scholarly journals Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1331
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Grasshopper Optimization Algorithm ◽  
Heating Load ◽  
Grasshopper Optimization ◽  
Intelligent Methods ◽  
Heating Loads ◽  
Cooling Loads ◽  
Swarm Size

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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.

scholarly journals Energy Consumption in Residential Buildings: Comparison between Three Different Building Styles

2020 ◽  
Vol 2 (1) ◽  
pp. p1
Author(s):  
Mohamed A. Umbark ◽  
Samah Khalifa Alghoul ◽  
Elhadi I. Dekam
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Residential Buildings ◽  
Weather Conditions ◽  
Electrical Energy ◽  
Local Power ◽  
Water Heating ◽  
Space Cooling ◽  
Heating Loads ◽  
Cooling Loads

More than one-third of the electricity generated in the world is being consumed in the residential sector. This study aims to model, simulate, and estimate electrical energy consumption in three different building styles. That is in order to compare and contrast energy consumption categories and their related social and architectural aspects for an unaddressed region that have its particular weather conditions and its special social and environmental aspects. The simulation is done by detailed modeling of the buildings using EnergyPlus. The results demonstrate that water heating systems account for almost one-fifth of the annual energy consumption. Cooling loads were found to be more than 5 times the heating loads. The peak of energy consumption was recorded to be in July, while the lowermost recorded in April and in November. The Apartment style requires the lowest annual energy consumption by an amount of 10 kWh/m2 per person followed by the Duplex house with 13 kWh/m2 per person, while the Single-Story house comes with the highest energy consumption of 18 kWh/m2 per person. These represent local power consumption of 69, 79, and 90 kWh/m2, respectively. On average, the water heating, space cooling, plus interior lights consume about 60% of total energy requirements with a mostly equal share for each, while the equipment has the maximum share of 35% of the total, leaving about 5% for the rest. The results of this study may be used as a reference line in the future for the calculations of energy savings in similar regions.

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.

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 On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Energy Consumption ◽  
Heuristic Algorithms ◽  
Invasive Weed ◽  
Elman Neural Network ◽  
Heating And Cooling ◽  
Second Tier ◽  
Grasshopper Optimization ◽  
Antlion Optimization ◽  
Teaching Learning ◽  
Cooling Loads

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, ten meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. Besides, an integrative ranking of the models is achieved using average ranking algorithm.

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.

Evaluation of impact of passive design measures with energy saving potential through estimation of shading control for visual comfort

2017 ◽  
Vol 42 (3) ◽  
pp. 220-238 ◽  
Author(s):  
Lakshya Sharma ◽  
K Kishan Lal ◽  
Dibakar Rakshit
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Climatic Conditions ◽  
Design Parameters ◽  
Visual Comfort ◽  
Passive Design ◽  
Lighting Energy ◽  
Insulation Thickness ◽  
Heating Loads ◽  
Cooling Loads

Residential and commercial buildings together account for one-third of world’s final energy consumption, thus making energy management in buildings of considerable significance. Passive design concept that depends on climate and location can be used as an effective and economical method to reduce the energy consumption in buildings. Seven cities in India, each representative of different geographic and climatic conditions, were selected for analysis. This article studies how the peak cooling and heating load are affected by varying some of the passive design parameters for each of the seven cities. The parameters varied are wall insulation thickness, roof insulation thickness, overhang depth, window orientation, and window-to-wall ratio. Results show that optimized passive design could reduce the peak cooling and heating loads by about 50%. Shading reduces cooling loads but is found to increase heating loads. In some of the locations, both heating in winter and cooling in summer are needed and designers should adopt appropriate passive measures depending on the location. Also for the same building, evaluation of shading is done in the context of lighting energy savings. An algorithm has been developed to iteratively alter and analyze set of roller blind positions to maintain visual comfort; as a result, the corresponding potential annual energy savings due to lighting were estimated. It was also observed that even after providing visual comfort to the occupants, energy savings only reduced by approximately 1% as compared to the case when visual comfort was overlooked.

Dividing air handling loads into different grades and handling air with different grade energies

2020 ◽  
pp. 1420326X2096150
Author(s):  
Gonghang Zheng ◽  
Xianting Li
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Air Conditioning ◽  
Air Cooling ◽  
Air Conditioning System ◽  
Energy Saving Potential ◽  
Air Handling Unit ◽  
Chilled Water ◽  
Heating Load ◽  
Heating Loads

In traditional air-conditioning system, low-temperature chilled water is used to cool air. Generally, the temperature difference between air to be operated and the chilled water is high, and majority of air can be operated using water at higher temperatures. Therefore, this paper proposes the concept of grade of load and the method of dividing cooling/heating load into different grades. A traditional air cooling/heating load and energy consumption of fresh air handling unit (FAHU) in Beijing, were compared with cooling/heating loads with different grades and energy consumption of FAHU with different grade energies. The results indicate that cooling and heating loads, handled by the lowest and highest water temperatures of 9.5°C and 37.5°C, account for 27% and 25% of cooling and heating loads in design conditions, respectively. The cumulative cooling/heating load handled by water temperature with highest grade, only accounts for 47%/35% of the total cumulative cooling/heating load. As compared to traditional air handling process, the energy-saving rate of FAHU using different grade energies is 16.4% in summer and 25.6% in winter. This study shows that handling air with different grade energies has significant energy-saving potential for air-conditioning system.

scholarly journals Thermal Analysis of an Educational Building with Different Construction Materials

2018 ◽  
Vol 01 (02) ◽  
pp. 75-86
Author(s):  
Kashif Rasheed ◽  
Shimza Jamil ◽  
Muhammad Ramzan ◽  
Muhammad Zulqarnain
Keyword(s):  
Energy Consumption ◽  
Building Materials ◽  
Construction Materials ◽  
Educational Institutions ◽  
Energy Usage ◽  
Heating And Cooling ◽  
Planning And Design ◽  
Warming Climate ◽  
Heating Load ◽  
Cooling Loads

The energy consumption has been increased to an alarming rate in the current world. This scenario has raised many problems like depletion of energy resources, energy supply difficulties and increased carbon footprint (global warming, climate change). The objective of this research is to minimize the energy consumption in educational institutions. This study will help us in reducing the heating and cooling loads of building and resulting to saving cost. A prototype building was modelled in Autodesk Software, Ecotect 2011 for the climatic zone of Multan to examine the thermal performance with different construction materials. The building studied with different aspects including passive and active techniques, planning and design. These aspects were analyzed and results were evaluated. Various construction materials were listed and examined for the development of energy efficient envelope. The results showed 11.86 % decrease in energy usage including 11.76% decrease in cooling load and 46.59% in heating load with locally available building materials.

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