Login | Register

A systematic approach of integrated building control for optimization of energy and cost

Title:

A systematic approach of integrated building control for optimization of energy and cost

Aria, Hatef (2015) A systematic approach of integrated building control for optimization of energy and cost. PhD thesis, Concordia University.

[img]
Preview
Text (application/pdf)
Aria_PhD_F2015.pdf - Accepted Version
3MB

Abstract

More efficient building energy management leads to lower energy consumption and cost, higher occupancy comfort and less detrimental effects on the environment. Improving building energy management with advanced integrated building control provides a tool to coordinate and optimize control of multiple indoor parameters by considering their interconnected effects on building energy consumption and comfort.
A building integrated optimization requires an approach to calculate building energy consumption, operate in real time, optimize building control parameters, and be able to modify systems operations or schedules in response to environmental or demand response signals inputs. The integrated optimization has significant effects on reductions in energy use and energy costs, reductions in peak load, and improvement of indoor environment quality without replacing the existing equipment. Most of previous research in integrated building control just focused on optimization of specific zone or some of the possible parameters. They also applied their optimization for the current hour without considering its effect on future-hours.
The main goal of this research is to develop an advanced building operation optimization tool for integrated control of lighting, shade, ventilation and heating and cooling systems for whole buildings to reduce building energy consumption, operation cost, and peak load while satisfying occupancy comfort. Also, this optimization tool is capable of coordinating integrated control and demand response by real-time modification of time-of-use prices that are received from utilities. In addition, it applies multi-hour optimization by optimizing several hours simultaneously and considering effects of current hour control parameters on future hour energy consumption.
As a first step, integrated optimization is investigated based on a developed and validated RC-network model of a typical small office building. Nonlinear optimization is applied to the RC-network model that is created in MATLAB. The optimization results show energy savings up to 35% more than the scheduled control. In addition, multi-hour optimization saved up to 4% of energy cost compare to optimization based on the current hour.
For more accurate building energy and cost calculation, using building simulation software is essential. In this research DOE-2 is chosen as an open source building energy use analysis tool and modified based on integrated optimization requirements by adding functions to DOE-2 source code. DOE-2 requires modifications to accept the control parameters’ online and hourly bases. Accomplished modification is validated by simulating nighttime ventilation strategy. Also, the daylighting and window energy calculation algorithm is modified to operate based on shade position instead of just open or closed shade.
A building-integrated optimization tool is developed by integrating the genetic algorithm optimization method in MATLAB with building energy and cost calculation software (DOE-2). This integrated optimization tool simulates and optimizes building control parameters such as indoor temperatures, shade position, artificial light power, and outdoor air ventilation rates for an entire building. This optimization tool can be easy applied to any type of building and system when their models are available in DOE-2. Moreover, different strategies are proposed for increasing speed of optimization. First, a rule-based decision-making tool is used before integrated optimization that modifies the control parameters optimization domain. Decision-making rules are developed based on sample integrated optimization results. Second, the neural network is trained for energy consumption prediction of building based on energy consumption results from DOE-2 for random control parameters. This trained neural network is connected to a genetic algorithm and replaces DOE-2 for the energy consumption calculation. Finally, a local optimization method is used after the genetic algorithm to search around genetic algorithm results of control parameters for new control parameters with lower building energy consumption.
The integrated MATLAB and DOE-2 optimization tool is initially evaluated by investigating nighttime ventilation and shade position optimization. The results for nighttime ventilation optimization show total energy savings up to 8% and cooling energy consumption reduction up to 23%. Higher savings occurred on days with high diurnal temperature range and average outdoor temperature near 17 ˚C. The results for shade position optimization indicate that in hot days shades stay nearly closed since the effect of solar heat gain, which increases cooling energy consumption in addition to the detrimental effect of conduction heat transfer, is more effective and important than lighting energy reduction from daylighting. Also, in transient seasons when the building is in heating mode, shades mostly stay open since heat gain and illuminance transmission from windows reduce both heating and lighting energy consumption. In addition, using thick shades and a lower illuminance set-point give optimization more flexibility for energy savings.
Finally the integrated MATLAB and DOE-2 optimization tool for whole building energy optimization is applied to a typical office building in Montreal. The results show energy savings between 10% and 30%; also higher energy savings potential could be expected during transient seasons compared to very hot or very cold seasons. The results also show peak load savings up to 40%.
Keywords: building model, energy consumption, integrated control, optimization, DOE-2

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Aria, Hatef
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:14 August 2015
Thesis Supervisor(s):Akbari, Hashem
Keywords:building model, energy consumption, integrated control, optimization, DOE-2
ID Code:980299
Deposited By: HATEF ARIA
Deposited On:27 Oct 2015 19:27
Last Modified:18 Jan 2018 17:51

References:

[1] http://canmetenergy.nrcan.gc.ca
[2] The World Commission on Environment and Development, Our common future, The Oxford University Press, Oxford, 1987
[3] Annual Energy Outlook 2009, DOE/EIA-0383(2009) March 2009
[4] N. Elliott, M. Molina, D. Trombley, A Defining Framework for Intelligent Efficiency, ACEEE report, June 2012
[5] M.R. Brambley, D. Hansen, P. Haves, D.R. Holmberg, S.C. McDonald, K.W. Roth, P. Torcellini, Advanced Sensors and Controls for Building Applications: Market Assessment and Potential R&D Pathways, PNNL-15149, April 2005
[6] A. Guillemin, N. Morel, An innovative lighting controller integrated in a self-adaptive building control system, Energy and Buildings 33: 477-487, 2001
[7] G. Clark, P. Mehta, Artificial intelligence and networking in integrated building management systems, Automation in Construction 6, 481–498, 1997
[8] D. Kolokotsa, K. Niachou, V. Geros, K. Kalaitzakis,G.S. Stavrakakis, M. Santamouris, Implementation of an integrated indoor environment and energy management system, Energy and Buildings 37, 93–99, 2005
[9] B.J. Moore and D.S. Fisher, Pump differential pressure setpoint reset based on chilled water valve position, ASHRAE Transactions 109(1):373-79, 2003
[10] N. Nassif, S. Kajl, R. Sabourin, Ventilation control strategy using the supply CO2 concentration set point, HVAC&R Research 11(2): 239-262, 2005
[11] Q. Zhang, Y.W. Wong, S.C. Fok, T.Y. Bong, Neural-based air-handling unit for indoor relative humidity and temperature control, ASHRAE Transactions 111(1):63-70, 2005
[12] I. Walker, M. Sherman, B. Less, Houses are dumb without smart ventilation, ACEEE Summer Study on Energy Efficiency in Buildings, 2014
[13] V. Vakiloroaya, S.W. Su, Q.P. Ha, HVAC integrated control for energy saving and comfort enhancement, Proceedings of the 28th ISARC, Seoul, Korea, 245-250, 2011
[14] E. H. Mathews, E. van Heerden, D. C. Arndt, A tool for integrated HVAC, building, energy and control analysis, Building and Environment 34, 429-449, 1999
[15] E. H. Mathews, C.P. Botha, D.C. Arndt, A. Malan, HVAC control strategies to enhance comfort and minimise energy usage, Energy and Building 33, 853-863, 2001
[16] D. Caicedo, A. Pandharipande, Daylight integrated illumination control of LED systems based on enhanced presence sensing, Energy and Buildings 43, 944–950, 2011
[17] E. Shen, T. Hong, Simulation-based assessment of the energy savings benefits of integrated control in office buildings, Environmental Energy Technologies Division, 2009
[18] S. Mukherjee, D. birru, Closed loop integrated lighting and daylighting control for low energy buildings, ACEEE Energy Efficiency in Buildings, 2010
[19] F. Rubinstein, D. Neils, N. Colak, Daylighting, Dimming, and the electricity crisis in California, Invited paper for the 2001 IESNA National Conference, LBNL-49971, 2001
[20] P. Roche , M. Milne, Effects of combining smart shading and ventilation on thermal comfort, Solar Energy National Conference, Orland , Florida , 2005
[21] D. Gyalistras, M. Gwerder, F. Oldewurtel, Analysis of energy savings potentials for integrated room automation, Opticontrol report, 2010
[22] M. C. Dubois, Impact of shading devices on daylight quality in offices: simulations with radiance. Report TABK--01/3062. Lund University, Dept. of Construction and Architecture, Div. of Energy and Building Design, 2001
[23] S. Karjalainen, V. Lappalainen, Integrated control and user interfaces for a space, Building and Environment 46, 938-944, 2011
[24] M. Gwerder, D. Gyalistras, Potential assessment of rule-based control for integrated room automation, 10th REHVA World Congress, Sustainable Energy Use in Buildings, Clima 2010
[25] A. Kaya, C.S. Chen, S. Raina, S.J. Alexander, Optimum control policies to minimize energy use in HVAC systems, ASHRAE Transactions 88, 1982
[26] B. Sun, P. B. Luh, Q. S. Jia, Z. Jiang, F. Wang, C. Song, An integrated control of shading blinds, natural ventilation, and HVAC systems for energy saving and human comfort, 6th annual IEEE Conference on Automation Science and Engineering, Toronto, Ontario, Canada, 21-24, 2010
[27] T. Maile, M. Fischer, V. Bazjanac, Building energy performance simulation tools -a life-cycle and interoperable perspective, CIFE Working Paper WP107, 2007
[28] R. Sullivan, F. Winkelmann, DOE-2 building energy simulation program final report, Environmental Energy, Technologies Division, 1998
[29] R. Kumar, R. K. Aggarwal , J. D. Sharma, S. Pathania, Predicting energy requirement for cooling the building using artificial neural network, Journal of environmental engineering and technology vol. 2, no. 1, 2013
[30] G. Foggia, T. T. Ha Pham, G. Warkozek, F. Wurtz, Optimization energy management in buildings with neural networks, International Journal of Applied Electromagnetics and Mechanics, Volume 30, Number 3-4 / 2009
[31] P. Bacher, H. Madsen, Identifying suitable models for the heat dynamics of buildings, Energy and Buildings 43, 2011
[32] A. I. Dounis, C. C. Lefas, A. Argiriou, Knowledge-based versus classical control for solar-building designs, Applied Energy 50(4): 281-292, 1995
[33] A.I. Dounis, C. Caraiscos, Advanced control systems engineering for energy and comfort management in a building environment—A review, Renewable and Sustainable Energy Reviews 13 1246–1261, 2009
[34] J.K. Wong, H. Lia, S.W. Wang, Intelligent building research: a review, Automation in Construction 14, 143– 159, 2005
[35] N. Morel, M. Bauer, M. el-khoury, J. Krauss, Neurobat, a predictive and adaptive heating control, International Journal of Solar Energy, 1999
[36] J. hang, J. X. Tian, H. G. Lin, Application of artificial neural network in intelligent building, Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22, 2007
[37] I. Yang, M. S. Yeo, K. W. Kim, Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management 44, 2791–2809, 2003
[38] M. T. Lah, B. Zupancic, J. Peternelj, A. Krainer, Daylight illuminance control with fuzzy logic, Solar Energy 80, 307–321, 2006
[39] H. Doukas, K. D. Patlitzianas, K. Iatropoulos, J. Psarras, Intelligent building energy management system using rule sets, Building and Environment 42, 3562–3569, 2007
[40] G. Clark, P. Mehta, Artificial intelligence and networking in integrated building management systems, Automation in Construction 6, 481-498, 1997
[41] A. Mahdavi, Simulation-based control of building systems operation, Building and Environment 36, 789–796, 2001
[42] F. Oldewurtel, A. Parisio, C. N. Jones, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, M. Morari, Use of model predictive control and weather forecasts for energy efficient building climate control, Energy and Buildings, ENB-3375, 2011
[43] A. E. Mady, G. M. Provan, C. Ryan, K. N. Brown, Stochastic model predictive controller for the integration of building use and temperature regulation, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011
[44] A. Guillemin, N. Morel, Experimental results of a self-adaptive integrated control system in buildings, Solar Energy Vol. 72, No. 5, 397–403, 2002
[45] S. Kiliccote, M. Piette, D. Hansen, Advanced controls and communications for demand response and energy efficiency in commercial buildings, Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and Its Valuation for the Changing Electric Power Industry, LBNL-59337, 2006
[46] S. Kiliccote, M. Piette, Advanced control technologies and strategies linking demand response and energy efficiency, ICEBO Conference Paper, LBNL # 58179, 2005
[47] C. B. Henrikson, K. Brief, Integrating energy efficiency and demand response: employing advanced technologies to unlock operational efficiency in commercial buildings, ACEEE Summer Study on Energy Efficiency in Buildings, 2010
[48] M. Gwerder, J. Tödtli, Predictive control for integrated room automation, 8th REHVA World Congress for Building Technologies – CLIMA 2005, Lausanne, 2005
[49] F.G. Uctug, E. Yukseltan, A linear programming approach to household energy conservation: Efficient allocation of budget, Energy and Buildings 49, 200–208, 2012
[50] L. Magnier, F. Haghighat, Multiobjective optimization of building design using TRNSYS simulations, genetic algorithms, and artificial neural network, Building and Environment 45, 739–746, 2010
[51] L. G. Caldas, L. K. Norford, Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems, Journal of Solar Energy Engineering, Vol. 125 / 343, 2003
[52] W. Wang, R. Zmeureanu, H. Rivard, Applying multi-objective genetic algorithms in green building design optimization, Building and Environment 40, 1512–1525, 2005
[53] T. Nagai, Dynamic optimization technique for control of HVAC system utilizing building thermal storage, Energy Conversion and Management, 999–1014, 1999
[54] A. Ravindran, D. T. Phillips, J. J. Solberg, Operations research-principles and practice, John Wiley & Sons Inc., Canada, 1987
[55] I. Petri, O. Rana, Y. Rezgui, Cloud supported building data analytics, 14th IEEE/ACM International Symposium, 2014
[56] F. Görkem, E. Yükseltan, A linear programming approach to household energy conservation: efficient allocation of budget, Energy and Buildings 49, 2012
[57] J. E. Braun, Reducing energy costs and peak electrical demand through optimal control of building thermal storage, ASHRAE Transactions 96(2): 876-888, 1990
[58] J. Zupan, Introduction to artificial neural network (ANN) methods: what they are and how to use them, Acta Chimica Slovenica, pp. 327-352, 1994
[59] I. Maqsood, M. R. Khan, A. Abraham, An ensemble of neural networks for weather forecasting, Neural Comput & Applic, 2004
[60] G. J. Klir and B. Yuan, Fuzzy sets and information granularity, Advances in Fuzzy Systems-Applications and Theory Vol 6, Singapore, 1996
[61] M. Hellmann, Fuzzy logic introduction, Laboratoire Antennes Radar Telecom, F.R.E CNRS 2272, Equipe Radar Polarimetrie, Universit´e de Rennes, France, 2001
[62] K.R. Keeney, J.E. Braun, A simplified method for determining optimal cooling control strategies for thermal storage in building mass. HVAC&R Research 2(1): 59-78, 1996
[63] M. Palonen, A. Hasan, K. Siren, A genetic algorithm for optimization of building envelope and HVAC system parameters, Eleventh International IBPSA Conference Glasgow, Scotland, 2009
[64] R. Parameshwaran, R. Karunakaran, C. Vinu, R. Kumar, S. Iniyan, Energy conservative building air conditioning system controlled and optimized using fuzzy-genetic algorithm, Energy and Buildings 42, 745–762, 2010
[65] V. Congradac, F. Kulic, HVAC system optimization with CO2 concentration control using genetic algorithms, Energy and Buildings 41, 571–577, 2009
[66] J. Wright, H. Loosemore, R. Farmani, Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34(9):959–72, 2002
[67] J. Cavazos, J. E. B. Moss, M. F. O'Boyle, Hybrid optimizations: which optimization algorithm to use? , 15th International Conference on Compiler Construction (CC 2006), Vienna, Austria, 2006
[68] C. Poloni, Hybrid GA for multi objective aerodynamic shape optimization, in: genetic algorithms in engineering and computer science, vol. 33, John Wiley and Sons, pp. 397_415, 1995
[69] J.M. Renders, S.P. Flasse, Hybrid methods using genetic algorithms for global optimization, IEEE Transactions on systems, man and cybernetics 26, 243_258, 1996
[70] F. Espinoza, B. Minsker, D.E. Goldberg, A self adaptive hybrid genetic algorithm, Proceedings of GECCO, Morgan Kaufmann Publishers, 2001
[71] M. R. Chen, Y. Z. Lu, Q. Luo, A novel hybrid algorithm with marriage of particle-swarm optimization and extremal optimization, Optimization community e-print, 2007
[72] Y.G. Xu, G.R. Li, Z.P.Wu, A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move, Appl. Artif. Intell. 15, 601–631, 2001
[73] T. Back, U. Hammel, H. P. Schwefel, Evolutionary computation: comments on the history and current state, Evolutionary Computation, IEEE Transactions, vol.1, no.1 pp.3-17, 1997
[74] G. Dozier, J. Bowen, A. Homaifar, Solving constraint satisfaction problems using hybrid evolutionary search Evolutionary Computation, IEEE Transactions on 2 (1), 23-33, 1998
[75] M. Gen, R.W. Chen, Genetic algorithms and engineering design, John Wiley & Sons, New York, 1997
[76] A. A. Mousa, K. A. Kotb, A hybrid optimization technique coupling an evolutionary and a local search algorithm for economic emission load dispatch problem, Applied Mathematics, 2011
[77] H. Ishibuchi, K. Nozaki, N. Yamamoto, Selecting fuzzy rules by genetic algorithm for classification. Proceedings of the IEEE international conference on fuzzy systems (Vol. 2, pp. 1119–1124), San Francisco, CA, 1993
[78] D. Park, A. Kandel, G. Langholz, Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Transactions on Systems Man and Cybernetics, 24 (1), 39–47, 1994
[79] W. Konga, T. Chaia, S. Yangb, J. Ding, A hybrid evolutionary multi-objective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing, , Applied Soft Computing 13, 2960–2969, 2013
[80] P. J. Angeline, Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences, Proceedings of the 7th International Conference on Evolutionary Programming VII, Springer-Verlag London, UK 1998
[81] C. F. Juang, A hybrid of genetic algorithm and particle swarm optimization for recurrent network design, IEEE transactions on systems, man, and cybernetics part b: cybernetics, vol. 34, no. 2, 2004
[82] P. Isomursu, T. Rauma, Self-tuning fuzzy logic controller for temperature control of superheated steam, IEEE International Conference on Fuzzy Systems v 3 1994
[83] Y. Wang, D. J. Birdwell, A nonlinear PID type controller utilizing fuzzy logic, Proceedings IEEE IFAC Joint Symposium Computer Aided Control System Design, p.89-94, 1994
[84] R. Hosseini, T. Ellis, M. Mazinani, S. Qanadli, J. Dehmeshki, A genetic fuzzy approach for rule extraction for rule-based classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) ,2011
[85] V.S. Gordonand, D. Whitley, Serial and parallel genetic algorithms as function optimizers, Forrest S. (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993
[86] S. Baluja, Structure and performance of fine-grain parallelism in genetic search, Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993
[87] W.E Hart., S. Baden, R.K. Belew, S. Kohn, Analysis of the numerical effects of parallelism on a parallel genetic algorithm, Proceedings of the Workshop on Solving Combinatorial Optimization Problems in Parallel, IEEE (Ed.), 1997
[88] S.A. Harp, T. Samad, Genetic synthesis of neural network architecture. L. Davies (Ed.), Handbook of genetic algorithms, pp. 202-221, 1991
[89] L. Wang, A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied mathematics and computation journal, Vol. 170 (2), Pages 1329–1343, 2005
[89] J. Page, D. Robinson, N. Morel, J.L. Scartezzini, A generalised stochastic model for the simulation of occupant presence, Energy and Buildings. Vol. 40 (2), 83-98, 2007
[90] C. Reinhart, A model for manual and automated control of electric lighting and blinds, Solar Energy, Vol. 77, pp. 15-28, 2004
[91] D. Bourgeois, C. Reinhart, I. Macdonald, Adding advanced behavioural models in whole building energy simulation: a study on the total energy impact of manual and automated lighting control, Energy and Buildings Vol. 38, 814-823, 2006
[92] J. Page, D. Robinson, N. Morel, J. L. Scartezzini, A generalised stochastic model for the simulation of occupant presence, Energy and Buildings, Vol. 40 (2), pp. 83-98, 2007
[93] F. Yamada, K. Yonezawa, S. Sugarawa, N. Nishimura, Development of air conditioning control algorithm for building energy-saving, IEEE international conference on control applications, 1999
[94] F. Westphal, R. Lamberts, The use of simplified weather data to estimate thermal loads of non-residential buildings, Energy and Buildings,36(8), 847–54, 2004
[95] F. Lei, P. Hu, A baseline model for office building energy consumption in hot summer and cold winter region, Proceedings of international conference on management and service science, 1–4, 2009
[96] T. Olofsson, S. Andersson, Long-term energy demand predictions based on short-term measured data. Energy and Buildings, 33(2), 85 – 91, 2001
[97] N. Kubota, S. Hashimoto, F. Kojima, K. Taniguchi, GP-preprocessed fuzzy inference for the energy load prediction, Proceedings of the 2000 congress on evolutionary computation, vol. 1, 1–6, 2000
[98] N. Motegi, M. A. Piette, D. S. Watson, S. Kiliccote, P. Xu, Introduction to commercial building control strategies and techniques for demand response, LBNL-59975, 2007
[99] C. Goldman, M. Reid, R. Levy, A. Silverstein, Coordination of energy efficiency and demand response, LBNL-3044E, 2010
[100] Landolt-Bornstein, Numerical data and functional relationships in science and technology: new series III, edited by Madelung, O. and White, G. K., Thermal Transport pp. 2-217, Springer, Berlin, 2005
[101] L. T. Leng, Guided genetic algorithm, Doctoral Dissertation. University of Essex, 1999
[102] R. Malhotra, N. Singh, Y. Singh, Genetic algorithms: concepts, design for optimization of process controllers, Comput Inform Sci 4, 2011
[103] S. Grossberg, Nonlinear neural networks: Principles, mechanisms and architectures, Neural Networks 1, pp. 17–61, 1988
[104] M.D. McKay, Y. Ronen (Ed.), Sensitivity arid uncertainty analysis using a statistical sample of input values, uncertainty analysis, CRC Press, pp. 145–186, 1988
[105] H. Demuth and H.M. Beale, Neural network toolbox for use with Matlab, Mathworks, Natick, Mass, 1998
[106] C. Sergiu, Financial predictor via Neural Network, code project, article 175777, 2011
[107] D. Westphalen, S. Koszalinski, Energy consumption characteristics of commercial building HVAC systems, Volume II: Thermal Distribution, Auxiliary Equipment, and Ventilation. U.S. Department of Energy, 1999
[108] Building Energy Simulation Group, Overview of the DOE-2 building energy analysis program, Applied Science Division Lawrence Berkeley Laboratory, 1985
[109] DOE-2 Supplement (Version 2.1E) and engineering manual, Berkeley, CA, USA, Lawrence Berkeley Laboratory, 1994
[110] DOE-2, Lawrence Berkeley National Laboratory DOE-2 website 2007, Online at http://simulationresearch.lbl.gov/dirsoft/d2whatis.html.
[111] J. Huang, Private communication, yjhuang@whiteboxtechnologies.com, 2011
[112] A.D. Robins, GAWK:an effective AWK programming, 3rd ed., 2010
[113] C. K. Tang, N. Chin, Building energy efficiency technical guideline for passive design, Building Sector Energy Efficiency Program (BSEEP), 2012
[114] H. Lamy, Solar shading for low energy buildings, ES-SO, the European Solar-Shading Organization, 2012
[115] M. C. Dubois, Solar shadings and building energy use, Marie-Claude Dubois and Department of Building Science, Lund University, Lund Institute of Technology, 1997
[116] M. T. Hagan, H. B. Demuth, M. H. Beale, Neural Network Design, PWS Publishing Company, Boston, 1996
[117] DOE 2 Sample Run Book, Version 2.l.E, Berkeley, CA, USA, Lawrence Berkeley Laboratory, 1993
[118] S. Geman, E. Bienenstock, R. Doursat, Neural networks and the bias/variance dilemma, Neural Computation, 4(1), 1992
[119] V. Torczon, On the convergence of pattern search algorithms, SIAM J Optim 7(1):1–25, 1997
[120] C. Audet, J. J. Dennis, Analysis of generalized pattern searches, SIAM J Optim 13(3):889–903, 2003
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top