I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset

Energy communities will play a central role in the sustainable energy transition by helping inform and engage end users to become more responsible consumers of energy. However, the true potential of energy communities can only be unlocked at scale. This scalability requires data-driven solutions that model not just the behaviour of building occupants but also of energy flexible resources in buildings, distributed generation and grid conditions in general. This understanding can then be utilized to improve the design and operation of energy communities in a variety of real-world settings. However, in practice, collecting and analysing the data necessary to realize these objectives forms a large part of such projects, and is often seen as a prohibitive stumbling block. Furthermore, without a proper understanding of the local context, these projects are often at risk of failure due to misplaced expectations. However, this process can be considerably accelerated by utilizing open source datasets and models from related projects, which have been carried out in the past. Likewise, a number of open source, general-purpose tools exist that can help practitioners design and operate LECs in a near-optimal manner. These resources are important because they not only help ground expectations, they also provide LECs and other relevant stakeholders, including utilities and distribution system operators, with much-needed visibility on future energy and cash flows. This review provides a detailed overview of these open-source datasets, models and tools, and the many ways they can be utilized in optimally designing and operating real-world energy communities. It also highlights some of the most important limitations in currently available open source resources, and points to future research directions. Highlights:1. The importance of open-source datasets and tools for local energy communities 2. Common use cases for open-source datasets, models and tools for energy communities 3. A thorough review of electricity demand and meteorological datasets and models 4. Most important shortcomings with currently available datasets, models and tools

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“…2. The I-BLEND dataset [40] provides minute-level electricity demand data from a university campus in India. The data is available for 52 months.…”

Section: Commercial Buildingsmentioning

confidence: 99%

Towards data-driven energy communities: A review of open-source datasets, models and tools

Kazmi

Munné‐Collado

Mehmood

et al. 2021

Renewable and Sustainable Energy Reviews

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“…The building energy analytics community has only just started to use open data sets towards the efforts of creating benchmarking data sets. Several prominent open building energy-related data sets have been released in recent years including applications to building-level office 6 and residential 7 appliances, occupant behavior 8 , heat pump 9 and natural ventilation systems 10 , as well as commercial and residential energy meter data 11 – 13 . The use of open data sets in the built environment enables the analysis of large numbers of buildings in applications such as benchmarking 14 .…”

Section: Background and Summarymentioning

confidence: 99%

The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition

et al. 2020

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This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

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“…For example, utility smart meter data, HVAC control system data, lighting system data, and submetered electricity and gas data are often obtained on a research-project specific data, and restricted by NDAs or other data sharing restrictions. There is a nascent body of shared operational datasets for buildings, including for example [8][9][10] .…”

Section: Background and Summarymentioning

confidence: 99%

Building fault detection data to aid diagnostic algorithm creation and performance testing

et al. 2020

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It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

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I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset

Cited by 36 publications

References 16 publications

Towards data-driven energy communities: A review of open-source datasets, models and tools

Towards data-driven energy communities: A review of open-source datasets, models and tools

The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition

Building fault detection data to aid diagnostic algorithm creation and performance testing

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