A hybrid algorithm that combines genetic programming (GP) and genetic algorithms (GAs) that deduce a closed-form correlation of building energy use is presented. Throughout the evolution, the terms, functions, and form of the correlation are evolved via the genetic program. Whenever the fitness of the best correlation stagnates for a specific number of GP generations, the GA optimizes the real-valued coefficients of each correlation in the population. When the GA, in turn, stagnates, correlations with optimized coefficients and powers are passed back to the GP for further search. The hybrid algorithm is applied to the problem of predicting energy use of a U-shape building. More than 800 buildings with various foot-print areas, relative compactness (RC), window-to-wall ratio (WWR), and projection factor (PF) values were simulated using the VisualDOETM energy simulation engine. The algorithm tries to minimize the difference between simulated and predicted values by maximizing the R2 value. The algorithm was able to arrive at a closed-form correlation that combines the four building parameters, accurate to within 4%. The methodology can be easily used to model any type of data behavior in any engineering or nonengineering application.
Issue Section:
Research Papers
References
1.
Bribián
, I. Z.
, Capilla
, A. V.
, and Usón
, A. A.
, 2011
, “Life Cycle Assessment of Building Materials: Comparative Analysis of Energy and Environmental Impacts and Evaluation of the Eco-Efficiency Improvement Potential
,” Build. Environ.
, 46
(5
), pp. 1133
–1140
.2.
Jylhä
, K.
, Jokisalo
, J.
, Ruosteenoja
, K.
, Pilli-Sihvola
, K.
, Kalamees
, T.
, Seitola
, T.
, Mäkelä
, H. M.
, Hyvönen
, R.
, and Laapas
, M.
, 2015
, “Energy Demand for the Heating and Cooling of Residential Houses in Finland in a Changing Climate
,” Energy Build.
, 99
, pp. 104
–116
.3.
Beghi
, A.
, Cecchinato
, L.
, Rampazzo
, M.
, and Simmin
, F.
, 2014
, “Energy Efficient Control of HVAC Systems With Ice Cold Thermal Energy Storage
,” J. Process Control
, 24
(6
), pp. 773
–781
.4.
Treado
, S.
, and Chen
, Y.
, 2013
, “Saving Building Energy Through Advanced Control Strategies
,” Energies
, 6
(12
), pp. 4769
–4785
.5.
Fabrizio
, E.
, Corrado
, V.
, and Filippi
, M.
, 2010
, “A Model to Design and Optimize Multi-Energy Systems in Buildings at the Design Concept Stage
,” Renewable Energy
, 35
(3
), pp. 644
–655
.6.
Caldas
, L. G.
, and Norford
, L. K.
, 2003
, “Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems
,” ASME J. Sol. Energy Eng.
, 125
(3
), pp. 343
–351
.7.
Ourghi
, R.
, AlAnzi
, A.
, and Krarti
, M.
, 2007
, “A Simplified Analysis Method to Predict the Impact of Shape on Annual Energy Use for Office Buildings
,” Energy Convers. Manage.
, 48
(1
), pp. 300
–305
.8.
AlAnzi
, A.
, Seo
, D.
, and Krarti
, M.
, 2009
, “Impact of Building Shape on Thermal Performance of Office Buildings in Kuwait
,” Energy Convers. Manage.
, 50
(3
), pp. 822
–828
.9.
Sofuoglu
, S.
, 2008
, “Application of Artificial Neural Network to Predict Prevalence of Building-Related Symptoms in Office Building
,” Building Environ.
, 43
(6
), pp. 1121
–1126
.10.
Magnier
, L.
, and Haghighat
, F.
, 2010
, “Multiobjective Optimization of Building Design Using TRNSYS Simulations, Genetic Algorithm, and Artificial Neural Network
,” Build. Environ.
, 45
(3
), pp. 739
–742
.11.
Moon
, J. W.
, Jung
, S. K.
, Kim
, Y.
, and Han
, S.-H.
, 2011
, “Comparative Study of Artificial Intelligence-Based Building Thermal Control Methods–Application of Fuzzy, Adaptive, Neuro-Fuzzy Inference System, and Artificial Neural Network
,” Appl. Therm. Eng.
, 31
(14–15
), pp. 2422
–2429
.12.
Bichiou
, Y.
, and Krarti
, M.
, 2011
, “Optimization of Envelope and HVAC Systems Selection for Residential Buildings
,” Energy Build.
, 43
(12
), pp. 3373
–3382
.13.
Katipamula
, S.
, Reddy
, T. A.
, and Claridge
, D. E.
, 1998
, “Multivariate Regression Modeling
,” ASME J. Sol. Energy Eng.
, 120
(3
), pp. 177
–184
.14.
Krarti
, M.
, 2003
, “An Overview of Artificial Intelligence-Based Methods for Building Energy Systems
,” ASME J. Sol. Energy Eng.
, 125
(3
), pp. 331
–342
.15.
Lee
, Y.-S.
, and Tong
, L.-I.
, 2011
, “Forecasting Energy Consumption Using a Grey Model Improved by Incorporating Genetic Programming
,” Energy Convers. Manage.
, 52
(1
), pp. 147
–152
.16.
Pérez
, J. L.
, Miguélez, M., Rabuñal
, J. R.
, and Abella
, F. M.
, 2008
, “Applying Genetic Programming to Civil Engineering in the Improvement of Models, Codes and Norms
,” 11th Ibero-American Conference on AI, Advances in Artificial Intelligence
(IBERAMIA
), Lisbon, Portugal, Oct. 14–17, pp. 452
–460
.17.
Raymer
, M. L.
, Punch
, W. F.
, Goodman
, E. D.
, and Kuhn
, L. A.
, 1996
, “Genetic Programming for Improved Data Mining: Application to the Biochemistry of Protein Interactions
,” First Annual Conference on Genetic Programming
, Stanford, CA, July 28–31, pp. 375
–380
.https://dl.acm.org/citation.cfm?id=159558918.
Castelli
, M.
, Trujillo
, L.
, Vanneschi
, L.
, and Popovič
, A.
, 2015
, “Prediction of Energy Performance of Residential Buildings: A Genetic Programming Approach
,” Energy Build.
, 102
, pp. 67
–74
.19.
Bourisli
, R. I.
, and Kaminski
, D. A.
, 2006
, “Evolutionary Optimization Techniques as Versatile Solvers for Hard-to-Converge Problems in Computational Fluid Dynamics
,” Int. J. Numer. Methods Fluids
, 52
(3
), pp. 321
–354
.20.
Liu
, F.-B.
, 2008
, “A Modified Genetic Algorithm for Solving the Inverse Heat Transfer Problem of Estimating Plan Heat Source
,” Int. J. Heat Mass Transfer
, 51
(15–16
), pp. 3745
–3752
.21.
Dodier
, R. H.
, and Henze
, G. P.
, 2004
, “Statistical Analysis of Neural Networks as Applied to Building Energy Prediction
,” ASME J. Sol. Energy Eng.
, 126
(1
), pp. 592
–600
.22.
Bourisli
, R. I.
, and AlAnzi
, A. A.
, 2011
, “Closed-Form Correlation of Buildings Energy Use With Key Design Parameters Calibrated Using a Genetic Algorithm
,” ASME J. Sol. Energy Eng.
, 133
(4
), p. 041005
.23.
Deb
, K.
, Anand
, A.
, and Joshi
, D.
, 2002
, “A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization
,” MIT Press Evol. Comput.
, 10
(4
), pp. 371
–395
.24.
Hildebrandt
, T.
, and Branke
, J.
, 2015
, “On Using Surrogates With Genetic Programming
,” MIT Press Evol. Comput.
, 23
(3
), pp. 343
–367
.25.
Renders
, J. M.
, and Bersini
, H.
, 1994
, “Hybridizing Genetic Algorithm With Hill-Climbing Methods for Global Optimization: Two Possible Ways
,” First IEEE
Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence
, Orlando, FL, June 27–29, pp. 312
–317
.26.
Zemella
, G.
, De March
, D.
, Borrotti
, M.
, and Poli
, I.
, 2011
, “Optimised Design of Energy Efficient Building Façades Via Evolutionary Neural Networks
,” Energy Build.
, 43
(12
), pp. 3297
–3302
.27.
Neto
, A. H.
, and Fiorelli
, F. A. S.
, 2008
, “Comparison Between Detailed Model Simulation and Artificial Neural Network for Forecasting Building Energy Consumption
,” Energy Build.
, 40
(12
), pp. 2169
–2176
.28.
Yu
, W.
, Li
, B.
, Jia
, H.
, Zhang
, M.
, and Wang
, D.
, 2015
, “Application of Multi-Objective Genetic Algorithm to Optimize Energy Efficiency and Thermal Comfort in Building Design
,” Energy Build.
, 88
, pp. 135
–143
.29.
Al-Anzi
, A.
, 2007
, “Architectural and Energy Related Parameters of Office Buildings in the State of Kuwait
,” Eighth International Conference on Multi-Purpose High-Rise Towers and Tall Buildings
, IFHS, Abu Dhabi, United Arab Emirates, Dec. 10–12, pp. 23–28.30.
Deb
, K.
, and Agrawal
, R. B.
, 1995
, “Simulated Binary Crossover for Continuous Search Space
,” Complex Syst.
, 9
(2
), pp. 115
–148
.http://www.complex-systems.com/abstracts/v09_i02_a02.html31.
Messa
, K.
, and Lybanon
, M.
, 1991
, “Curve Fitting Using Genetic Algorithms
,” Naval Oceanographic and Atmospheric Research Laboratory, Stennis Space Center, MS, Technical Report No. AD-A247 206
.http://www.dtic.mil/dtic/tr/fulltext/u2/a247206.pdfCopyright © 2018 by ASME
You do not currently have access to this content.
