.png){kind=icon}
返回
Verification of deterministic solar forecasts
.png){kind=icon}
OA
AI
DOI：10.1016/j.solener.2020.04.019.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 sub-domain 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.
Keyword:
Solar forecasting
Measure-oriented forecast verification
Distribution-oriented forecast verification
Skill score
Combination of climatology and persistence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
6.2W
机构
查看更多机构
引用论文
Short-term solar irradiance forecasting using exponential smoothing state space model.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏Calculating the financial value of a concentrated solar thermal plant operated using direct normal irradiance forecastsSOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏Clear-sky irradiance predictions for solar resource mapping and large-scale applications: Improved validation methodology and detailed performance analysis of 18 broadband radiative modelsSOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏Quantifying the amplified bias of PV system simulations due to uncertainties in solar radiation estimatesSOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏分享收藏 查看更多内容