Unique Building Identifier: A natural key for building data matching and its energy applications

Wang, Na; Vlachokostas, Alex; Borkum, Mark; Bergmann, Harry; Zaleski, Sarah

doi:10.1016/j.enbuild.2018.11.052

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…For example, geometry, size, and height can be used to predict energy consumption in buildings. Wang et al [109] developed the Unige Building Identifier to correspond attribute data and building energy to smooth the way for corresponding across datasets. Depecker et al [110] matched the consumption of heating of the buildings and their shape.…”

Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…We require an energy consumption model based on building use, because residential, commercial, and public buildings have different energy consumption patterns. For example, heating energy dominates household energy use, whereas commercial buildings use energy disproportionately for cooling [14]. In this way, energy consumption differs by building use, and consequently, residential, commercial, and public buildings require separate energy demand forecasts.…”

Section: Methodsmentioning

confidence: 99%

Energy Demand Forecast Models for Commercial Buildings in South Korea

Tae

Kim

2019

Energies

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With the Paris Agreement entering into full force, South Korea must submit its target greenhouse gas emissions for commercial buildings by 2030 to the United Nations Framework Convention on Climate Change. To determine this target, the annual energy demands must be forecasted through appropriate models; the development of these models is the focus of our study. We developed a system to calculate energy demand forecasts by searching for suitable methods. We built distinct energy forecast models for petroleum, city gas, electricity, heat, and renewable energies. The results show that the most appropriate variable for the petroleum energy model is energy trend. Moreover, the annual increase rate of petroleum energy demand from 2019 to 2030 was forecasted to be −1.7%. The appropriate variable for city gas energy model was the floor area of commercial buildings, which was forecasted to increase at an annual average growth rate of 0.4% from 2019 to 2030. According to the forecast results of energy demand from 2019 to 2030, the annual average growth rates of electricity, heat, and renewable energy demands were 2.1%, −0.2%, and 1.3%, respectively.

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“…SEED and other applications are in the process of implementing Unique Building Identification (UBID) (N. Wang, Vlachokostas, Borkum, Bergmann, & Zaleski, 2019) in their platforms. UBID is a standardized framework that facilitates building data matching across different data sources into a single location as well as data exchange.…”

Section: Synthetic Data Storage and Retrievalmentioning

confidence: 99%

Generation and representation of synthetic smart meter data

Hong

Macumber

et al. 2020

Build. Simul.

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Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This study describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

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Unique Building Identifier: A natural key for building data matching and its energy applications

Cited by 9 publications

References 12 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energy Demand Forecast Models for Commercial Buildings in South Korea

Generation and representation of synthetic smart meter data

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