UNICON:大型多校区大学环境下电、气和水消耗的开放数据集

Harsha Moraliyage, Nishan Mills, Prabod Rathnayake, Daswin De Silva, Andrew Jennings
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引用次数: 3

摘要

在本文中,我们介绍了UNICON,一个关于大学公用事业、电力、天然气和水消耗的大规模开放数据集。该数据集作为拉筹伯大学到2029年实现净零碳排放承诺的一部分公开发布,为此我们正在建立拉筹伯能源人工智能/分析平台(LEAP),该平台利用人工智能(AI)和数据分析来分析、预测和优化电力、可再生能源、天然气和水资源的消耗、生产和利用。UNICON包含拉筹伯大学分布在不同地理区域的五个校区的消费数据,涵盖2018年至2021年的四年。这包括COVID-19全球大流行期间大学关闭和在家工作措施,这些措施导致公用事业消耗大幅减少。消费数据包括智能电表读数(15分钟粒度)、燃气表读数(每小时间隔)和水表读数(每15分钟间隔)。UNICON还包含距离每个校园最近的气象站的天气数据,以1分钟和10分钟的两种速度延迟收集。数据集注释了具有重要意义的内部事件,例如作为LEAP优化的一部分进行的节能措施(ecm)和其他测量和验证(M&V)活动。据我们所知,这是第一个大规模、全面、开放的数据集,涉及多校区大学环境中的三种主要公用事业,即电力、天然气和水的消耗。高粒度数据字典和对消费趋势、基线建模和预测数据集的技术验证是本文的进一步贡献,将使感兴趣的研究科学家、学者、行业从业者、可持续性和能源顾问能够实验和评估他们的人工智能算法、模型、预测,并为能源基准、指导方针和急需的数据驱动能源政策的制定提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting
In this paper we introduce UNICON, a large-scale open dataset on UNIversity CONsumption of utilities, electricity, gas and water. This dataset is publicly released as part of La Trobe University’s commitment to Net Zero Carbon Emissions by 2029, for which we are building the La Trobe Energy AI/Analytics Platform (LEAP) that leverages Artificial Intelligence (AI) and Data Analytics to analyse, predict and optimize the consumption, generation and utilization of electricity, renewables, gas and water resources. UNICON contains consumption data for La Trobe’s five campuses in geographically distributed regions, across four years, 2018-2021 inclusive. This includes the COVID-19 global pandemic timeline of university shutdown and work from home measures that led to a significant decrease in the consumption of utilities. The consumption data consists of smart electricity meter readings at 15-minute granularity, gas meter readings at hourly intervals and water meter readings at 15-minute intervals. UNICON also contains weather data from the closest weather station to each campus, collected at two-speed latency of 1 minute and 10 minutes. The dataset is annotated with internal events of significance, such as energy conservation measures (ECMs) and other measurement and validation (M&V) activities conducted as part of LEAP optimization. To the best of our knowledge, this is the first large-scale, comprehensive, open dataset for the three main utilities, electricity, gas, and water consumption in a multi-campus university setting. A high granularity data dictionary and technical validation of the dataset for consumption trends, baseline modelling and forecasting are further contributions of this article that will enable interested research scientists, academics, industry practitioners, sustainability and energy consultants to experiment and evaluate their AI algorithms, models, forecasts, as well as inform the development of energy benchmarks, guidelines and much needed data-driven energy policies.
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