{
  "fetched_at": "2026-10-24T10:03:15Z",
  "source_url": "https://www.kaggle.com/competitions/global-energy-forecasting/data",
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    {
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      "content": "\n\n## 数据集描述\n“train.csv”包含训练数据：\n\n第一列（“date”）是时间戳，记录每小时风力测量数据的日期和时间。例如“2009070812”表示2009年7月8日12:00；\n\n接下来的7列（“wp1”至“wp7”）收集了7个风电场的标准化风力测量数据。它们进行了标准化处理，使得取值在0到1之间，以便不暴露各个风电场的身份。\n\n同时，也提供了解释性变量文件（即风预报）供有兴趣使用的人参考。例如，“windforecasts_wf1”文件包含了第一个风电场的风预报数据。在这些文件中：\n\n第一列（“date”）是时间戳，记录预报发布的日期和时间。例如“2009070812”表示2009年7月8日12:00；\n\n第二列（“hors”）表示预报的提前时间。例如如果“date”=2009070812且“hors”=1，则预报的时间为2009年7月8日13:00；\n\n接下来的4列（“u”，“v”，“ws”和“wd”）是预报数据本身，前两列是纬向和经向风分量，后两列则分别是对应的风速和风向。\n\n最后，“benchmark.csv”文件提供了持续性预报方法（“所见即所得”）的示例预报结果。这个文件还给出了提交结果的模板，提交时必须严格遵循。\n\n请注意，“benchmark.csv”的第一列名为“id”，包含每行的唯一标识符。其他8列与“train.csv”相同。提交结果时，请确保您的文件包括与“benchmark.csv”相同格式的9列。\n\n\n\"test.csv\" 里面包含需要预测的数据",
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    {
      "name": "Description",
      "content": "The topic for the wind forecasting track is focused on mimicking the operation 48-hour ahead prediction of hourly power generation at 7 wind farms, based on historical measurements and additional wind forecast information (48-hour ahead predictions of wind speed and direction at the sites). The data is available for period ranging from the 1st hour of 2009/7/1 to the 12th hour of 2012/6/28.\n\nThe period between 2009/7/1 and 2010/12/31 is a model identification and training period, while the remainder of the dataset, that is, from 2011/1/1 to 2012/6/28, is there for the evaluation. The training period is there to be used for designing and estimating models permiting to predicting wind power generation at lead times from 1 to 48 hours ahead, based on past power observations and/or available meteorological wind forecasts for that period. Over the evaluation part, it is aimed at mimicking real operational conditions. For that, a number of 48-hour periods with missing power observations where defined. All these power observations are to be predicted. These periods are defined as following. The first period with missing observations is that from 2011/1/1 at 01:00 until 2011/1/3 at 00:00. The second period with missing observations is that from 2011/1/4 at 13:00 until 2011/1/6 at 12:00. Note that to be consistent, only the meteorological forecasts for that period that would actually be available in practice are given. These two periods then repeats every 7 days until the end of the dataset. Inbetween periods with missing data, power observations are available for updating the models.",
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    {
      "name": "Evaluation",
      "content": "The forecasting accuracy will be evaluated with a Root Mean Square Error (RMSE) criterion. The RMSE is calculated as the square root of the average of square forecast errors. All forecasts will be equally weighted in the score calculation.",
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    },
    {
      "name": "rules",
      "content": "One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.",
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    },
    {
      "name": "abstract",
      "content": "Let's Start",
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    }
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}