scholarly journals Thermal Analysis of an Educational Building with Different Construction Materials

2018 ◽  
Vol 01 (02) ◽  
pp. 75-86
Author(s):  
Kashif Rasheed ◽  
Shimza Jamil ◽  
Muhammad Ramzan ◽  
Muhammad Zulqarnain
Keyword(s):  
Energy Consumption ◽  
Building Materials ◽  
Construction Materials ◽  
Educational Institutions ◽  
Energy Usage ◽  
Heating And Cooling ◽  
Planning And Design ◽  
Warming Climate ◽  
Heating Load ◽  
Cooling Loads

The energy consumption has been increased to an alarming rate in the current world. This scenario has raised many problems like depletion of energy resources, energy supply difficulties and increased carbon footprint (global warming, climate change). The objective of this research is to minimize the energy consumption in educational institutions. This study will help us in reducing the heating and cooling loads of building and resulting to saving cost. A prototype building was modelled in Autodesk Software, Ecotect 2011 for the climatic zone of Multan to examine the thermal performance with different construction materials. The building studied with different aspects including passive and active techniques, planning and design. These aspects were analyzed and results were evaluated. Various construction materials were listed and examined for the development of energy efficient envelope. The results showed 11.86 % decrease in energy usage including 11.76% decrease in cooling load and 46.59% in heating load with locally available building materials.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

2016 ◽  
Vol 819 ◽  
pp. 541-545 ◽  
Author(s):  
Sholahudin ◽  
Azimil Gani Alam ◽  
Chang In Baek ◽  
Hwataik Han
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Simulation Data ◽  
Heating And Cooling ◽  
Heating Load ◽  
Input Variables ◽  
Cooling Loads

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

Future trends of building heating and cooling loads and energy consumption in different climates

2011 ◽  
Vol 46 (1) ◽  
pp. 223-234 ◽  
Author(s):  
Kevin K.W. Wan ◽  
Danny H.W. Li ◽  
Dalong Liu ◽  
Joseph C. Lam
Keyword(s):  
Energy Consumption ◽  
Future Trends ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Different Climates

scholarly journals Energy Audit and Analysis of an Institutional Building under Subtropical Climate

2018 ◽  
Vol 8 (2) ◽  
pp. 845 ◽  
Author(s):  
Yazed Yasin Ghadi ◽  
Ali M. Baniyounes
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Environmental Parameters ◽  
Subtropical Climate ◽  
University Campus ◽  
Energy Usage ◽  
Climate Conditions ◽  
Heating And Cooling ◽  
And Performance ◽  
Heating And Cooling Load

<p>Evaluation and estimation of energy consumption are essential in order to classify the amount of energy used and the way it is utilized in building. Hence, the possibility of any energy savings potential and energy savings opportunities can be identified. The intention of this article is to study and evaluate energy usage pattern of the Central Queensland University campus’ buildings, Queensland, Australia. This article presents the field survey results from the audit of an office building and performance-related measurements of the indoor environmental parameters, for instance, indoor air temperature, humidity and energy consumption concerned to the indoor heating and cooling load. Monthly observed energy usage information was employed to investigate influence of the climate conditions on energy usage.</p>

scholarly journals On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Energy Consumption ◽  
Heuristic Algorithms ◽  
Invasive Weed ◽  
Elman Neural Network ◽  
Heating And Cooling ◽  
Second Tier ◽  
Grasshopper Optimization ◽  
Antlion Optimization ◽  
Teaching Learning ◽  
Cooling Loads

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, ten meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. Besides, an integrative ranking of the models is achieved using average ranking algorithm.

Designing a mediator space and the study of its effect on the energy consumption of a residential building using EnergyPlus software in Savadkuh, Iran

2019 ◽  
Vol 17 (4) ◽  
pp. 833-846
Author(s):  
Yasaman Yousefi ◽  
Mehdi Jahangiri ◽  
Akbar Alidadi Shamsabadi ◽  
Afshin Raeesi Dehkordi
Keyword(s):  
Energy Consumption ◽  
Design Methodology ◽  
Residential Building ◽  
Environmental Costs ◽  
Content Type ◽  
Design And Implementation ◽  
Heating And Cooling ◽  
Energy Simulations ◽  
Cooling Loads ◽  
Reducing Energy

Purpose Reducing energy consumption of a building may have a significant effect on the energy and environmental costs. Nowadays, energy simulations have come to the aid of engineers in the design and implementation of buildings with a perspective on energy consumption. Design/methodology/approach In the current study, the suggested volume of a residential building in the Savadkuh City, Iran, is modeled using Ecotect® software, and the amount of radiation on the sides during various months of the year is studied. Then, using EnergyPlus™ software, climate analyses are performed on the suggested design, and finally, the amount of heating and cooling loads of the building are examined under two difference scenarios of mediator space. Findings Results indicated that nearly at all times of the year, both the heating and cooling loads were reduced in the scenario where mediator space had two functions, i.e. as greenhouse and as a space for higher ventilation, compared to the scenario where mediator space did not have a climate role and merely served as an entrance and passageway with rigid dividers. Originality/value Nowadays, energy simulations have come to the aid of engineers in the design and implementation of buildings with a perspective on energy consumption. Therefore, in the current study, the suggested volume of a residential building in the Savadkuh City, Iran, is modeled using Ecotect® software, and the amount of radiation on the sides during various months of the year is studied. Then, using EnergyPlus™ software, climate analyses are performed on the suggested design, and finally, the amount of heating and cooling loads of the building are examined under two difference scenarios of mediator space.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

scholarly journals A METHOD OF OPTIMIZING FENESTRATION DESIGN FOR DAYLIGHTING TO REDUCE HEATING AND COOLING LOADS IN OFFICES

2014 ◽  
Vol 20 (5) ◽  
pp. 714-723 ◽  
Author(s):  
Hendrik Voll ◽  
Erkki Seinre
Keyword(s):  
Office Building ◽  
Design Parameters ◽  
Energy Usage ◽  
Heating And Cooling ◽  
Scale Models ◽  
Design Calculations ◽  
Cooling Loads ◽  
Building Designs ◽  
Window Design ◽  
Careful Design

Modern office building designs tend to increase the window share per facade to make the building more impressive with extensive visibility and well daylit rooms. In general, an increased window share results in higher energy usage and higher costs of heating and cooling, but these disadvantages can be reduced with a more careful design. The aim of this paper is to show the influence of window design and room layout on heating and cooling demand and daylight availability in office buildings in northern Europe. The results in the paper are based on design calculations for two different room types and daylight measurements on two room scale models in a daylight laboratory. The calculations show the influence of window design parameters on the cooling and heating demand. The daylight measurements show the influence of window design parameters on the availability of daylight. The results have then been combined to show a feasible window design regarding daylight availability and the resulting cooling and heating demands for different window orientations. The results show that in most cases it is possible to find a combination of window share and window solar factor that is feasible with regard to daylight as well as cooling and heating. The main finding is that there is a smaller or wider range of feasible designs for different window orientations.

scholarly journals Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

2021 ◽  
Vol 13 (22) ◽  
pp. 12442
Author(s):  
Amal A. Al-Shargabi ◽  
Abdulbasit Almhafdy ◽  
Dina M. Ibrahim ◽  
Manal Alghieth ◽  
Francisco Chiclana
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Residential Buildings ◽  
Arid Climate ◽  
Mean Square ◽  
Heating And Cooling ◽  
Cooling Loads

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

Application of Phase Change Materials in Construction Materials for Thermal Energy Storage Systems in Buildings

2021 ◽  
pp. 1-20
Author(s):  
Eray Mertkan Meric ◽  
Savas Erdem ◽  
Ezgi Gurbuz
Keyword(s):  
Energy Consumption ◽  
Phase Change Materials ◽  
Residential Buildings ◽  
Construction Materials ◽  
Building Design ◽  
World Population ◽  
Total Consumption ◽  
Heating And Cooling ◽  
Thermal Energy Storage Systems ◽  
Developed World

There has been a continuous increase in world population and industrialization. In parallel, energy consumption grew substantially in the world. The residential buildings constitute the majority of the total consumption in the developed world. Therefore, energy saving and reducing heat losses of buildings are of major concern for our society. At this point, phase changing materials (PCMs) show up as one of the most useful materials in building design. In this study, PCMs, which are used at places such as facade of buildings, bricks, and inside of concrete in order to be able to reduce the energy consumption due to heating and cooling, to provide comfort temperature inside buildings, are extensively reviewed.

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