A regime-based approach for integrating wind information in spatio-temporal solar forecasting models

2019 ◽  
Vol 11 (5) ◽  
pp. 056102
Author(s):  
R. Amaro e Silva ◽  
S. E. Haupt ◽  
M. C. Brito
Keyword(s):  
Solar Forecasting ◽  
Forecasting Models ◽  
Spatio Temporal

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 A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

2014 ◽  
Vol XL-2 ◽  
pp. 7-12 ◽  
Author(s):  
J. Haworth ◽  
T. Cheng
Keyword(s):  
Road Network ◽  
Model Building ◽  
Predictive Performance ◽  
Selection Methods ◽  
Large Dataset ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Parsimonious Models ◽  
Selection Operators ◽  
Spatio Temporal

Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London’s road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L<sub>1</sub> penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.

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.

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