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 A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

scholarly journals Optimising the Parameters of a Building Envelope in the East Mediterranean Saharan, Cool Climate Zone

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 43
Author(s):  
Aiman Albatayneh
Keyword(s):  
Residential Buildings ◽  
Building Envelope ◽  
Cooling Load ◽  
Total Energy Consumption ◽  
Heating And Cooling ◽  
Design Variables ◽  
Heating Load ◽  
Wall Construction ◽  
Optimum Solution ◽  
Cooling Loads

Enhancing the energy efficiency and environmental sustainability of buildings is a significant global aim. New construction regulations are, therefore, geared specifically towards low-emission and energy-efficient projects. However, there are numerous and typically competitive priorities, such as making the most of energy usage in residential buildings. This leads to the complex topic of multi-objective optimisation. The primary aim of this research was to reduce the energy consumed for heating and cooling loads in residential buildings in Ma’an City, which is located in the Jordanian Saharan Mediterranean, a cool climate zone. This was achieved by optimising various design variables (window to wall percent, ground floor construction, local shading type, infiltration rate (ac/h), glazing type, flat roof construction, natural ventilation rate, window blind type, window shading control schedule, partition construction, site orientation and external wall construction) of the building envelope. DesignBuilder software (version 6.1) was utilised to run a sensitivity analysis (SA) for 12 design variables to evaluate their influence on both heating and cooling loads simultaneously using a regression method. The variables were divided into two groups according to their importance and a genetic algorithm (GA) was then applied to both groups. The optimum solution selected for the high-importance variables was based on minimising the heating and cooling loads. The optimum solution selected for the low-importance variables was based on the lowest summation of the heating and cooling loads. Finally, a scenario was devised (using the combined design variables of the two solutions) and simulated. The results indicate that the total energy consumption was 1186.21 kWh/year, divided into 353.03 kWh/year for the cooling load and 833.18 kWh/year for the heating load. This was compared with 9969.38 kWh/year of energy, divided into 3878.37 kWh/year for the heating load and 6091.01 kWh/year for the cooling load for the baseline building. Thus, the amount of energy saved was 88.1%, 94.2% and 78.5% for total energy consumption, cooling load and heating load, respectively. However, implementing the modifications suggested by the optimisation of the low-importance variables was not cost-effective, especially the external wall construction and partition construction, and therefore these design variables can be neglected in future studies.

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.

A STUDY ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PREDICTING THE HEATING AND COOLING LOADS OF BUILDINGS

2019 ◽  
Vol 14 (3) ◽  
pp. 115-128 ◽  
Author(s):  
Sushmita Das ◽  
Aleena Swetapadma ◽  
Chinmoy Panigrahi
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficient ◽  
Energy Performance ◽  
Accurate Estimation ◽  
Cooling Load ◽  
Alternative Technologies ◽  
Heating And Cooling ◽  
Energy Efficient Buildings ◽  
Cooling Loads ◽  
Heating And Cooling Load

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.

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 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 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.

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.

Sign in / Sign up

Export Citation Format

Share Document