{
  "fetched_at": "2026-10-20T12:47:40Z",
  "source_url": "https://www.kaggle.com/competitions/elec7011-forecasting-competition-23-fall/data",
  "pages": [
    {
      "name": "data-description",
      "content": "You can download the file on this page.\n## Files\n\n*   **Train data.csv** - the training set\n*   **Test** - the file fold with the 6 test set and submission demo ‘Answer.csv’\n   you just need to replace the column 'Load' with your forecasting result. (the result of six days you forecasts should be concat into one columns)",
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    {
      "name": "Description",
      "content": "This competition requires you to forecast future electricity loads based on the historical load data of a specific region.",
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    {
      "name": "rules",
      "content": "- Complete independently, no plagiarism (including code and predicted results)!\n- You can discuss the questions you meet in the discussion module we have set up.\n- If you need to use the data for any purpose other than this course, please contact the Dr. Wang or TAs.\n- Have fun!",
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    {
      "name": "abstract",
      "content": "**This is project 2 of the ELEC 7011 of HKU.**\nThis is a load forecasting assignment for the ELEC7011 course.\n\nIf you have any questions, feel free to contact the email **yzzhou@connect.hku.hk**",
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    {
      "name": "Grade",
      "content": "Your grade will be determined by the accuracy of your work and the quality of your report.\n\nWe will evaluate the code and results you submitted on Moodle. If your code fails to produce the submitted results, you will be regarded as plagiarizing. Your score of this project will be zero or even fall this course.",
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    {
      "name": "Submission format",
      "content": "**In Kaggle：**\nYou can submit your predictions to Kaggle three times a day to test and adjust your model based on them. Sample submissions can be downloaded on Moodle.\n\n**In Moodle**\nYou should submit a report and your code about your project on Moodle",
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}