Low Exergy Heating and Cooling in Residential Buildings

2011 ◽  
Author(s):  
Michael Jochum ◽  
Gokulakrishnan Murugesan ◽  
Kelly Kissock ◽  
Kevin Hallinan
Keyword(s):  
Cooling System ◽  
Residential Buildings ◽  
Thermal Mass ◽  
Design Strategies ◽  
High Quality ◽  
Small Temperature ◽  
Transfer Rates ◽  
Heating And Cooling ◽  
Solar Wall ◽  
Cooling Loads

Exergy is destroyed when work is degraded by friction and turbulence and when heat is transferred through finite temperature differences. Typical HVAC systems use a combination of high quality energy from combustion and electricity to overcome relatively small temperature differences between the building and the environment. It is possible to achieve the heating/cooling necessary to maintain comfort in a building without these high quality energy sources and their high potential-energy destruction. A low-exergy heating and cooling system seeks to better match the quality of energy to the loads of the building and thus to minimize exergy destruction and increase the exergetic efficiency of the building’s heating and cooling system. The method described here for low exergy building system design begins by minimizing overall heating and cooling loads using a tight, highly-insulated envelope and passive solar design strategies. Next a low-exergy heating and cooling system is designed that uses hydronic radiant heating and cooling in floors, along with high thermal mass. The large surface area of the floors enable low fluid flow rates and relatively small temperature differences to achieve heat transfer rates that would traditionally be driven by high temperature differentials and flows. The building uses a solar wall to passively drive ventilation requirements and earth tubes to condition the ventilation air. High thermal mass in the floor reduces peak loads and eliminates the need for solar thermal storage tanks. Thus, this paper begins to explore the practical limits of low-exergy design.

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.

scholarly journals Impact of urban heat island effect on the heating and cooling loads of residential buildings in Tianjin City, China

2020 ◽  
Vol 39 (8) ◽  
pp. 1296-1307
Author(s):  
Fanchao MENG ◽  
Guoyu REN ◽  
Jun GUO ◽  
Lei ZHANG ◽  
Ruixue ZHANG ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Residential Buildings ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Heating And Cooling ◽  
Island Effect ◽  
Urban Heat ◽  
Tianjin City ◽  
Cooling Loads

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 Prediction of Indoor Air Circulation of Residential Room with Adaptation of Solar Chimney Using Numerical Technique

2019 ◽  
Vol 8 (11) ◽  
pp. 4110-4116
Keyword(s):  
Air Temperature ◽  
Cooling System ◽  
Residential Buildings ◽  
Numerical Technique ◽  
Electrical Power ◽  
Air Gap ◽  
Solar Chimney ◽  
Heating And Cooling ◽  
Gap Width ◽  
Ventilation Velocity

With the exponential increase in consumption of electrical power during the summer season by household, there is a great need for households to withhold sustainability. To maintain the temperature of the household a passive heating and cooling system is used i.e. Solar Chimney. Ventilation, through a natural convection process, is gaining a lot of attention to be an alternative technique for mechanical air conditioning ventilation because of its reduced power usage when compared to the external cooling devices used in residential buildings of hot regions. The present study, involve solar chimney of horizontal and vertical designs in comparison with different width and height. The following paper studies the effect of a solar chimney on the indoor thermal behavior using Numerical Technique for a prototype of a residential room. The performance on the ventilation velocity and air temperature operation inside the room with varying air gap width is studied based on multiple numerical analysis solutions. The present study deals with two different architectures of a two dimensional model and results have shown that the ventilation velocity has increased to 0.017626444 kg/s and operative air temperature has been decreased by 7.26ºC for the vertical model while the horizontal model has shown a mass flow rate of 0.018027636 kg/s and a temperature decrease of 9.15ºC. The most efficient chimney was found to be model 7 which is horizontal solar chimney 3 with an air gap width of 0.05625m and a height of 0.3175 m, when compared to the other models from model number one to six.

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.

Optimization of Thermal Mass in Commercial Building Applications

1990 ◽  
Vol 112 (4) ◽  
pp. 273-279 ◽  
Author(s):  
M. Judson Brown
Keyword(s):  
Energy Savings ◽  
Thermal Storage ◽  
Office Building ◽  
Base Case ◽  
Thermal Mass ◽  
Commercial Building ◽  
Heating And Cooling ◽  
Initial Cost ◽  
Heavy Construction ◽  
Cooling Loads

Based on results from a one-year intensive monitoring project of a Northern New York commercial building with energy-conserving design features, a thermal storage project was undertaken to optimize the design of a thermal mass storage system for a moderately sized commercial building and transfer the technology to the commercial building sector. A generic commercial building design of 27,000 square feet (2508 m2) was selected for the optimization project. Several different types of thermal mass designs were considered as potentially practical for a commercial building. These included a “sandmass” design such as the mass incorporated in the previously monitored commercial building mentioned above, a foundation slab of sufficient thickness to serve as a significant building thermal mass, and the use of poured cement in interior wall and floor construction. Five different office building thermal designs were selected which represented various thermal storage features and two different building insulation levels (R10 and R20). Energy performance of the five thermal designs was modeled in building energy simulations using DOE 2.1C (Department of Energy 2.1C) energy simulation code. Results of the simulations showed a reduction in peak heating and cooling loads would be experienced by the HVAC equipment. The reduction in peak heating and cooling loads was anticipated because thermal mass within a building serves to average peak heating and cooling loads due to the capacity of the thermal mass to store and release heat from all building heat sources over a period of time. Peak heating loads varied from 1972 kBtuh (578 kW) for the R-10 light construction base case to a minimum of 980 kBtuh (287 kW) for the R-20 heavy construction sandmass storage case. Peak cooling loads dropped from 772 kBtuh (226 kW) for the R-20 light construction case to 588 kBtuh (172 kW) for the R-20 heavy construction sandmass storage case. Results of the simulations also showed annual energy savings for the high thermal mass designs. Energy savings varied from 20 percent [16.0 kBtu/ft2 (50 kWh/m2)] for the R-10 high thermal mass design in comparison to its base case to 18 percent [12.2 kBtu/ft2 (39 kWh/m2)] for the R-20 high thermal mass design in comparison to its base case. The annual energy savings are due to the ability of the thermal mass to absorb heat from all sources of heat generation (lights, occupancy, solar, and auxiliary) during occupied periods and release the heat during unoccupied periods. An optimized thermal design was developed based on results from the DOE 2.1C simulations. The initial cost for the optimized thermal storage design is lower than the initial costs for light construction office buildings, since the lower initial cost of the down-sized HVAC system for the optimized thermal storage design more than offsets the increased cost of wall and floor systems incorporated in the optimized design. Annual energy savings are realized from the high thermal mass system in both cooling and heating modes due to the interaction of building HVAC systems operation in the simulated 27000 ft2 (2508 m2) office building. Annual operating savings of $3781 to $4465 per year are estimated based on simulation results.

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