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 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.

The Effects of Window Film on the Performance of Window System

2014 ◽  
Vol 525 ◽  
pp. 408-411
Author(s):  
Min Seon Jang ◽  
Gyeong Seok Choi ◽  
Jae Sik Kang ◽  
Yumin Kim
Keyword(s):  
Energy Consumption ◽  
Cooling Load ◽  
Indoor Temperature ◽  
Heating And Cooling ◽  
Window System ◽  
Window Systems ◽  
Clear Glass ◽  
Window Film ◽  
And Performance ◽  
Heating And Cooling Load

Window film is generally attached the glazing in buildings to improve the thermal performance of the window system by addressing a range of problems such as indoor temperature rise, indoor temperature imbalance, degraded heating and cooling load due to excessive influx of solar radiation. To evaluate the performance of window films, window films are attached to 3mm or 6mm clear glass. However, window films are generally used on existing window systems for reducing the annual energy consumption. Therefore it is necessary to evaluate the performance of window films depending on the performance of glazing such as clear double glazing or low-e double glazing. Thus the purpose of this study is to analyze the performance of window systems when window film is attached. As a result, in the case of applying window films for reducing the SHGC of buildings, it is necessary to select window films suitable for the configuration and performance of the glazing to be installed, considering the SHGC of the entire glazing system.

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.

Heating and Cooling Load Characteristics Comparison between Normal Building and Low Energy Consumption Building

2014 ◽  
Vol 672-674 ◽  
pp. 1855-1858
Author(s):  
Yuan Su ◽  
Fu Lin Wang ◽  
Yue Fan
Keyword(s):  
Energy Consumption ◽  
Low Energy ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Low Energy Consumption ◽  
Load Characteristics ◽  
Heating And Cooling Load

In this research, a normal building and low energy consumption building were chosen to compare and analyze heating and cooling load characteristics. Firstly, the abstract of two buildings were carried out. Secondly, methodology of measurement and calculation was researched. At last the heating and cooling load of two buildings was examined using this methodology.

Analysis of the Indoor Thermal Load Variations Depending on the Shading Device Parameters and Window Orientation

2015 ◽  
Vol 23 (03) ◽  
pp. 1550023 ◽  
Author(s):  
Yeo Beom Yoon ◽  
Rashmi Manandhar ◽  
Kwang Ho Lee
Keyword(s):  
Vertical Wall ◽  
Building Performance ◽  
Cooling Load ◽  
Load Variations ◽  
Heating And Cooling ◽  
Window System ◽  
Shading Devices ◽  
Shading Device ◽  
Cooling Loads ◽  
Heating And Cooling Load

Many studies have been done to study the advantage of using window shading devices as a means of controlling solar penetration into the building. Shading devices like blinds have been proved to have a significant effect on the heating and cooling load of the building. As it is easier and less costly to change blinds than changing the window system in a building, using blinds is a very effective way of improving building performance. Although many studies have been done, mostly the study focuses on window that is oriented towards the south. As it is obvious that in a real building windows can be facing any direction, in this study the effect of blinds on heating and cooling loads of a building has been done, when the design of blind is either horizontal or vertical, when it is placed either inside or outside and when the slat angle automatically changes based on either solar energy received on vertical wall or on horizontal surface (roof).

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.

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