A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

Name: A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
Creator: Zenodo
Published: 2019-06-25T19:47:31+08:00

Zenodo2020-07-29 更新2026-05-28 收录

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https://zenodo.org/record/2826938 数据链接链接失效反馈

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Description This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. We also include sample codes of baseline models for benchmarking of more elaborated models. Data usage The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper: Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494 Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data. Sample code As part of the data release, we are also including the sample code written in Python 3. The preprocessed data used in the scripts are also provided. The code can be used to reproduce the results presented in this work and as a starting point for future studies. Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. Units All time stamps are in UTC (YYYY-MM-DD HH:MM:SS). All irradiance and weather data are in SI units. Sky image features are derived from 8-bit RGB (256 color levels) data. Satellite images are derived from 8-bit gray-scale (256 color levels) data. Missing data The string "NAN" indicates missing data File formats All time series data files as in CSV (comma separated values) Images are given in tar.bz2 files Files Folsom_irradiance.csv Primary One-minute GHI, DNI, and DHI data. Folsom_weather.csv Primary One-minute weather data. Folsom_sky_images_{YEAR}.tar.bz2 Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2. Folsom_NAM_lat{LAT}_lon{LON}.csv Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. Folsom_sky_image_features.csv Secondary Features derived from the sky images. Folsom_satellite.csv Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. Irradiance_features_{horizon}.csv Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). Sky_image_features_intra-hour.csv Secondary Sky image features for the intra-hour forecasting issuing times. Sat_image_features_intra-day.csv Secondary Satellite image features for the intra-day forecasting issuing times. NAM_nearest_node_day-ahead.csv Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting. Target_{horizon}.csv Secondary Target data for the different forecasting horizons. Forecast_{horizon}.py Code Python script used to create the forecasts for the different horizons. Postprocess.py Code Python script used to compute the error metric for all the forecasts.

数据集描述 本仓库包含一套完整的太阳辐照度、成像与太阳预测相关数据集。 本次发布的目标是为科研社区提供标准化的太阳与气象数据集，以加速太阳预测方法的开发与基准测试。 数据集包含2014年至2016年三年间、经过质量控制的1分钟分辨率地面实测总水平辐照度（global horizontal irradiance, GHI）与直接法向辐照度（direct normal irradiance, DNI）数据，采集地点位于美国加利福尼亚州。 此外，本数据集还提供常用外生变量的重叠数据，包括天空图像、卫星影像、数值天气预报（Numerical Weather Prediction, NWP）产品及气象观测数据。 我们还提供了基准模型的示例代码，用于支撑更复杂模型的基准测试工作。 数据使用须知 本数据集及示例代码仅可用于科研与开发用途，使用时需明确引用以下论文： Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. 用于太阳预测方法快速开发与基准测试的完整数据集. 《可再生与可持续能源期刊》(Journal of Renewable and Sustainable Energy) 11, 036102. https://doi.org/10.1063/1.5094494 尽管本团队已尽最大努力确保数据质量，但数据作者与发布方不对数据的适用性承担任何明示或默示的担保与责任。 示例代码 作为本次数据发布的一部分，我们还提供了基于Python 3编写的示例代码。 脚本中使用的预处理数据也一并提供。 该代码可用于复现本文的实验结果，也可作为后续研究的基础框架。 除标准科学计算Python包（numpy、scipy与matplotlib）外，本代码还依赖pandas实现时序运算、pvlib完成常见太阳相关任务，以及scikit-learn构建机器学习（Machine Learning）模型。 所有依赖的Python包均可在Mac、Linux及Windows系统上获取，并可通过pip等工具完成安装。 单位说明 所有时间戳均采用协调世界时（UTC），格式为YYYY-MM-DD HH:MM:SS。 所有辐照度与气象数据均采用国际单位制（SI）。 天空图像特征源自8位RGB（256色阶）数据。 卫星影像源自8位灰度（256色阶）数据。 缺失数据标识 字符串"NAN"表示缺失数据。 文件格式 所有时序数据文件均采用CSV（逗号分隔值）格式。 图像文件以tar.bz2格式压缩打包。 文件清单 Folsom_irradiance.csv 核心文件包含1分钟分辨率的总水平辐照度（GHI）、直接法向辐照度（DNI）及散射水平辐照度（diffuse horizontal irradiance, DHI）数据。 Folsom_weather.csv 核心文件包含1分钟分辨率的气象观测数据。 Folsom_sky_images_{YEAR}.tar.bz2 核心文件包含2014、2015及2016年日间以1分钟间隔采集的天空图像，以bz2格式压缩打包。 Folsom_NAM_lat{LAT}_lon{LON}.csv 核心文件包含距目标地点最近的4个节点的北美国数值天气预报模式（North American Mesoscale Model, NAM）预报产品。{LAT}与{LON}需替换为论文表I中列出的对应节点坐标。 Folsom_sky_image_features.csv 辅助文件由天空图像提取得到的特征数据。 Folsom_satellite.csv 辅助文件以目标地点为中心的10×10像素GOES-15卫星影像数据。 Irradiance_features_{horizon}.csv 辅助文件对应不同预测时长的辐照度特征数据（{horizon} 可取值为小时内（intra-hour）、日内（intra-day）及日前（day-ahead））。 Sky_image_features_intra-hour.csv 辅助文件针对小时内预测发布时刻的天空图像特征数据。 Sat_image_features_intra-day.csv 辅助文件针对日内预测发布时刻的卫星图像特征数据。 NAM_nearest_node_day-ahead.csv 辅助文件针对目标地点最近节点的NAM预报数据（包含采用DISC算法计算得到的GHI、DNI及总云量），用于日前预测任务。 Target_{horizon}.csv 辅助文件对应不同预测时长的目标标签数据。 Forecast_{horizon}.py 代码文件用于生成不同预测时长下的预报结果的Python脚本。 Postprocess.py 代码文件用于计算所有预报结果的误差指标的Python脚本。

提供机构：

Zenodo

创建时间：

2019-06-25