A model for estimating cooling energy demand at early design stage of condominiums

Geekiyanage, Devindi; Ramachandra, Thanuja

doi:10.1016/j.jobe.2018.01.011

Cited by 23 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This code underlines the importance of the right selection of systems, energy, and materials [23]. The main factors that have been suggested in most studies are glazing types (SHGC) [21], U-value [24], window-to-wall ratio (WWR), which introduces the suitable natural light as well as the heat transmission through openings [25], orientation, and AC system (HVAC) selection [26]. In the present study, the thermal performance of an office building located in the UAE has been assessed using the eQUEST software by testing all of the above-mentioned parameters.…”

Section: Building Retrofittingmentioning

confidence: 99%

Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building

2020

View full text Add to dashboard Cite

Retrofitting an existing building can oftentimes be more cost-effective than building a new facility. Since buildings consume a significant amount of energy, particularly for heating and cooling, and because existing buildings comprise the largest segment of the built environment, it is important to initiate energy conservation retrofits to reduce energy consumption and the cost of heating, cooling, and lighting buildings. However, conserving energy is not the only reason for retrofitting existing buildings. The goal should be to create a high-performance building by applying an integrated, whole-building design process to the project during the planning phase that ensures that all key design objectives are met. This paper presents a real case study of the retrofitting of an existing building to achieve lower energy consumption. Indeed, most of the constructed buildings in the UAE are unsuitable for the region, which is characterized by a very harsh climate that causes massive cooling loads and energy consumption due to an appropriate selection of design parameters at the design level. In this study, a monthly computer simulation of energy consumption of an office building in Sharjah was carried out under UAE weather conditions. Several parameters, including the building orientation, heating, ventilation, and air conditioning (HVAC) system, external shading, window-to-wall ratio, and the U-values of the walls and the roof, were investigated and optimized to achieve lower energy consumption. The simulation shows that the best case is 41.7% more efficient than the real (original) case and 30.6% more than the base case. The most sensitive parameter in the retrofitting alternatives is the roof component, which affects the energy savings by 8.49%, followed by the AC system with 8.34% energy savings if well selected using the base case. Among the selected five components, a new roof structure contributed the most to the decrease in the overall energy consumption (approximately 38%). This is followed by a new HVAC system, which leads to a 37% decrease, followed by a new wall type with insulation, resulting in a 20% decrease.

show abstract

Section: Building Retrofittingmentioning

confidence: 99%

Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building

2020

View full text Add to dashboard Cite

show abstract

“…SVM has been used to predict failures of construction companies (Horta and Camanho, 2013). Linear interpolation methods have been used to predict the annual electricity consumption of elevators (Tukia et al, 2016) and cooling loads (Geekiyanage and Ramachandra, 2018). Intelligent decision support systems have been developed to facilitate the management of construction processes (Hajdasz, 2014) and rule-based support systems have been used to check regulatory compliance (Beach et al, 2015).…”

Section: Predictive Analyticsmentioning

confidence: 99%

Big Data Analytics System for Costing Power Transmission Projects

Delgado

Bilal

Ajayi

et al. 2020

J. Constr. Eng. Manage.

View full text Add to dashboard Cite

Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a Big Data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a Predictive Analytics and Modelling System (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75 million-point dataset of power transmission projects has been used as a case study. The proposed Big Data architecture is fit for purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the Body of Knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a Big Data architecture that can manage and process large varied datasets and seamless scalability.

show abstract

“…Many different variables are influencing the cooling energy demand of buildings [16]. These, for instant, include time of a year, building's location, which accordingly effects on local temperature, humidity, wind speed, building material and size, occupants activities, etc.…”

Section: Input Variables To the Ann Modelmentioning

confidence: 99%

“…Building's orientation refers to the direction of the building in which it is based and it affects the building thermal performance by minimizing the direct solar radiation on the building envelop [16]. In the literature has been reported that the selection of building orientation has to be done carefully considering the sun movements according to latitude, and time of day and month [16,23,24]. Thus, this factor has been considered here as an important factor that has to be properly selected by designers.…”

Section: ) Orientationmentioning

confidence: 99%

“…Thus, window to wall ratio (WWR), which is the ratio of the glazed surface to the gross facade area, has been considered as one of the important input variables to the ANN, since glass represents the weakest thermal component in the building due to its high U-Value. The building becomes too cold in winter and too hot in the summer if too high WWR is used; this is due to the heat loss, coming sunlight and heat through windows [16,20]. Here, three WWR ratios, shown in Table II, are investigated using HAP and also the got result are utilized in the training process of the ANN model.…”

Section: ) Window To Wall Ratio (Wwr)mentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Jaber¹,

Saleh²,

Ali³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system. Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.

show abstract

A model for estimating cooling energy demand at early design stage of condominiums

Cited by 23 publications

References 16 publications

Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building

Retrofitting an Existing Office Building in the UAE Towards Achieving Low-Energy Building

Big Data Analytics System for Costing Power Transmission Projects

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Contact Info

Product

Resources

About