A data-driven analysis of building energy use with emphasis on operation and maintenance: A case study from the UAE

Lin, Min; Afshari, Afshin; Azar, Elie

doi:10.1016/j.jclepro.2018.04.270

Cited by 59 publications

(34 citation statements)

References 83 publications

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“…This drove similar increases in generation capacity, as indicated in Figure 1 [1]. The surge in energy consumption, positions the UAE as one of the world's largest energy consumers per capita, with the building sector accounting for almost 70% of its total electrical energy consumption [2]. The primary electricity loads in the UAE are, by far, Figure 1.…”

Section: Introductionmentioning

confidence: 91%

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Enhancing Green Building Rating of a School under the Hot Climate of UAE; Renewable Energy Application and System Integration

2018

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Similar to many fast growing countries, the United Arab Emirates (UAE) witnessed fast population and urbanization growth. The building sector accounts for a major share of its electricity consumption, reaching up to 70%. To encourage sustainable development and reduce energy consumption and emissions, the government introduced a sustainability initiative called “Estidama”, which employs the use of the Pearl Building Rating System (PBRS). Government buildings, which constitute 20% of the built environment, aim to lead the way, and are therefore required to attain a high level of achievement, based on their PBRS ranking (minimum of two out of five pearls). Schools, led by Abu Dhabi Educational Council (ADEC), are governmental buildings and aim to attain a higher level of achievement (three out of five pearls). The ADEC plans to build one hundred schools to be built by 2020, through its Future Schools Program. Over half of the schools have been completed, but only 20% reached the targeted rating (of three out of five pearls). The Renewable Energy (RE) application in the UAE is minimal, although it represents 25% of the local rating code. The objective of this paper is to explore the sustainable performance of one representative school that did not reach the desired green rating level, with the objective to assess opportunities for an enhanced performance. This is done through testing the performance and the application of three RE systems comprising of photovoltaics (PV) array, an absorption cooling system and a geothermal cooling system through Transient Systems Simulation (TRNSYS) software. Cumulatively, implementation of these options results in RE potentially contributing to 19% of the school’s annual energy consumption, enhancing the school’s performance by up to 14 additional credit points, and reaching the target level of achievement (a three pearl rating). Furthermore, system integration of RE into the existing school were also considered. Results indicate the significant potential of integrating RE systems in future schools in hot climatic contexts, for an improved energy performance.

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Section: Introductionmentioning

confidence: 91%

“…The Estidama PBRS also awards additional RE-5 credits for peak cooling capacity reductions as given in Equation (2). The annual average electrical load is calculated in Equation (3…”

Section: Credit Enhancement Verification Of the Local Green Rating Symentioning

confidence: 99%

Enhancing Green Building Rating of a School under the Hot Climate of UAE; Renewable Energy Application and System Integration

2018

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“…24 In order to further confirm the validity of the base case model, its estimates are benchmarked against data collected from multiple buildings as part of a previous research effort by the authors. 48 As detailed in Lin et al 48 , the primary source of data used is a database compiling the results of a representative survey of Abu Dhabi's building stock. A total of the 13 buildings matched the main characteristics of the base case model, mainly its type (ie, office building) and size (ie, medium to high rise).…”

Section: Building Energy Modelingmentioning

confidence: 99%

A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

Alkaabi

Cho

Mayyas

et al. 2020

Energy Science & Engineering

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Economic development, population growth, and urbanization are putting increasing pressures on critical resources, such as food, water, and electricity. 1,2 The rapid population growth and urban development are often accompanied by continual increases in CO 2 emissions, 70 percent of which are coming from cities. 3 In countries with extreme climate conditions, the demand for resources, particularly energy, is intensified. 4 For instance, the United Arab Emirates (UAE) is often ranked among the top 10 countries with the highest electricity consumption per capita (MWh/person) in the world. 5,6 When comparing different sectors of the economy, the building sector often carries the highest weight in terms of energy consumption and associated CO 2 emissions. In most developed countries, the building sector accounts for 30 to 40

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“…Within the scientific context, several research activities have been carried out on buildings energy performance assessment, for: (i) predicting energy demand [7,10,23] and energy class [24], (ii) rating and benchmarking [25][26][27][28], (iii) individuating representative buildings for different classes of energy performance [29][30][31], (iv) characterizing the relationship between energy demand and relevant building features [32][33][34], and (v) improving existing methods, also using new model based on data mining algorithms like regression models, decision trees, neural networks, and clustering [24,32,[35][36][37][38].…”

Section: Related Workmentioning

confidence: 99%

Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

et al. 2019

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Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating (PED h ) and (ii) the characterization of the relationship between the PED h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the PED h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

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A data-driven analysis of building energy use with emphasis on operation and maintenance: A case study from the UAE

Cited by 59 publications

References 83 publications

Enhancing Green Building Rating of a School under the Hot Climate of UAE; Renewable Energy Application and System Integration

Enhancing Green Building Rating of a School under the Hot Climate of UAE; Renewable Energy Application and System Integration

A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

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