{
  "id": 112855,
  "title": "Similar Competitions",
  "url": "/competitions/ashrae-energy-prediction/discussion/112855",
  "author_name": "",
  "post_date": "2019-10-15T20:12:42.219247400Z",
  "votes": 20,
  "comment_count": 1,
  "views": 0,
  "content": "<p>[Work In Progress]</p>\n\n<p>This thread is collect information about previous competitions about energy usage (Kaggle and Non-Kaggle).</p>\n\n<p><a href=\"https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/\">Power Laws: Forecasting Energy Consumption - By DrivenData</a>\n    - <a href=\"http://drivendata.co/blog/power-laws-forecasting-winners/\">Winning Solutions</a></p>\n\n<p>In fact, driven data organized a bunch of competitions related to energy in collaboration with Schneider Electric\n- <a href=\"https://www.drivendata.org/competitions/52/anomaly-detection-electricity/\">Power Laws: Detecting Anomalies in Usage</a>\n- <a href=\"https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/\">Power Laws: Optimizing Demand-side Strategies</a>\n- <a href=\"https://www.drivendata.org/competitions/55/schneider-cold-start/\">Power Laws: Cold Start Energy Forecasting</a>\n- <a href=\"https://www.drivendata.org/competitions/56/predict-cleaning-time-series/\">Sustainable Industry: Rinse Over Run</a></p>",
  "messages": [
    {
      "id": "649841",
      "postDate": "10/15/2019 20:12:42",
      "content": "<p>[Work In Progress]</p>\n\n<p>This thread is collect information about previous competitions about energy usage (Kaggle and Non-Kaggle).</p>\n\n<p><a href=\"https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/\">Power Laws: Forecasting Energy Consumption - By DrivenData</a>\n    - <a href=\"http://drivendata.co/blog/power-laws-forecasting-winners/\">Winning Solutions</a></p>\n\n<p>In fact, driven data organized a bunch of competitions related to energy in collaboration with Schneider Electric\n- <a href=\"https://www.drivendata.org/competitions/52/anomaly-detection-electricity/\">Power Laws: Detecting Anomalies in Usage</a>\n- <a href=\"https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/\">Power Laws: Optimizing Demand-side Strategies</a>\n- <a href=\"https://www.drivendata.org/competitions/55/schneider-cold-start/\">Power Laws: Cold Start Energy Forecasting</a>\n- <a href=\"https://www.drivendata.org/competitions/56/predict-cleaning-time-series/\">Sustainable Industry: Rinse Over Run</a></p>",
      "rawMarkdown": "[Work In Progress]\n\nThis thread is collect information about previous competitions about energy usage (Kaggle and Non-Kaggle).\n\n[Power Laws: Forecasting Energy Consumption - By DrivenData](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/)\n    - [Winning Solutions](http://drivendata.co/blog/power-laws-forecasting-winners/)\n\nIn fact, driven data organized a bunch of competitions related to energy in collaboration with Schneider Electric\n- [Power Laws: Detecting Anomalies in Usage](https://www.drivendata.org/competitions/52/anomaly-detection-electricity/)\n- [Power Laws: Optimizing Demand-side Strategies](https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/)\n- [Power Laws: Cold Start Energy Forecasting](https://www.drivendata.org/competitions/55/schneider-cold-start/)\n- [Sustainable Industry: Rinse Over Run](https://www.drivendata.org/competitions/56/predict-cleaning-time-series/)",
      "votes": null
    },
    {
      "id": "649925",
      "postDate": "10/15/2019 22:36:55",
      "content": "<p>Thanks <a href=\"/arnabbiswas1\">@arnabbiswas1</a> </p>",
      "rawMarkdown": "Thanks @arnabbiswas1",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 649925,
      "author_name": "hamditarek",
      "author_url": "",
      "post_date": "10/15/2019 22:36:55",
      "content": "<p>Thanks <a href=\"/arnabbiswas1\">@arnabbiswas1</a> </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "649841": "[Work In Progress]\n\nThis thread is collect information about previous competitions about energy usage (Kaggle and Non-Kaggle).\n\n[Power Laws: Forecasting Energy Consumption - By DrivenData](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/)\n    - [Winning Solutions](http://drivendata.co/blog/power-laws-forecasting-winners/)\n\nIn fact, driven data organized a bunch of competitions related to energy in collaboration with Schneider Electric\n- [Power Laws: Detecting Anomalies in Usage](https://www.drivendata.org/competitions/52/anomaly-detection-electricity/)\n- [Power Laws: Optimizing Demand-side Strategies](https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/)\n- [Power Laws: Cold Start Energy Forecasting](https://www.drivendata.org/competitions/55/schneider-cold-start/)\n- [Sustainable Industry: Rinse Over Run](https://www.drivendata.org/competitions/56/predict-cleaning-time-series/)",
    "649925": "Thanks @arnabbiswas1"
  },
  "source": "meta"
}