scholarly journals Analysis of ARMA Solar Forecasting Models Using Ground Measurements and Satellite Images

2019 ◽  
Author(s):  
Franco Marchesoni-Acland ◽  
Philippe Lauret ◽  
Alvaro Gomez ◽  
Rodrigo Alonso-Suarez
Keyword(s):  
Satellite Images ◽  
Solar Forecasting ◽  
Forecasting Models

Review of onsite temperature and solar forecasting models to enable better building design and operations

2021 ◽  
Vol 14 (4) ◽  
pp. 885-907
Author(s):  
Bing Dong ◽  
Reisa Widjaja ◽  
Wenbo Wu ◽  
Zhi Zhou
Keyword(s):  
Building Design ◽  
Solar Forecasting ◽  
Forecasting Models

A Novel Metric for Evaluation of Solar Forecasting Models

2011 ◽  
Author(s):  
Ricardo Marquez ◽  
Carlos F. M. Coimbra
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Correlation Coefficients ◽  
Solar Variability ◽  
Solar Forecasting ◽  
Model Quality ◽  
Quality Metric ◽  
Forecasting Models ◽  
Forecasting Error ◽  
Irradiance Data

This work presents an alternative, time-window invariant metric for evaluating the quality of solar forecasting models. Conventional approaches use statistical quantities such as the root-mean-square-error and/or the correlation coefficients to evaluate model quality. The straightforward use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of the time-series included in the solar irradiance data. In contrast, the quality metric proposed here, which is defined as the ratio of solar uncertainty to solar variability, compares forecasting error with solar variability directly. By making the forecasting error to variability comparisons for different time windows, we show that this ratio is essentially a statistical invariant for each forecasting model employed, i. e., the ratio is preserved for widely different time horizons.

scholarly journals Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2216
Author(s):  
Myeongchan Oh ◽  
Chang Ki Kim ◽  
Boyoung Kim ◽  
Changyeol Yun ◽  
Yong-Heack Kang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Motion Vector ◽  
Satellite Image ◽  
Skill Score ◽  
Solar Forecasting ◽  
Short Term ◽  
Forecast Horizon ◽  
Forecasting Models ◽  
Optimized Model

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

scholarly journals Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Energy ◽  
2015 ◽  
Vol 90 ◽  
pp. 671-679 ◽  
Author(s):  
Cyril Voyant ◽  
Ted Soubdhan ◽  
Philippe Lauret ◽  
Mathieu David ◽  
Marc Muselli
Keyword(s):  
A Priori ◽  
Solar Forecasting ◽  
Statistical Parameters ◽  
Forecasting Models

Proposed Metric for Evaluation of Solar Forecasting Models

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Ricardo Marquez ◽  
Carlos F. M. Coimbra
Keyword(s):  
Time Windows ◽  
Correlation Coefficients ◽  
Forecast Model ◽  
Solar Variability ◽  
Time Averaging ◽  
Solar Forecasting ◽  
Model Quality ◽  
Forecasting Models ◽  
Forecasting Error ◽  
Persistence Model

This work presents an alternative metric for evaluating the quality of solar forecasting models. Some conventional approaches use quantities such as the root-mean-square-error (RMSE) and/or correlation coefficients to evaluate model quality. The direct use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of the time-series for the solar irradiance data. In contrast, the quality metric proposed here, which is defined as the ratio of solar uncertainty to solar variability, compares the forecasting error with the solar variability directly. By making the forecasting error to variability comparisons for different time windows, we show that this ratio is essentially a statistical invariant for each forecast model employed, i.e., the ratio is preserved for widely different time horizons when the same time averaging periods are used, and therefore provides a robust way to compare solar forecasting skills. We employ the proposed metric to evaluate two new forecasting models proposed here, and compare their performances with a persistence model.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

Sign in / Sign up

Export Citation Format

Share Document