Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Energy    2021, Vol. 15 Issue (2) : 550-563    https://doi.org/10.1007/s11708-019-0607-1
RESEARCH ARTICLE
An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties with maximum thermal performance
Yaolin LIN1(), Wei YANG2
1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
2. College of Engineering and Science, Victoria University, Melbourne 8001, Australia
 Download: PDF(1069 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triple-layer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.
Keywords ANN (artificial neural network)      exhaustive-listing      building shape      optimization      thermal load      thermal comfort     
Corresponding Author(s): Yaolin LIN   
Online First Date: 14 January 2019    Issue Date: 18 June 2021
 Cite this article:   
Yaolin LIN,Wei YANG. An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties with maximum thermal performance[J]. Front. Energy, 2021, 15(2): 550-563.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-019-0607-1
https://academic.hep.com.cn/fie/EN/Y2021/V15/I2/550
Reference Building types/shapes Objective functions
Ref. [13] Multi-sided polygon floor Life cycle cost;
life cycle environmental impact
Ref. [15] Rectangle, L, T, cross, U, H, and trapezoid Life cycle cost
Ref. [17] Free-form Solar radiation gain;
space efficiency;
shape coefficient
Ref. [10]
Ref. [19]
Ref. [21]		 Free-form Solar heat gain
Ref. [9] Two arbitrary curves bounding the south and north faces, prismatic shape Heating cost;
building cost
Ref. [12] Free-form External thermal load
Ref. [18] Multi-sided polygon floor Building construction cost;
seasonal heating energy demand;
pollution emitted by heat sources
Ref. [14] Deformed Building energy consumption
Tab.1  Summary of objective functions for building shape optimization
Fig.1  Overview of four types of buildings
Design variable Value
Building area (S)/m2 100
Building height (h)/m 3.6
Building volume (V)/m3 360
Heating temperature setpoint/°C 18
Cooling temperature setpoint/°C 26
U-value of external wall/(W?(m2?K)–1) 0.395
U-value of roof/(W?(m2?K)–1) 0.363
Window-to-external surface area ratio/% 10
Tab.2  Initial values of building parameters
Items Components
External wall Wall 10: Face brick+ 10 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 20: Face brick+ 20 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 30: Face brick+ 30 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 40: Face brick+ 40 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 50: Face brick+ 50 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 60: Face brick+ 60 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 70: Face brick+ 70 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 80: Face brick+ 80 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 90: Face brick+ 90 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 100: Face brick+ 100 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 110: Face brick+ 110 mm XPS board+ 240 mm concrete block+ gypsum plaster
Wall 120: Face brick+ 120 mm XPS board+ 240 mm concrete block+ gypsum plaster
Roof Roof 10: protection layer+ water-proof layer+ 10 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 20: protection layer+ water-proof layer+ 20 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 30: protection layer+ water-proof layer+ 30 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 40: protection layer+ water-proof layer+ 40 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 50: protection layer+ water-proof layer+ 50 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 60: protection layer+ water-proof layer+ 60 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 70: protection layer+ water-proof layer+ 70 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 80: protection layer+ water-proof layer+ 80 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 90: protection layer+ water-proof layer+ 90 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 100: protection layer+ water-proof layer+ 100 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 110: protection layer+ water-proof layer+ 110 mm PU board+ 240 mm concrete block+ gypsum plaster
Roof 120: protection layer+ water-proof layer+ 120 mm PU board+ 240 mm concrete block+ gypsum plaster
Window Type 1: 6 mm single layer clear glazing
Type 2: 6 mm single layer low-E glazing
Type 3: double layer 6 mm clear glazing+ 13 mm air gap
Type 4: double layer 6 mm low-E glazing+ 13 mm air gap
Type 5: triple layer 3 mm clear glazing+ 13 mm air gap
Type 6: triple layer 3 mm low-E glazing+ 13 mm air gap
Tab.3  Variable types and value ranges for envelope design
City Latitude/(°) Longitude/(°) HDD18/(°C·d) CDD26/(°C·d) Average OAT/(°C) Climatic region
Harbin 45.75 126.77 5032 14 4.2 Severe cold region
Beijing 39.9 116.4 2699 94 12.3 Cold region
Wuhan 30.62 114.13 1501 283 16.7 Hot summer and cold winter region
Guangzhou 23.13 113.32 373 313 22.1 Hot summer and warm winter region
Kunming 25.02 102.68 1103 0 14.9 Mild region
Tab.4  Climatic information for 5 selected cities
Harbin Beijing Wuhan Guangzhou Kunming
Pyramid 16401 11104 11377 11740 3091
Rectangular 16058 10516 10537 10758 2716
Cylindrical 15596 10319 10202 10608 2689
Dome 14606 9796 9816 9952 2709
Tab.5  Base thermal load of selected cities/kWh
Fig.2  Optimization framework
Alpha Initial Eta High Eta Eta Decay Low Eta
0.9 0.3 0.1 30 0.01
Tab.6  ANN parameters
Method Simulation Prediction Relative error/%
Ndis/h Thermal load/kWh Ndis/h Thermal load/kWh Ndis/h Thermal load/kWh
Method one Max 7306 24596.3 7282.0 24472.9 0.42 7.96
Min 6573.5 6640.9 6589.5 7166.2 –0.33 –4.44
Method two Max 7219.5 24719.5 7204.7 24447.1 0.55 4.25
Min 6537.5 5750.2 6564.9 5929.8 –0.72 –4.73
Method three Max 7219.5 24719.5 7202.7 24444.7 0.47 4.34
Min 6537.5 5750.2 6568.1 5967.3 –0.52 –1.95
Tab.7  Ranges of relative errors using different methods for the city of Wuhan
Fig.3  Regression between ANN outputs and simulated targets
Fig.4  Variations of Ndis and thermal loads
Insulation thickness/mm WESR/% Window
type		 Building
shape		 Prediction Simulation Relative error/%
Ndis/h Thermal load /kWh Ranking Ndis/h Thermal load /kWh Ranking Thermal load Ndis
Method one 120 10 6 2 6608.0 7283.3 1 6168.9 6671.5 1 –18.06 0.95
120 15 6 4 6663.3 7192.4 2 6746.1 6670.5 10 –6.61 0.11
120 10 6 3 6676.5 7047.7 3 5911.4 6738.5 3 –19.22 0.92
110 10 6 2 6624.7 7335.6 4 6300.1 6698.5 5 –16.44 1.10
120 15 6 3 6655.1 7257.2 5 6863.0 6663.0 – –5.74 0.12
120 10 6 4 6685.1 7028.5 6 5750.2 6735.0 2 –22.23 0.74
120 10 5 2 6657.3 7295.9 7 6993.6 6684.0 – –4.32 0.40
110 15 6 4 6688.0 7232.1 8 6843.7 6686.5 – –5.68 –0.02
110 10 6 3 6704.7 7070.4 9 6042.0 6754.5 6 –17.02 0.74
100 10 6 2 6646.5 7394.8 10 6459.4 6725.0 8 –14.48 1.17
Method two 120 10 6 2 6185.8 6653.2 1 6168.9 6671.5 1 –0.27 0.27
110 10 6 2 6188.1 6688.1 2 6300.1 6698.5 5 1.78 0.16
120 15 6 2 6426.3 6616.4 3 7149.7 6599.0 – 10.12 –0.26
100 10 6 2 6192.4 6721.5 4 6459.4 6725.0 8 4.13 0.05
120 10 6 3 6004.6 6740.4 5 5911.4 6738.5 3 –1.58 –0.03
110 15 6 2 6509.9 6648.0 6 7269.6 6622.0 – 10.45 –0.39
110 10 6 3 6042.6 6746.7 7 6042.0 6754.5 6 –0.01 0.12
120 10 6 4 5929.8 6751.5 8 5750.2 6735.0 2 –3.12 –0.24
110 10 6 4 5967.0 6757.8 9 5864.8 6753.0 4 –1.74 –0.07
120 10 4 2 6490.4 6677.6 10 6812.7 6684.0 – 4.73 0.10
Method three 120 10 6 2 6249.3 6663.9 1 6168.9 6671.5 1 –1.30 0.11
110 10 6 2 6346.8 6683.9 2 6300.1 6698.5 5 –0.74 0.22
120 10 6 4 5966.0 6733.7 3 5750.2 6735.0 2 –3.75 0.02
120 10 6 3 6162.3 6735.3 4 5911.4 6738.5 3 –4.2 0.05
110 10 6 4 6021.6 6746.3 5 5864.8 6753.0 4 –2.67 0.10
100 10 6 4 6096.2 6759.3 6 6027.8 6767.0 7 –1.14 0.11
110 10 6 3 6239.3 6749.1 7 6042.0 6754.5 6 –3.26 0.08
100 10 6 2 6466.7 6705.4 8 6459.4 6725.0 8 –0.11 0.29
120 15 6 4 6467.2 6709.1 9 6746.1 6670.5 10 4.13 –0.58
90 10 6 4 6197.6 6773.1 10 6182.1 6786.0 9 –0.25 0.19
Tab.8  List of optimal solutions using different methods
Fig.5  Comparison of optimal solutions using the AEL and ANNGA approaches
City Insulation thickness/mm WESR/% Window type Building shape Ndis/h Thermal load/kWh Ranking
Harbin 120 15 6 4 6224.5 9335.0 1
110 15 6 4 6250.5 9566.7 2
120 15 6 3 6217.5 9831.2 3
120 15 6 2 6171.5 9895.9 4
120 10 6 2 6303.5 9600.6 5
120 10 6 1 6228.0 9973.9 6
120 10 6 4 6339.0 9001.7 7
110 15 6 3 6252.0 10101.3 8
110 10 6 4 6372.0 9256.4 9
120 10 6 3 6380.5 9526.6 10
Beijing 120 10 6 3 5912.0 5779.6 1
120 10 6 4 5957.5 5530.6 2
120 10 6 2 5883.0 5879.4 3
110 10 6 4 5987.5 5669.3 4
110 10 6 3 5940.0 5939.0 5
110 10 6 2 5907.5 6050.9 6
120 15 6 4 6006.0 6168.2 7
120 15 6 3 5962.5 6382.8 8
120 15 6 2 5917.0 6480.1 9
110 15 6 4 6030.0 6291.5 10
Wuhan 120 10 6 2 6671.5 6168.9 1
120 10 6 4 6735.0 5750.2 2
120 10 6 3 6738.5 5911.4 3
110 10 6 4 6753.0 5864.8 4
110 10 6 2 6698.5 6300.1 5
110 10 6 3 6754.5 6042.0 6
100 10 6 4 6767.0 6027.8 7
100 10 6 2 6725.0 6459.4 8
90 10 6 4 6786.0 6182.1 9
120 15 6 4 6670.5 6746.1 10
Guangzhou 120 10 6 4 5927 6381.68 1
110 10 6 4 5946 6435.16 2
120 10 6 3 5902 6573.55 3
110 10 6 3 5922 6644.1 4
100 10 6 4 5976 6498.52 5
90 10 6 4 5984.5 6637.22 6
100 10 6 3 5947.5 6731.04 7
90 10 6 3 5978 6832.34 8
80 10 6 4 6036 6735.36 9
120 10 6 2 5887 7038.98 10
Kunming 120 10 6 2 4588 235.82 1
120 10 4 2 4553 357.68 2
110 10 6 2 4676.5 233.13 3
100 10 4 2 4713.5 349.72 4
100 10 6 2 4760.5 231.8 5
120 10 6 4 4763 232.86 6
120 10 4 4 4733 358.78 7
120 10 4 3 4861.5 273.87 8
90 10 6 2 4886.5 231.8 9
120 10 6 3 4923 179.93 10
Tab.9  List of top 10 optimal solutions for different cities
f1 Total building thermal load/kWh
f2 Total number of discomfort hours/h
h Building height/m
Li Lower bound of xi
m Number of the design variables
QC Accumulated hourly cooling load/kWh
QH Accumulated hourly heating load/kWh
r Radius of the base of the building/m
S Building area/m2
Ui Upper bound of xi
V Building volume/m3
WESR Window-to-external envelop surface area ratio, dimensionless
X Combination of the design-variables (x1, x2, ..., xm)
ALT Altitude
ANN Artificial neural network
ATT Annual air temperature
CDD Cooling degree days
HDD Heating degree days
LAT Latitude
LON Longitude
OAT Outside air temperature
  
1 P Depecker, C Menezo, J Virgone, S Lepers. Design of buildings shape and energetic consumption. Building and Environment, 2001, 36(5): 627–635
https://doi.org/10.1016/S0360-1323(00)00044-5
2 U T Aksoy, M Inalli. Impacts of some building passive design parameters on heating demand for a cold region. Building and Environment, 2006, 41(12): 1742–1754
https://doi.org/10.1016/j.buildenv.2005.07.011
3 I Y Choi, S H Cho, J T Kim. Energy consumption characteristics of high-rise apartment buildings according to building shape and mixed-use development. Energy and Building, 2012, 46: 123–131
https://doi.org/10.1016/j.enbuild.2011.10.038
4 M Premrov, V Žegarac Leskovar, K Mihalič. Influence of the building shape on the energy performance of timber-glass buildings in different climatic conditions. Energy, 2016, 108: 201–211
https://doi.org/10.1016/j.energy.2015.05.027
5 A Okeil. A holistic approach to energy efficient building forms. Energy and Building, 2010, 42(9): 1437–1444
https://doi.org/10.1016/j.enbuild.2010.03.013
6 E Yaşa, V Ok.. Evaluation of the effects of courtyard building shapes on solar heat gains and energy efficiency according to different climatic regions. Energy and Building, 2014, 73: 192–199 doi:10.1016/j.enbuild.2013.12.042
7 A AlAnzi, D Seo, M Krarti. Impact of building shape on thermal performance of office buildings in Kuwait. Energy Conversion and Management, 2009, 50(3): 822–828
https://doi.org/10.1016/j.enconman.2008.09.033
8 R Ourghi, A Al-Anzi, M Krarti. A simplified analysis method to predict the impact of shape on annual energy use for office buildings. Energy Conversion and Management, 2007, 48(1): 300–305
https://doi.org/10.1016/j.enconman.2006.04.011
9 W Marks. Multicriteria optimisation of shape of energy-saving buildings. Building and Environment, 1997, 32(4): 331–339
https://doi.org/10.1016/S0360-1323(96)00065-0
10 J H Kämpf, D Robinson. Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms. Energy and Building, 2010, 42(6): 807–814
https://doi.org/10.1016/j.enbuild.2009.11.019
11 G Caruso, F Fantozzi, F Leccese. Optimal theoretical building form to minimize direct solar irradiation. Solar Energy, 2013, 97: 128–137
https://doi.org/10.1016/j.solener.2013.08.010
12 J Jin, J Jeong. Thermal characteristic prediction models for a free-form building in various climate zones. Energy, 2013, 50: 468–476
https://doi.org/10.1016/j.energy.2012.11.011
13 W Wang, H Rivard, R Zmeureanu. Floor shape optimization for green building design. Advanced Engineering Informatics, 2006, 20(4): 363–378
https://doi.org/10.1016/j.aei.2006.07.001
14 Y K Yi, A M Malkawi. Optimizing building form for energy performance based on hierarchical geometry relation. Automation in Construction, 2009, 18(6): 825–833
https://doi.org/10.1016/j.autcon.2009.03.006
15 D Tuhus-Dubrow, M Krarti. Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 2010, 45(7): 1574–1581
https://doi.org/10.1016/j.buildenv.2010.01.005
16 J Jin, J Jeong. Optimization of a free-form building shape to minimize external thermal load using genetic algorithm. Energy and Building, 2014, 85: 473–482
https://doi.org/10.1016/j.enbuild.2014.09.080
17 L Zhang, L Zhang, Y Wang. Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Solar Energy, 2016, 132: 38–50
https://doi.org/10.1016/j.solener.2016.02.053
18 H Jedrzejuk, W Marks. Optimization of shape and functional structure of buildings as well as heat source utilisation example. Building and Environment, 2002, 37(12): 1249–1253
https://doi.org/10.1016/S0360-1323(01)00100-7
19 G Caruso, J H Kämpf. Building shape optimisation to reduce air-conditioning needs using constrained evolutionary algorithms. Solar Energy, 2015, 118: 186–196
https://doi.org/10.1016/j.solener.2015.04.046
20 M Adamski. Optimization of the form of a building on an oval base. Building and Environment, 2007, 42(4): 1632–1643
https://doi.org/10.1016/j.buildenv.2006.02.004
21 P A Fokaides, A Panayidou, C Hadjichristos, M C Phocas. Application of non-linear programming to optimize buildings’ solar exposure. Journal of Building Engineering, 2017, 11: 127–133
https://doi.org/10.1016/j.jobe.2017.04.007
22 L Magnier, F Haghighat. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 2010, 45(3): 739–746
https://doi.org/10.1016/j.buildenv.2009.08.016
23 J Li, S W Yin, G S Shi, L Wang. Optimization of indoor thermal comfort parameters with the adaptive network-based fuzzy inference system and particle swarm optimization algorithm. Mathematical Problems in Engineering, 2017: 3075432
https://doi.org/10.1155/2017/3075432
24 E Mushtaha, O Helmy. Impact of building forms on thermal performance and thermal comfort conditions in religious buildings in hot climates: a case study in Sharjah city. International Journal of Sustainable Energy, 2017, 36(10): 926–944
https://doi.org/10.1080/14786451.2015.1127234
25 S Gou, V M Nik, J L Scartezzini, Q Zhao, Z Li. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Building, 2018, 169: 484–506
https://doi.org/10.1016/j.enbuild.2017.09.095
26 B K Debnath, R Das. Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network. Journal of Renewable and Sustainable Energy, 2010, 2(4): 043107
https://doi.org/10.1063/1.3467510
27 R Das, D K Prasad. Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm. Swarm and Evolutionary Computation, 2015, 23: 27–39
https://doi.org/10.1016/j.swevo.2015.03.001
28 R Das, B Akay, R K Singla, K Singh. Application of artificial bee colony algorithm for inverse modelling of a solar collector. Inverse Problems in Science and Engineering, 2017, 25(6): 887–908
https://doi.org/10.1080/17415977.2016.1209748
29 R Das, K Singh, B Akay, T K Gogoi. Application of artificial bee colony algorithm for maximizing heat transfer in a perforated fin. Proceedings of the Institution of Mechanical Engineers. Part E, Journal of Process Mechanical Engineering, 2018, 232(1): 38–48
https://doi.org/10.1177/0954408916682985
30 DesignBuilder. Simulation made easy. 2017–07–05,
31 EnergyPlus. Testing and Validation. 2018–05,
32 D Coakley, E J Corry, D M Keane. Validation of simulated thermal comfort in the context of building performance evaluation & optimisation. In: Proceedings of the 13th International Conference for Enhanced Building Operations, Montreal, Quebec, Canada, 2013
33 DesignBuilder. Tests for the calculation of energy use for space heating and cooling. 2017–08–05,
34 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Beijing: China Architectural Engineering Industrial Publishing Press, 2010 (in Chinese)
35 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Warm Winter Zone. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)
36 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Severe Cold and Cold Zones. Beijing: China Architectural Engineering Industrial Publishing Press, 2010 (in Chinese)
37 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Evaluation Standard for Indoor Thermal Environment in Civil Building. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)
38 P O Fanger. Thermal Comfort. Copenhagen: Danish Technical Press, 1970
39 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Standard for Lighting Design of Buildings. Beijing: China Architectural Engineering Industrial Publishing Press, 2013 (in Chinese)
40 Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Code for Design of Hating Ventilation and Air Conditioning of Civil Buildings. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)
41 M D McKay. In: Ronen Y, ed. Sensitivity Arid Uncertainty Analysis Using a Statistical Sample of Input Values, Uncertainty Analysis. CRC Press, 1998, 145–186
42 L Zhou, F Haghighat. Optimization of ventilation system design and operation in office environment, part I: methodology. Building and Environment, 2009, 44(4): 651–656
https://doi.org/10.1016/j.buildenv.2008.05.009
43 J Conraud. A methodology for the optimization of building energy, thermal, and visual performance. Dissertation for Master’s Degree. Montreal: Concordia University, 2008
[1] Fating LI, Pengfei JIE, Zhou FANG, Zhimei WEN. Determining the optimum economic insulation thickness of double pipes buried in the soil for district heating systems[J]. Front. Energy, 2021, 15(1): 170-185.
[2] Binxuan ZHOU, Tao WANG, Tianming XU, Cheng LI, Yuan ZHAO, Jiapeng FU, Zhen ZHANG, Zhanlong SONG, Chunyuan MA. Optimization of process parameters for preparation of powdered activated coke to achieve maximum SO2 adsorption using response surface methodology[J]. Front. Energy, 2021, 15(1): 159-169.
[3] Jidong WANG, Chenghao LI, Peng LI, Yanbo CHE, Yue ZHOU, Yinqi LI. MPC-based interval number optimization for electric water heater scheduling in uncertain environments[J]. Front. Energy, 2021, 15(1): 186-200.
[4] Liang YIN, Yonglin JU. Review on the design and optimization of hydrogen liquefaction processes[J]. Front. Energy, 2020, 14(3): 530-544.
[5] Jianpeng ZHENG, Liubiao CHEN, Ping WANG, Jingjie ZHANG, Junjie WANG, Yuan ZHOU. A novel cryogenic insulation system of hollow glass microspheres and self-evaporation vapor-cooled shield for liquid hydrogen storage[J]. Front. Energy, 2020, 14(3): 570-577.
[6] Mohammad Reza NAZEMZADEGAN, Alibakhsh KASAEIAN, Somayeh TOGHYANI, Mohammad Hossein AHMADI, R. SAIDUR, Tingzhen MING. Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm[J]. Front. Energy, 2020, 14(3): 649-665.
[7] Jidong WANG, Boyu CHEN, Peng LI, Yanbo CHE. Distributionally robust optimization of home energy management system based on receding horizon optimization[J]. Front. Energy, 2020, 14(2): 254-266.
[8] Aeidapu MAHESH, Kanwarjit Singh SANDHU. A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm[J]. Front. Energy, 2020, 14(1): 139-151.
[9] Ali EL YAAKOUBI, Kamal ATTARI, Adel ASSELMAN, Abdelouahed DJEBLI. Novel power capture optimization based sensorless maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG[J]. Front. Energy, 2019, 13(4): 742-756.
[10] Junjie MA, Xiang CHEN, Zongchang QU. Structural optimal design of a swing vane compressor[J]. Front. Energy, 2019, 13(4): 764-769.
[11] B. TUDU, K. K. MANDAL, N. CHAKRABORTY. Optimal design and development of PV-wind-battery based nano-grid system: A field-on-laboratory demonstration[J]. Front. Energy, 2019, 13(2): 269-283.
[12] Chongzhe ZOU, Huayi FENG, Yanping ZHANG, Quentin FALCOZ, Cheng ZHANG, Wei GAO. Geometric optimization model for the solar cavity receiver with helical pipe at different solar radiation[J]. Front. Energy, 2019, 13(2): 284-295.
[13] Hongbo REN, Yinlong LU, Qiong WU, Xiu YANG, Aolin ZHOU. Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm[J]. Front. Energy, 2018, 12(4): 518-528.
[14] Maurizio FACCIO, Mauro GAMBERI, Marco BORTOLINI, Mojtaba NEDAEI. State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)[J]. Front. Energy, 2018, 12(4): 591-622.
[15] Shixi MA, Shengnan SUN, Hang WU, Dengji ZHOU, Huisheng ZHANG, Shilie WENG. Decoupling optimization of integrated energy system based on energy quality character[J]. Front. Energy, 2018, 12(4): 540-549.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed