Rye microgrid historical weather forecasts and stochastic scenarios

This datasets connects historical weather forecasts from the Norwegian Meteorological Institute (met.no) and historical observations from Rye microgrid (https://doi.org/10.5281/zenodo.4448894). Each csv file represents a historical weather forecast for approximately 60 hours ahead. Each csv-file also contains the corresponding observations in the same time interval. Finally, the files also contain load, wind generation and solar PV generation predicitons. The predictions are generated using gradient boosting. The predictions ending with "_ls" are based on least square. The predictions ending with "_quantile_i" represent a quantile prediction. For example, "wind_quantile_2" means that there is a 20% probability the wind will be less than this value. The gradient boosting predictor has been trained to predict the wind power, solar power and load using the explanatory variables below: Solar PV: Cloud area fraction, initial production, clear sky production and forecast look-ahead time Wind power: wind speed, wind direction, wind power converted from wind speed forecast, initial production and forecast look-ahead time Load: hour of day, month of year

本数据集整合了挪威气象研究所（Norwegian Meteorological Institute，met.no）提供的历史天气预报数据，以及Rye微电网的历史观测数据（数据DOI链接：https://doi.org/10.5281/zenodo.4448894）。每个CSV文件对应一份提前约60小时的历史天气预报，同时包含同一时间区间内的对应观测数据。此外，文件中还涵盖负荷、风电及光伏（solar PV）发电预测数据。上述预测均由梯度提升（gradient boosting）模型生成：其中以"_ls"结尾的预测基于最小二乘法（least square）；以"_quantile_i"结尾的预测为分位数预测，例如"wind_quantile_2"表示风电功率低于该数值的概率为20%。本梯度提升预测器已基于以下解释变量完成训练，用于预测风电功率、光伏功率与负荷：
- 光伏（solar PV）：云量占比、初始发电量、晴空发电量以及预报前瞻时长
- 风电：风速、风向、由风速预报转换得到的风电功率、初始发电量以及预报前瞻时长
- 负荷：当日时刻、当年月份