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 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.

Future trends of building heating and cooling loads and energy consumption in different climates

2011 ◽  
Vol 46 (1) ◽  
pp. 223-234 ◽  
Author(s):  
Kevin K.W. Wan ◽  
Danny H.W. Li ◽  
Dalong Liu ◽  
Joseph C. Lam
Keyword(s):  
Energy Consumption ◽  
Future Trends ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Different Climates

Designing a mediator space and the study of its effect on the energy consumption of a residential building using EnergyPlus software in Savadkuh, Iran

2019 ◽  
Vol 17 (4) ◽  
pp. 833-846
Author(s):  
Yasaman Yousefi ◽  
Mehdi Jahangiri ◽  
Akbar Alidadi Shamsabadi ◽  
Afshin Raeesi Dehkordi
Keyword(s):  
Energy Consumption ◽  
Design Methodology ◽  
Residential Building ◽  
Environmental Costs ◽  
Content Type ◽  
Design And Implementation ◽  
Heating And Cooling ◽  
Energy Simulations ◽  
Cooling Loads ◽  
Reducing Energy

Purpose Reducing energy consumption of a building may have a significant effect on the energy and environmental costs. Nowadays, energy simulations have come to the aid of engineers in the design and implementation of buildings with a perspective on energy consumption. Design/methodology/approach In the current study, the suggested volume of a residential building in the Savadkuh City, Iran, is modeled using Ecotect® software, and the amount of radiation on the sides during various months of the year is studied. Then, using EnergyPlus™ software, climate analyses are performed on the suggested design, and finally, the amount of heating and cooling loads of the building are examined under two difference scenarios of mediator space. Findings Results indicated that nearly at all times of the year, both the heating and cooling loads were reduced in the scenario where mediator space had two functions, i.e. as greenhouse and as a space for higher ventilation, compared to the scenario where mediator space did not have a climate role and merely served as an entrance and passageway with rigid dividers. Originality/value Nowadays, energy simulations have come to the aid of engineers in the design and implementation of buildings with a perspective on energy consumption. Therefore, in the current study, the suggested volume of a residential building in the Savadkuh City, Iran, is modeled using Ecotect® software, and the amount of radiation on the sides during various months of the year is studied. Then, using EnergyPlus™ software, climate analyses are performed on the suggested design, and finally, the amount of heating and cooling loads of the building are examined under two difference scenarios of mediator space.

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 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 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.

Variable Air Volume System Application in Museums

2007 ◽  
Author(s):  
Zhan Wang ◽  
Bin Zheng ◽  
Wenlong Xu ◽  
Gang Wang ◽  
Mingsheng Liu
Keyword(s):  
Energy Consumption ◽  
System Control ◽  
Variable Air Volume ◽  
Heating And Cooling ◽  
Air Volume ◽  
Variable Frequency Drives ◽  
Air Handling Units ◽  
Simultaneous Heating ◽  
Simultaneous Heating And Cooling ◽  
Cooling Loads

Relative humidity and temperature control is key in museums, galleries, libraries and archives. Normally constant volume (CV) air handling units (AHUs) with reheat coils are applied in these buildings. Setting a low supply air temperature limits the highest humidity level; however, reheat coils have to be used to maintain space temperature due to constant supply airflow. As a result, simultaneous heating and cooling exists with excessive energy consumption. It is well known that variable air volume (VAV) technologies can reduce simultaneous heating and cooling as well as fan power. This paper presents the detail VAV system retrofit for the existing CV system, control sequence development and system performance evaluation in a museum facility at Omaha, Nebraska. Variable Frequency Drives (VFDs) were installed for the supply fan on the AHUs. Space humidity and temperature, heating and cooling energy consumption, and fan power were measured. The measurements showed that the space humidity and temperature was maintained within the required range under VAV operation while the reheat consumption was reduced by up to 85% and the fan power consumption was reduced by 90% under partial cooling loads.

scholarly journals The Significance of Temperature Based Approach Over the Energy Based Approaches in the Buildings Thermal Assessment

2017 ◽  
Vol 19 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Aiman Albatayneh ◽  
Dariusz Alterman ◽  
Adrian Page ◽  
Behdad Moghtaderi
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Thermal Performance ◽  
Low Energy ◽  
Heating And Cooling ◽  
Adaptive Thermal Comfort ◽  
Brick Veneer ◽  
Time Required ◽  
Cooling Loads ◽  
Thermal Comfort Model

Abstract The design of low energy buildings requires accurate thermal simulation software to assess the heating and cooling loads. Such designs should sustain thermal comfort for occupants and promote less energy usage over the life time of any building. One of the house energy rating used in Australia is AccuRate, star rating tool to assess and compare the thermal performance of various buildings where the heating and cooling loads are calculated based on fixed operational temperatures between 20 °C to 25 °C to sustain thermal comfort for the occupants. However, these fixed settings for the time and temperatures considerably increase the heating and cooling loads. On the other hand the adaptive thermal model applies a broader range of weather conditions, interacts with the occupants and promotes low energy solutions to maintain thermal comfort. This can be achieved by natural ventilation (opening window/doors), suitable clothes, shading and low energy heating/cooling solutions for the occupied spaces (rooms). These activities will save significant amount of operating energy what can to be taken into account to predict energy consumption for a building. Most of the buildings thermal assessment tools depend on energy-based approaches to predict the thermal performance of any building e.g. AccuRate in Australia. This approach encourages the use of energy to maintain thermal comfort. This paper describes the advantages of a temperature-based approach to assess the building’s thermal performance (using an adaptive thermal comfort model) over energy based approach (AccuRate Software used in Australia). The temperature-based approach was validated and compared with the energy-based approach using four full scale housing test modules located in Newcastle, Australia (Cavity Brick (CB), Insulated Cavity Brick (InsCB), Insulated Brick Veneer (InsBV) and Insulated Reverse Brick Veneer (InsRBV)) subjected to a range of seasonal conditions in a moderate climate. The time required for heating and/or cooling using the adaptive thermal comfort approach and AccuRate predictions were estimated. Significant savings (of about 50 %) in energy consumption in minimising the time required for heating and cooling were achieved by using the adaptive thermal comfort model.

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)).

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