{
  "fetched_at": "2026-10-19T07:54:21Z",
  "source_url": "https://www.kaggle.com/competitions/ashrae-energy/data",
  "pages": [
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      "name": "data-description",
      "content": "Assessing the value of energy efficiency improvements can be challenging as there's no way to truly know how much energy a building would have used without the improvements. The best we can do is to build counterfactual models. Once a building is overhauled the new (lower) energy consumption is compared against modeled values for the original building to calculate the savings from the retrofit. More accurate models could support better market incentives and enable lower cost financing.\n\nThis competition challenges you to build these counterfactual models across four energy types based on historic usage rates and observed weather. The dataset includes three years of hourly meter readings from over one thousand buildings at several different sites around the world.\nFiles\n\n\ntrain.csv\n\n    building_id - Foreign key for the building metadata.\n    meter - The meter id code. Read as {0: electricity, 1: chilledwater, 2: steam, 3: hotwater}. Not every building has all meter types.\n    timestamp - When the measurement was taken\n    meter_reading - The target variable. Energy consumption in kWh (or equivalent). Note that this is real data with measurement error, which we expect will impose a baseline level of modeling error. UPDATE: as discussed here, the site 0 electric meter readings are in kBTU.\n\nbuilding_meta.csv\n\n    site_id - Foreign key for the weather files.\n    building_id - Foreign key for training.csv\n    primary_use - Indicator of the primary category of activities for the building based on EnergyStar property type definitions\n    square_feet - Gross floor area of the building\n    year_built - Year building was opened\n    floor_count - Number of floors of the building\n\nweather_[train/test].csv\n\nWeather data from a meteorological station as close as possible to the site.\n\n    site_id\n    air_temperature - Degrees Celsius\n    cloud_coverage - Portion of the sky covered in clouds, in oktas\n    dew_temperature - Degrees Celsius\n    precip_depth_1_hr - Millimeters\n    sea_level_pressure - Millibar/hectopascals\n    wind_direction - Compass direction (0-360)\n    wind_speed - Meters per second\n\ntest_upload.csv\n\nThe submission files use row numbers for ID codes in order to save space on the file uploads. test.csv has no feature data; it exists so you can get your predictions into the correct order.\n\n    id - Row id for your submission file\n    building_id - Building id code\n    meter - The meter id code\n    timestamp - Timestamps for the test data period\n\nsample_submission.csv \n\nMake your submission in this format \n\nid- row id of your submission\n\n\nmeter_reading - predictions of your model\n",
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      "name": "Description",
      "content": "Q: How much does it cost to cool a skyscraper in the summer?\nA: A lot! And not just in dollars, but in environmental impact.\n\nThankfully, significant investments are being made to improve building efficiencies to reduce costs and emissions. The question is, are the improvements working? That’s where you come in. Under pay-for-performance financing, the building owner makes payments based on the difference between their real energy consumption and what they would have used without any retrofits. The latter values have to come from a model. Current methods of estimation are fragmented and do not scale well. Some assume a specific meter type or don’t work with different building types.\n\nIn this competition, you’ll develop accurate models of metered building energy usage in the following areas: chilled water, electric, hot water, and steam meters. The data comes from over 1,000 buildings over a three-year timeframe. With better estimates of these energy-saving investments, large scale investors and financial institutions will be more inclined to invest in this area to enable progress in building efficiencies.",
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    {
      "name": "Evaluation",
      "content": "#Evaluation Metric\n\nThe evaluation metric for this competition is Root Mean Squared Logarithmic Error.\n[RMSLE](https://www.kaggle.com/c/ashrae-energy-prediction/discussion/113064)\n\nWhere:\nϵ is the RMSLE value (score)\nn is the total number of observations in the (public/private) data set,\npi is your prediction of target, and\nai is the actual target for i.\nlog(x) is the natural logarithm of x\n\nNote that not all rows will necessarily be scored.",
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    {
      "name": "rules",
      "content": "<h2>One account per participant</h2>\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n<h2>No private sharing outside teams</h2>\n<p>Privately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums. </p>\n<h2>Team Mergers</h2>\n<p>Team mergers are allowed in this competition and deadline for team mergers is 23rd Sept, 2020 11:59 PM IST.</p>\n<h2>Team Limit</h2>\n<p>Maximum Team Size is 3.</p>\n<h2>General Guidelines</h2>\n<ul>\n<li>Use of any pretrained model is not allowed.</li>\n<li>Any use of deep learning approaches is not allowed. Please stick to classical approaches.</li>\n<li>No use of External Data is allowed, only the data provided with the competition has to be used.</li>\n</ul>\n<h2>Submission Limit</h2>\n<p>You may submit a maximum of 5 entries per day till 10th Oct, 2020 11:59 PM IST and 2 submissions after that.<br>\nYou may select up to 2 final submissions for judging.</p>\n<h2>Competition Deadline</h2>\n<p>End Date: 18th Dec, 2020 11:59 PM IST</p>",
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