"Quantitative estimation of cooling load capabilities of residential buildings using machine learning"

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Nedret Bećirović ◽  
Ismail Bejtović ◽  
Jasmin Kevrić
Keyword(s):  
Machine Learning ◽  
Quantitative Estimation ◽  
Residential Buildings ◽  
Cooling Load ◽  
Wall Area ◽  
Area Distribution ◽  
Roof Area ◽  
Study Support ◽  
Input Variables ◽  
Learning Software

Based on previous research on energy efficiency of the buildings, particularly their cooling load capabilities we will develop a collection of machine learning methods for detecting buildings with best cooling load capabilities. This collection will study the influence of 8 input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on one output parameter, that is cooling load of buildings. The results of this study support the practicability of using machine-learning software to estimate building parameters as a convenient and accurate approach, as long as the methods chosen are well suited for the type of data in question.

scholarly journals Estimation of Energy Performance of Buildings Using Machine Learning Tools

2020 ◽  
Author(s):  
Rasoul Rashidifar
Keyword(s):  
Machine Learning ◽  
Residential Buildings ◽  
Energy Performance ◽  
Cooling Load ◽  
Learning Tools ◽  
Learning Framework ◽  
Energy Performance Of Buildings ◽  
Heating Load ◽  
Input Variables

it is developed a machine learning framework to study the effect of eight input variables on two output variables, namely heating load (HL) and cooling load (CL), of residential buildings.

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.

scholarly journals Performance and Techno-economic Analysis of a Hybrid Photovoltaic/thermal System for Building Application in Turkey

2021 ◽  
Author(s):  
Kivanc Basaran ◽  
İlayda Koç
Keyword(s):  
Economic Analysis ◽  
Net Present Value ◽  
Residential Buildings ◽  
Mean Value ◽  
Climatic Conditions ◽  
Simulink Model ◽  
Cost Of Energy ◽  
Equity Ratio ◽  
Roof Area ◽  
Input Variables

Abstract Residential buildings need demand of energy for both heat and electricity. However, it’s not always possible to meet this need by using individual panels due to the limited roof area. In addition, performing electrical and thermal energy production by using separate panels causes loss of performance and efficiency. The hybrid photovoltaic thermal (PV/T) collectors could be used in order to meet this necessity into the same collector. This paper investigates the performance and economic analysis of a PV/T collector for a building application in Turkey climatic conditions. For this purpose, the Matlab/Simulink model of the PV/T collector was prepared. The electrical and thermal performance of the PV/T collector has been investigated by changing various parameters on this model. In addition, market survey was conducted for economic analysis. 11 different input variables such as average daily irradiation, electrical and thermal efficiency, price of electricity and heating, operation and management cost, capital cost, debt to equity ratio, interest rate, discount rate and inflation rate are used to calculate the economic evaluation parameters such as net present value (NPV), levelized cost of energy (LCOE) and payback period (PBP). The results show that, the mean value of LCOE, NPV and PBP are 0.0467 €/kWh, 7905.3 € and 6 years respectively for the project size at 8.96 m2 which is consist of 7 panels in the 25 years life cycle. Also, the average electrical and thermal efficiencies are defined as 13.4% and 69.3% respectively during.

scholarly journals ANALYSIS OF ENERGY-EFFICIENT HOUSE LAYOUT DESIGN IN TROPICAL CLIMATE

2021 ◽  
Vol 47 (1) ◽  
pp. 11-18
Author(s):  
Aisyah Zakiah
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Weather Data ◽  
Cooling Load ◽  
City Development ◽  
Total Energy Consumption ◽  
Wall Area ◽  
Consumption Energy ◽  
Cooling Loads

Energy-efficient residential provision is an essential concern for the present and future city development. Currently, the residential buildings contribute approximately 37.5% to significant energy consumption and carbon emissions, which mainly used for cooling. This research aims to study the house layout arrangement to minimise cooling loads and further reduce energy consumption. Energy efficiency analysis is performed by comparing the cooling load and total energy consumption from variations of the hypothetical design of detached or semi-detached housing layouts commonly built in Indonesia. The calculation of cooling loads and energy consumption is performed by simulation in Energy Plus 8.4 with Jakarta weather data. The results show that the arrangement of the house layout may reduce the cooling load up to 24%. The total conditioned wall area that varies due to the variations of house layouts are found to affect the cooling loads.

scholarly journals Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

2021 ◽  
Vol 13 (15) ◽  
pp. 8298
Author(s):  
Ahmed Salih Mohammed ◽  
Panagiotis G. Asteris ◽  
Mohammadreza Koopialipoor ◽  
Dimitrios E. Alexakis ◽  
Minas E. Lemonis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Residential Buildings ◽  
Input Parameter ◽  
Regression Tree ◽  
Energy Performance ◽  
Classification And Regression Tree ◽  
Coefficient Of Determination ◽  
Tree Model ◽  
Wall Area

In this research, a new machine-learning approach was proposed to evaluate the effects of eight input parameters (surface area, relative compactness, wall area, overall height, roof area, orientation, glazing area distribution, and glazing area) on two output parameters, namely, heating load (HL) and cooling load (CL), of the residential buildings. The association strength of each input parameter with each output was systematically investigated using a variety of basic statistical analysis tools to identify the most effective and important input variables. Then, different combinations of data were designed using the intelligent systems, and the best combination was selected, which included the most optimal input data for the development of stacking models. After that, various machine learning models, i.e., XGBoost, random forest, classification and regression tree, and M5 tree model, were applied and developed to predict HL and CL values of the energy performance of buildings. The mentioned techniques were also used as base techniques in the forms of stacking models. As a result, the XGboost-based model achieved a higher accuracy level (HL: coefficient of determination, R2 = 0.998; CL: R2 = 0.971) with a lower system error (HL: root mean square error, RMSE = 0.461; CL: RMSE = 1.607) than the other developed models in predicting both HL and CL values. Using new stacking-based techniques, this research was able to provide alternative solutions for predicting HL and CL parameters with appropriate accuracy and runtime.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

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