scarce enough to properly explain the spatial and temporal variability. As a consequence, great efforts are still being devoted to improving modeling and retrievals of solar radiation data. This Special Issue reviews techniques for solar radiation modeling and remote sensing using satellite and advanced statistical techniques for solar radiation. Satellite remote sensing of solar radiation provides better spatial coverage, and various methods have been presented on this issue covering several aspects: updated models for solar radiation modeling under clear sky conditions, new approaches for retrieving solar radiation from satellite imagery and validation against ground data, forecasting solar radiation, and modeling photosynthetically active radiation.

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“…The nowcasting time horizon, the central topic of the present work, is crucial in solving grid stability issues and optimizing the microgrid's energy management systems [15]. Moreover, it is vital in regulating the electricity market whenever a solar power production facility is connected to the electrical grid [16]. In nowcasting, the object of the solar forecast is the solar irradiance, which directly influences the power produced by PV modules.…”

Section: Introductionmentioning

confidence: 98%

Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

et al. 2022

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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.

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Fundamentals: Quantities, Definitions, and Units

Cited by 4 publications

References 16 publications

Verification of deterministic solar forecasts

Verification of deterministic solar forecasts

Editorial for the Special Issue “Solar Radiation, Modeling, and Remote Sensing”

Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

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