Trimming outliers using trees

Fu, Chun; Arjunan, Pandarasamy; Miller, Clayton

doi:10.1145/3563357.3566147

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…An in-depth analysis of errors of the top solutions illustrated the capability and limitations of forecasting for a large number of meters of different building and meter types, locations, and climates [110]. Another competition that deserves attention is the Large-scale Energy Anomaly Detection (LEAD) competition hosted on the Kaggle platform, which challenges participants to develop accurate machine learning models for energy anomaly detection in smart meter time series data from 406 buildings [111,112]. Other competitions have targeted more niche use cases, e.g., predicting utility-scale electricity demand in a post covid-19 world (i.e.…”

Section: Competitionsmentioning

confidence: 99%

Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale

Kazmi

Miller

2023

Building and Environment

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Section: Competitionsmentioning

confidence: 99%

Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale

Kazmi

Miller

2023

Building and Environment

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“…The interface comprises of a main page, referred to as the Meta Directory, which provides an overview of all available datasets and several sub-pages presenting datasets by types. Ground-truth and simulated datasets for anomalies in the built environment and building systems Large-scale Energy Anomaly Detection (LEAD) Dataset [21] Occupant Data…”

Section: Overview Of the Directory Interfacementioning

confidence: 99%

The Building Data Genome Directory – An open, comprehensive data sharing platform for building performance research

Jin,

Fu,

Kazmi

et al. 2023

J. Phys.: Conf. Ser.

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The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existing open datasets, no database currently offers a comprehensive collection of building data types with all subcategories and time granularities (e.g., year, month, and sub-hour). This paper presents the Building Data Genome Directory, an open data-sharing platform serving as a one-stop shop for the data necessary for vital categories of building energy research. The data directory is an online portal (buildingdatadirectory.org/) that allows filtering and discovering valuable datasets. The directory covers meter, building-level, and aggregated community-level data at the spatial scale and year-to-minute level at the temporal scale. The datasets were consolidated from a comprehensive exploration of sources, including governments, research institutes, and online energy dashboards. The results of this effort include the aggregation of 60 datasets pertaining to building energy ontologies, building energy models, building energy and water data, electric vehicle data, weather data, building information data, text-mining-based research data, image data of buildings, fault detection diagnosis data and occupant data. A crowdsourcing mechanism in the platform allows users to submit datasets they suggest for inclusion by filling out an online form. This directory can fuel research and applications on building energy efficiency, which is an essential step toward addressing the world’s energy and environmental challenges.

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