98/03320 A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings

The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.

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Heating and Cooling Application in Energy Efficient Buildings using Trombe Wall: A Review

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1130/1/012015 ◽

2021 ◽

Vol 1130 (1) ◽

pp. 012015

Author(s):

S. Navakrishnan ◽

B. Sivakumar ◽

R. Senthil ◽

Rajendran Senthil Kumar

Keyword(s):

Energy Efficient ◽

Heating And Cooling ◽

Energy Efficient Buildings ◽

Trombe Wall

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Energy Efficient Residential Block Design

Sustainable Practices ◽

10.4018/978-1-4666-4852-4.ch057 ◽

2013 ◽

pp. 1027-1046 ◽

Cited By ~ 2

Author(s):

Hakan Hisarligil ◽

Sule Karaaslan

Keyword(s):

Energy Efficient ◽

Energy Use ◽

Block Design ◽

Methodological Approach ◽

Sustainable Urban Development ◽

Efficient Design ◽

Heating And Cooling ◽

Building Energy Analysis ◽

Cooling Loads ◽

User Friendly

This chapter presents a methodological approach to residential block design for sustainable urban development for hot-summer and cold-winter climates. Taking Ankara as a case, its focus is on developing an energy efficient design process as regards residential block geometry with optimum performance for both climate and energy use. The numerous variables analyzed are orientation, building geometry and envelope, heating and cooling loads of buildings, and microclimatic conditions including solar radiation, air, and wall temperature, and wind speed. It is also important in this study to demonstrate the potential use of “free and user-friendly” simulation tools for such analysis in the early design phase for those who are not experts but have moderate knowledge of urban microclimate and energy. For this aim Weather Tool v2.00 for climate and passive design analysis, CASAnova 3.0 for building energy analysis, and ENVI-met 3.0 for microclimatic analysis are used.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.819.541 ◽

2016 ◽

Vol 819 ◽

pp. 541-545 ◽

Cited By ~ 8

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.

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New developments in illumination, heating and cooling technologies for energy-efficient buildings

Energy ◽

10.1016/j.energy.2009.05.020 ◽

2010 ◽

Vol 35 (6) ◽

pp. 2647-2653 ◽

Cited By ~ 33

Author(s):

H.J. Han ◽

Y.I. Jeon ◽

S.H. Lim ◽

W.W. Kim ◽

K. Chen

Keyword(s):

Energy Efficient ◽

New Developments ◽

Heating And Cooling ◽

Energy Efficient Buildings

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Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

Applied Sciences ◽

10.3390/app9173543 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3543 ◽

Cited By ~ 13

Author(s):

Dieu Tien Bui ◽

Hossein Moayedi ◽

Dounis Anastasios ◽

Loke Kok Foong

Keyword(s):

Energy Efficient ◽

Residential Buildings ◽

Influential Factors ◽

Ann Model ◽

Wall Area ◽

Heating And Cooling ◽

Energy Efficient Buildings ◽

Performance Error ◽

Artificial Neural Network Ann ◽

Intelligent Models

Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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ENERGY EFFICIENT IMPROVEMENT OF HISTORIC BUILDINGS: A CASE STUDY IN SINOP

Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences ◽

10.38065/euroasiaorg.730 ◽

2021 ◽

Vol 8 (18) ◽

pp. 11-25

Author(s):

Gökhan GENÇ ◽

Figen BEYHAN

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

State Energy ◽

Residential Building ◽

Maximum Energy ◽

Historical Buildings ◽

Heating And Cooling ◽

Physical Effects ◽

Cooling Loads

Although historical buildings are ecological with their construction systems and materials, they cannot provide necessary performance in today's comfort conditions and therefore they are abandoned and remain in a damaged or dysfunctional state. Energy efficient improvement works are carried out in historical buildings in order to bring the historical buildings today's conditions, re-use and ensure their sustainability. However, there are many limitations in these studies due to the heritage characteristics of historical buildings. With these limitations, the works to be done should be carried out with the least intervention without damaging the heritage values of the historical buildings. For this reason, it is necessary to specially select the applications to be realized within the scope of energy efficiency in historical buildings and scaling the physical effects of the applications relative to each other. In this context, in this study, it is aimed to reveal the appropriate improvement methods in order to reach the maximum energy efficiency with the least physical intervention, with the techniques suitable for the historical texture by preserving the original qualities in the historical buildings. Based on the Historic England intervention evaluation scale developed in this framework, 5 scenarios, including the current situation and 4 different design scenarios, including interventions from small to large impacts, were created on a sample historical residential building, and the data of each scenario in terms of energy consumption were obtained. Models created within the framework of the scenarios were evaluated with the Design Builder simulation program, and annual heating and cooling loads and the amount of energy consumed per total m² were obtained. Evaluations were made by comparing the energy efficiency of applications at different degrees with the graphics and tables prepared in the light of these data. As a result, suggestions have been developed regarding the interventions to be made to historical buildings according to the intervention effect sizes in the context of energy efficiency with the evaluations made.

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The Effect of Density on the Delicate Balance between Structural Requirements and Environmental Issues for AAC Blocks: An Experimental Investigation

Sustainability ◽

10.3390/su132313186 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13186

Author(s):

Daniele Ferretti ◽

Elena Michelini

Keyword(s):

Thermal Conductivity ◽

Energy Efficient ◽

Construction Materials ◽

Mechanical Index ◽

Material Density ◽

Safety Issues ◽

Heating And Cooling ◽

Energy Efficient Buildings ◽

Optimum Material ◽

Energy Efficient Building

Among other construction materials, Autoclaved Aerated Concrete (AAC) offers several advantages to face the pressing need to build more sustainable and energy-efficient buildings. From the building side, the low thermal conductivity of AAC allows the realization of energy-efficient building envelopes, with interesting savings in terms of heating and cooling processes. The equilibrium between structural performances (related to safety issues) and energy efficiency requirements is, however, very delicate since it is strictly related to the search for an “optimum” material density. Within this context, this work discusses the results of wide experimental research, showing the dependency of the most important mechanical properties (compressive strength, elastic modulus, flexural strength and fracture energy) from density, as well as the corresponding variation in thermal conductivity. In order to identify the better compromise solution, a sort of eco-mechanical index is also defined. The big challenge for future researches will be the improvement of this eco-mechanical index by working on pore structure and pore distribution within the material without significantly reducing the density and/or by improving the strength of the skeleton material.

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