The Great Energy Predictor III competition technical team is happy to provide links to several publications that resulted from this competition that help summarize some of the outputs/lessons learned as hosted open-access on arXiv:
\nGitHub Repositories:
\nThe Great Energy Predictor III competition technical team is happy to provide links to several publications that resulted from this competition that help summarize some of the outputs/lessons learned as hosted open-access on arXiv:
\nGitHub Repositories:
\n", "rawMarkdown": "The Great Energy Predictor III competition technical team is happy to provide links to several publications that resulted from this competition that help summarize some of the outputs/lessons learned as hosted open-access on arXiv:\n\n- [Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned](https://arxiv.org/abs/2202.02898)\n- [Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis](https://arxiv.org/abs/2106.13475)\n- [The ASHRAE Great Energy Predictor III competition: Overview and results](https://arxiv.org/abs/2007.06933)\n- [The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition](https://arxiv.org/abs/2006.02273)\n- [Using Google Trends as a proxy for occupant behavior to predict building energy consumption](https://arxiv.org/abs/2111.00426)\n\nGitHub Repositories:\n- [GEPIII Solutions replication and analysis](https://github.com/buds-lab/ashrae-great-energy-predictor-3-solution-analysis)\n- [GEPIII Error analysis](https://github.com/buds-lab/ashrae-great-energy-predictor-3-error-analysis)\n- [Building Data Genome 2 Project -- the open data set that includes the GEPIII data set](https://github.com/buds-lab/building-data-genome-project-2)\n- [Overview paper supplement](https://github.com/buds-lab/ashrae-great-energy-predictor-3-overview-analysis)\n- [Using Google Trends to improve building energy prediction](https://github.com/buds-lab/google-trends-for-buildings)\n\n", "votes": 9 } ], "comments": [], "raw_markdown_by_id": { "1689105": "The Great Energy Predictor III competition technical team is happy to provide links to several publications that resulted from this competition that help summarize some of the outputs/lessons learned as hosted open-access on arXiv:\n\n- [Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned](https://arxiv.org/abs/2202.02898)\n- [Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis](https://arxiv.org/abs/2106.13475)\n- [The ASHRAE Great Energy Predictor III competition: Overview and results](https://arxiv.org/abs/2007.06933)\n- [The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition](https://arxiv.org/abs/2006.02273)\n- [Using Google Trends as a proxy for occupant behavior to predict building energy consumption](https://arxiv.org/abs/2111.00426)\n\nGitHub Repositories:\n- [GEPIII Solutions replication and analysis](https://github.com/buds-lab/ashrae-great-energy-predictor-3-solution-analysis)\n- [GEPIII Error analysis](https://github.com/buds-lab/ashrae-great-energy-predictor-3-error-analysis)\n- [Building Data Genome 2 Project -- the open data set that includes the GEPIII data set](https://github.com/buds-lab/building-data-genome-project-2)\n- [Overview paper supplement](https://github.com/buds-lab/ashrae-great-energy-predictor-3-overview-analysis)\n- [Using Google Trends to improve building energy prediction](https://github.com/buds-lab/google-trends-for-buildings)\n\n" } }