scholarly journals Optimal Combination of External Wall Insulation Thickness and Surface Solar Reflectivity of Non-Residential Buildings in the Korean Peninsula

2021 ◽  
Vol 13 (6) ◽  
pp. 3205
Author(s):  
Jung Ho Kim ◽  
Young Il Kim
Keyword(s):  
Korean Peninsula ◽  
Residential Buildings ◽  
Northern Region ◽  
Cooling Load ◽  
Total Load ◽  
Heating And Cooling ◽  
Present Worth ◽  
Insulation Thickness ◽  
Saving Effect ◽  
Insulation Performance

To delay fossil energy depletion and implement the Paris Climate Change Accord, the South Korean government is attempting to reduce greenhouse gas emissions with the establishment of the 2030 Roadmap. The insulation performance of external walls is being continuously enhanced in the architectural domain. However, Korea’s policy and construction market focuses only on the heat resistance of buildings’ external walls to enhance the insulation performance, leading to an increased thickness of the insulation materials. In this study, the relationship between the surface reflectivity and insulation thickness of external walls was examined to formulate an effective insulation strategy for buildings in Korea. Office buildings of 12 regions in the Korean Peninsula were considered. The dynamic energy simulation program EnergyPlus was used to perform the heating and cooling load analyses. The present worth method was adopted to perform the economic analysis. The analysis of the cooling and heating loads indicated that a change occurred not only in terms of the latitude but also between the Eastern and Western regions. The energy consumption could be reduced by increasing the reflectivity in the Southern region and lowering the reflectivity in the Northern region, based on the total load. In addition, a higher latitude corresponded to a higher energy saving effect owing to the increased insulation thickness. In the case of Jeju Island and Busan, regions with a relatively large cooling load and small heating load, the total load is little affected by insulation thickness at high reflectivity. If the external skin was considered to have the optimal reflectivity, the regions for optimal insulation thickness could be divided into three categories: north, central and south.

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.

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.

Energy Saving Effect of Different Wall Structures Based on Intermittent Energy Use Characteristics in a Hot Summer and Cold Winter Zone

2016 ◽  
Author(s):  
Yanyan Zhu ◽  
Wei Li ◽  
Bin Zhou ◽  
David J. Kukulka
Keyword(s):  
Energy Use ◽  
Three Dimensional ◽  
Transfer Model ◽  
Cold Winter ◽  
Heating And Cooling ◽  
Living Space ◽  
Wall Structures ◽  
Insulation Thickness ◽  
Reduction Of Energy Consumption ◽  
Saving Effect

An analysis is performed for a typical residential living space with intermittent energy use in a hot summer and cold winter zone. Analysis performed in this study include a dynamic, three dimensional heat transfer model that examine the intermittent heating and cooling loads in a typical living space in Shanghai. Different wall structures (non-insulation structure, exterior insulation structure, or interior insulation structure) can have different influences on the energy efficiency in buildings. Results conclude that the interior insulation structure provides the largest reduction of energy consumption in buildings when compared to the other wall structures in a hot summer and cold winter zone. For the interior insulation structure in this study, the typical thermal insulation thickness is 0.03 m.

scholarly journals Investigation of parameters affecting the optimum thermal insulation thickness for buildings in hot and cold climates

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2891-2903
Author(s):  
Ahmet Canbolat ◽  
Ali Bademlioglu ◽  
Kenan Saka ◽  
Omer Kaynakli
Keyword(s):  
Cooling System ◽  
Heating System ◽  
Climatic Conditions ◽  
Graphical Form ◽  
Insulation Material ◽  
Factors Affecting ◽  
Heating And Cooling ◽  
Present Worth ◽  
Insulation Thickness ◽  
Optimum Insulation Thickness

This paper investigates the factors affecting the optimum insulation thickness and its pay-back period, such as heating and cooling energy requirements of building, lifetime, present worth factor, costs of insulation material and installation, costs of energy sources for heating and cooling, heating and cooling system efficiencies, and solar radiation. For this purpose, by considering two cities characterizing the hot and cold climatic conditions, the optimum insulation thickness and its pay-back period have been calculated and a detailed parametric analysis has been carried out. To achieve practical results, the ranges of the parameters considered in the study include the values typically reported in the literature. The variations in the optimum insulation thickness and the pay-back period with all parameters are presented in graphical form. Finally, order of importance and contribution ratios of the examined parameters on the optimum insulation thickness are determined with the help of Taguchi method. It is found that heating degree-days is the most efficient parameter on the optimum insulation thickness with an impact ratio of 27.33% of the total effect while the least efficient parameter is the efficiency of heating system with an impact ratio of 3.21%.

Analysis on the Effect of External-Wall Insulation on Energy Consumption of Residential Building in Lanzhou

2014 ◽  
Vol 936 ◽  
pp. 1496-1501
Author(s):  
Chu Ne Li ◽  
Gang Wang ◽  
Ya Jun Wang
Keyword(s):  
Energy Consumption ◽  
Life Cycle Cost ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Software ◽  
Insulation Layer ◽  
External Wall ◽  
Insulation Thickness ◽  
Building Load ◽  
Saving Effect

Based on the comparison of external wall with different insulation thicknesses and without insulation, residential buildings energy consumption was simulated by using hourly energy consumption simulation software DeST-h in Lanzhou.The effects of external wall insulation on building energy consumption were analyzed. The result shows that the total annual load can be reduced to 42% ~ 72% with the insulation thicknesses vary from 10mm to 80mm. But With the increase of the thickness of the insulation layer, the amount of fluctuation of building load reduce gradually.That is not to say the insulation layer is thicker the energy saving effect is more distinct and there is shoud be the thickness is the most economical thickness. So the economic insulation thickness is determined as 40mm by using a life-cycle cost analysis.

scholarly journals Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6419
Author(s):  
Muhammad Sajjad ◽  
Samee Ullah Khan ◽  
Noman Khan ◽  
Ijaz Ul Haq ◽  
Amin Ullah ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Cooling Load ◽  
Load Prediction ◽  
Unified Framework ◽  
Single Output ◽  
Heating And Cooling ◽  
Low Performance ◽  
High Level

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

scholarly journals Financial and Energetic Optimization of Greek Buildings Insulation

Designs ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 34 ◽  
Author(s):  
Georgios Mitsopoulos ◽  
Evangelos Bellos ◽  
Christos Tzivanidis
Keyword(s):  
Residential Buildings ◽  
Cost Effective ◽  
Objective Evaluation ◽  
Primary Energy ◽  
Building Envelope ◽  
Evaluation Procedure ◽  
Climate Zones ◽  
Heating And Cooling ◽  
Insulation Thickness ◽  
Cooling Loads

The most important parameter in the design of the building envelope is the insulation thickness, because it dramatically influences the heating and cooling loads. The objective of this study is the investigation of different insulation scenarios for the four climate zones of Greece and, more specifically, the cities Heraklion, Athens, Thessaloniki, and Florina. The insulation thickness is examined up to 8 cm and the optimum thickness is determined by the minimization of the simple payback period in order to design a cost-effective system. Moreover, the primary energy consumption, the heating/cooling loads, and the equivalent CO2 emissions are calculated. Furthermore, a multi-objective evaluation procedure of the various insulated scenarios is conducted in order to show the relationship between the energetic and the financial optimization. Generally, it is found that the optimum insulation thickness is around 4 cm for all the climate zones using financial criteria, while the energy criteria indicate higher thicknesses. These results can be applied to the suitable design of Greek residential buildings.

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