Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level

Tardioli, Giovanni; Kerrigan, Ruth; Oates, Michael; O’Donnell, James; Finn, Donal

doi:10.1016/j.egypro.2015.11.754

Cited by 87 publications

(46 citation statements)

References 44 publications

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“…In the context of opportunities coming along with big data analytics in electric utilities, the relevant issues and opportunities are discussed and reviewed in [34] focusing on the policymaking perspective. The review of [35] summarised the data driven approaches for predicting building energy consumption at urban level, whilst the data driven technologies and applications are reviewed in regard to the development of intelligent energy network in [36].…”

Section: Energy Efficiency and Intelligencementioning

confidence: 99%

Big Data and Climate Change

Hassani

Huang

Silva

2019

BDCC

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Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

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Section: Energy Efficiency and Intelligencementioning

confidence: 99%

Big Data and Climate Change

Hassani

Huang

Silva

2019

BDCC

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“…Moreover, the bottom-up engineering model provides the maximum flexibility for testing energy conservation measures in detail and determining building energy end-use distribution. It has been classified as a white-box based approach utilizing a detailed thermal physics simulation (Tardioli et al, 2015).…”

Section: Building Stock Energy Modelingmentioning

confidence: 99%

Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach

Yao

et al. 2019

Journal of Cleaner Production

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Building stock modeling can predict stock energy consumption and carbon emissions for both current and future conditions to inform building design and retrofitting policies. A 'bottom-up' engineering approach for building stock energy modeling is attractive to built environment energy researchers because of its capacity for detailed energy analysis. However, such studies in China have been very limited to date. The aim of this research is to develop a modeling approach to residential building stock energy consumption for space heating and cooling. A holistic four-step approach of archetype configurations; building performance simulation; stock floor area estimation and local weather adjustment is presented. The Chongqing municipality was chosen to demonstrate the approach. The results show that adopting the northern China standard pattern of central space heating for Chongqing's urban residential stock is not feasible because it dramatically increases primary energy consumption and therefore carbon dioxide emissions from space heating usage. By applying energy conservation retrofit measures to the Chongqing urban residential stock, the total energy consumption for space heating and cooling and resulting carbon dioxide emissions can be significantly reduced, with estimated reductions of 57.6% to 60.7% in 2020 and 55.3% to 57.2% in 2050. The method described can provide useful information and guidance for policymakers contemplating energy retrofit schemes.

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“…It was concluded that machine learning approaches can be the easiest to deploy, with the hybrid machine learning and thermal modelling approaches offering significant promise in the future [30]. Tardioli et al reviewed the recent application of data-driven models at an urban level and proved that they are useful in reducing the time taken to create an energy consumption model while maintaining an adequate level of accuracy [31]. Finally, Ahmad et al reviewed ANN and SVM's with the aim of identifying better forecasting of building's electricity consumption.…”

Section: Baseline Energy Modellingmentioning

confidence: 99%

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Gallagher

Leahy

O’Donovan

et al. 2018

Energy and Buildings

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The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM's). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a particular focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate and reliable quantification of savings.A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology's ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances.

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Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level

Cited by 87 publications

References 44 publications

Big Data and Climate Change

Big Data and Climate Change

Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

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