Scholarly article on topic 'Minimizing Energy Consumption and Carbon Emissions of Aging Buildings'

Minimizing Energy Consumption and Carbon Emissions of Aging Buildings Academic research paper on "Economics and business"

CC BY-NC-ND
0
0
Share paper
Academic journal
Procedia Engineering
OECD Field of science
Keywords
{"Energy Consumption" / "Sustainability Measures" / "Aging Buildings" / "Carbon emissions"}

Abstract of research paper on Economics and business, author of scientific article — Moatassem Abdallah, Khaled El-Rayes, Caroline Clevenger

Abstract The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model.

Academic research paper on topic "Minimizing Energy Consumption and Carbon Emissions of Aging Buildings"

CrossMark

Available online at www.sciencedirect.com

ScienceDirect

Procedía Engineering 118 (2015) 886 - 893

Procedía Engineering

www.elsevier.com/locate/procedia

International Conference on Sustainable Design, Engineering and Construction Minimizing energy consumption and carbon emissions of aging buildings Moatassem Abdallaha*, Khaled El-Rayesb, Caroline Clevengera

aUniversity of Colordo Denver, 1200 Larimer Street, North classroom, Devver Colorado, 80217, USA University of Illinois at Urbana Champaign, 205 N. Mathews Ave., Urbana, Illinois, 61801, USA

Abstract

The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model.

© 2015The Authors.Publishedby ElsevierLtd.Thisisan openaccessarticle undertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review underresponsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015

Keywords: Energy Consumption, Sustainability Measures, Aging Buildings, Carbon emissions

* Corresponding author. Tel.: +1-303-556-5287; fax: +1-303-556-2368. E-mail address: moatassem.abdallah@ucdenver.edu

1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015 doi:10.1016/j.proeng.2015.08.527

1. Introduction

Buildings in the United States are responsible for significant percentage of energy use (41%) and carbon emissions (39%%) while aging buildings represent 70%% of existing buildings [1-3]. The energy consumption and carbon emissions of aging building can be significantly reduced by implementing sustainability measures such as energy efficient lighting, efficient HVAC systems, and renewable energy systems. Decision makers in the private and public sectors are frequently challenged to identify the optimal selection of building upgrade measures that can minimize their building energy consumption while complying with available budgets. To support decision makers and building owners in this challenging task, there is a pressing need to develop an optimization model that is capable of minimizing building energy demand and carbon emissions according to available budgets and building operational performance.

Several studies have been conducted to evaluate the performance of implementing various sustainability measures in existing buildings. These studies focused on evaluating the implementation of energy-efficient HVAC systems in buildings [4-7], energy-efficient lighting systems in buildings and streets [8-10], installation of occupancy sensors to control lighting systems in commercial buildings [11], installation of renewable energy systems to generate electricity at building sites and offset energy consumption such as wind power technology and photovoltaic systems [12-15], and implementation of solar water heating systems to reduce energy consumption of water heaters [16,17]. Other studies focused on developing optimization models and decision support systems to minimize building negative environmental impacts during operation [ 18], minimize operational cost of existing buildings [19], evaluate existing building conditions and identify optimal decisions pertaining to building renovations [20- 22], and select optimal structural and architecture design of new buildings [23,24]. Despite the significant contribution of the aforementioned research studies, there is limited or no studies that focused on developing a novel optimization model that is capable of identifying the optimal selection of building upgrade measures to simultaneously minimize energy consumption and carbon emissions of aging buildings while complying with a specified upgrade budget and preferred operational performance. Furthermore, there is limited research that considers all sustainability measures of building fixtures and equipment, and the use of renewable energy systems simultaneously to minimize carbon emissions of existing buildings.

2. Objective

The objective of this research paper is to develop an optimization model to minimize energy consumption and carbon emission of aging buildings. The optimization model is designed to provide the optimal selection of building upgrade measures to minimize simultaneously energy consumption and carbon footprint of aging buildings while complying with a specified upgrade budget and building operational performance. This optimization model will support building owners and operators in ongoing efforts to identify the optimal allocation of their budgets to minimize energy consumption and carbon footprint of their buildings. This optimization model is developed by identifying decision variables, objective function and constraints to identify the optimal replacement of building upgrade fixtures and equipment that minimizes energy consumption and carbon emissions. The energy consumption of buildings is calculated in the model using eQuest energy simulation software package. The optimization model is then implemented using Genetic Algorithms (GA) to execute the optimization computations. The model uses databases of build products to facilitate the model input data and the results output. The following sections describe the development of the optimization model and analyze an application example of an aging building to illustrate the capabilities of the model and demonstrate its use.

3. Research development

The optimization model is designed to identify the optimal replacements of building fixtures and equipment, and the installation of onsite renewable energy systems to minimize energy consumption and carbon emissions of aging buildings. This optimization model is developed in two main development steps. The first step is to identify the model decisions variables, objective function, and constraints while the second step is to implement the model using

an optimization technique to identify the optimal replacements of building fixtures and equipment and selection of renewable energy systems.

The decision variables of this optimization model are designed represent all building fixtures and equipment, including HVAC systems, interior and exterior lighting fixtures and bulbs, water heaters, refrigerators and vending machines, and hand dryers. Furthermore, the decision variables of this optimization model are designed to represent the installation of solar photovoltaic systems including solar panels, inverters, and percentage of renewable energy at the building site. All of these decision variables are designed to represent a large search space of feasible sustainability measures that have impact in the energy consumption and carbon emissions.

The objective function of this optimization model is designed to minimize building carbon emissions by summing up the carbon emissions of building fixtures and equipment except for the saved carbon emissions due to renewable electricity that is generated at the building site. To calculate the carbon emissions of aging buildings, energy simulation software packages such as eQuest are used to calculate energy consumption [25]. The carbon emissions of aging buildings are calculated based on the total energy consumption and location of the building, which accounts for the utility plants that are generating electricity to the buildings and their associated weighted emissions and transmission factors. The Environmental Protection Agency provide emission factors and average percentages of transmission loss in the major electricity grids in the United States. These emission factors can be used to estimate emissions of energy use in buildings [26]. Installing more energy efficient fixtures or installing renewable energy systems will reduce energy demand of buildings and subsequently the carbon emissions. The reduction in carbon emissions can be estimated based on the non-base emission factors, which assume that the reduction in buildings' energy demand will result in reducing the generation of energy from inefficient plants [26].

To ensure the practicality of the optimization model, a number of constraints are integrated in the model to comply with (1) available upgrade budgets, (2) existing building operational performance, (3) design constraints of photovoltaic systems, and (4) feasibility of model decision variables. The available upgrade budget constraint is designed to ensure that the upgrade cost of replacing building fixtures and equipment in addition to the installation of renewable energy systems will not exceed the specified available budget by the building owner or operator. The building performance constraints are designed to ensure that the operational performance of the building will not change after the replacement of building fixtures and equipment including lighting systems output, heating and cooling capacities, and water heating capacity. The constraints of photovoltaic systems are designed to satisfy the requirements of the photovoltaic system design. The constraints of decision variables are designed to identify the type of decision variables and their bounds. Integer decision variables are used in the model to represent products of building fixtures and equipment from databases of building products. Furthermore, integer decision variables are used to represent the components of photovoltaic systems and the percentage of generated renewable energy.

In terms of the model implementation, the developed optimization model is integrated with several databases of building products, which include data of sustainability building fixtures and equipment and components of renewable energy systems. These databases are designed to include energy and cost data, general products data, and physical characteristics of HVAC equipment and ground source heat pumps, interior and exterior lighting fixtures and bulbs, water heaters, hand dryers, refrigerators, vending machines, solar panels, and inverters. The model is designed to allow the decision maker to select the existing fixtures and equipment from the model databases. The model is also designed to provide detailed recommendations when replacing the existing building fixtures and equipment with sustainability measures from the model database by provided details for the recommended upgrade measures, which include brand name, model number, upgrade cost, expected savings and reduction in carbon emission, and supplier information. The developed optimization model and its databases are flexibility to integrate new and updated sustainability measures based on their availability in the market. The developed optimization model is implemented using Genetic Algorithms (GAs) due to its capability of modeling step changes and nonlinearity in the model objective functions and constraints and efficiently modeling the optimization problem with the least number of decision variables. Furthermore, GAs have the capability of identifying near optimal solution for this type

of problems in reasonable computational time [27- 30]. The computations of the optimization model starts by searching the model databases to identify feasible replacements for HVAC systems and water heaters. The model then creates eQuest input files based on the identified feasible replacements of HVAC systems and water heaters and next sends them to eQuest to calculate energy consumption of each feasible alternative. The calculated energy consumption of each feasible alternative is stored in the model databases where they can be used during the optimization computations. The GA computations starts by generating an initial solutions that represent the random replacements of building fixtures and equipment, selection of the components of renewable energy systems, the percentage of renewable energy that can generated at the building site. The fitness of these solutions are evaluated based on their carbon emissions. Solutions that satisfy all the constraints and provide low carbon emissions are classified as solutions with high fitness value. Other solutions are classified as infeasible solutions or solutions with low fitness values. Solutions with high fitness values are ranked based on their carbon emissions and the GA operators of selection, crossover, and mutation are applied to generate a new set of solutions. This process is repeated iteratively until no further improvements can be achieved within a predefined number of iterations. The intimal population, mutation and crossover rates are set based on the GA string and possible alternatives of the model decision variables [31,32].

4. Application example

An application example of an aging building in Illinois is analyzed to illustrate the model capabilities and demonstrate its use. This building has an area of 3,500 square feet and was built in 1980. The major contributors of the building energy consumption include space heating and cooling, interior and exterior lighting, water heating, hand dryers, vending machines. The input data of the optimization model include characteristics of building fixtures and equipment, building geometry and occupants use; electricity and gas consumption, and utility billing rates for a previous year. The optimization model is designed to allow the decision maker to select building fixtures and equipment from the model databases, as shown in Table 1. The building geometry and occupants use are used to create an energy simulation model to estimate the energy consumption of the aging building after replacing its fixtures and equipment. The energy simulation model was calibrated using the reported energy bills to ensure the practicality and accuracy of the optimization model results. The annual energy consumption of the building is reported in the energy bills at 212,381KWH and 3,139 Therms. The existing conditions of the building leads to annual carbon emissions of 159,188 metric tons of CO2 emissions based on the building energy use.

Table 1. Sample Input Data for Building Fixtures and Equipment of Public Building

Building fixture or equipment Number of fixtures or equipment working hours per day (hrs)

Fluorescent light fixture - 4'' with 4 T8 Lamps of 34 Watts 4 24

Square fluorescent fixture with 2 T12 U-shaped lamps of 34 Watts 6 24

Fluorescent light fixture - 5'' with 2 T10 Lamps of 60 Watts 4 0.33

Fluorescent light fixture - 4''with 2 T12 Lamps of 34 Watts 1 2

Snacks vending machine with average usage of 138 Watts HVAC system with a cooling capacity of 3.5 tons and heating capacity of 100Kbtu 2 1 24 24

Water heater with a capacity of 60 gallons 1 24

Hand dryer - 2300 Watts 4 per use

The developed optimization model is used to analyze and optimize the selection of building upgrade measures of the aforementioned aging building to minimize its energy consumption and carbon emissions within specified upgrade budgets. For each of the specified upgrade budget, the model was able to identify the optimal selection of building fixtures and equipment in addition to the components of renewable energy systems to minimize the building carbon emissions. The developed optimization model searched a large number of possible solutions to minimize the carbon emissions of the building with a specified budget of $10K, as shown in Figure 1. It should be noted that the feasible solutions that the optimization model searched in Figure 1 are separated in two groups due to the impact of replacing the HVAC system which results in high reduction in energy consumption and high initial cost as compared

to other building replacements such as lighting fixtures and bulbs, hand dryers, vending machines, and water heater. The model is designed to provide the results in a graphical and tabular form which include recommendations for replacing building fixtures and equipment; upgrade costs, carbon emission before and after the implementing the recommended upgrade measures, expected annual savings, and payback periods (if any). Table 2 shows an example of the model results for the building example with an upgrade budget of $10K. The results of the model identify the optimal selection of building upgrades based on an identified upgrade budget which helps decision makers and building owners in their ongoing task of maximizing the sustainability of their building while complying with their available budgets.

£ 150

« c £

• • » •

• - • • •

• • • •« » ; • « * . • •

■t^amtr, 0* «

SO S2,000 S4,000 S6,000 S8,000

Total Upgrade Cost

S10,000

S12,000

Figure 1. Feasible solutions searched during the optimization computations with upgrade budget of $30,000

Table 2. Optimization Results for an Upgrade Budget of $10,000

Building Sustainability

Measure

Upgrade Cost

Carbon Emissions (metric ton CO2 emissions) Before After

Savingsin Payback operational period costs (years)

Efficient Interior Lighting $1,389 22,806 9,722 $1,969 1.1

Exterior Lighting $0 16,930 16,930 $0 0

HVAC System $5,821 61,146 1576 $4,341 1.3

Water Heater $0 6 6 $0 0

Hand Dryers $1,734 6,024 903 $603 2.6

Vending Machines $0 17,569 17,569 $0 0

Other Devices and Loads $699 34,707 34,112 $70 N.A.

Photovoltaic System $0 0 0 $0 0

Total_$9,985_159,188_80,818_$6,983_1.5

5. Summary and conclusions

This paper presented the development of an optimization model that is capable of identifying the optimal replacement of building fixtures and equipment and the installation of renewable energy systems at the building site to simultaneously reduce energy consumption and carbon emissions. The optimization model is designed to analyze

the replacement of interior and exterior lighting systems, HVAC systems, water heaters, hand dryers, and the installation of renewable energy systems. The model is also designed with a set of constraints to perform its analysis within a specified upgrade budget while maintaining the existing operational performance of buildings.

An application example of an aging building is used to illustrate the model capabilities and demonstrate its use. The developed model was able to identify the optimal selection of building upgrade measures to minimize carbon emissions and energy consumption with various upgrade budgets. The optimization model is designed to generate the output results in a tabular and graphical form which provide detailed recommendations of the selected upgrade measures, including brand name and model number of the building fixtures and equipment, and renewable energy systems; savings in energy consumption and carbon emissions, savings in operational costs, required upgrade cost; and payback period (if any). The developed model provides new and unique capabilities that allow decision makers, building owners, and operators to identify, from a range of feasible alternative, the optimal selection of building upgrade measures that helps them in their ongoing task of maximizing the sustainability of their buildings. Future expansion of the model is needed to further study the impact of feasible upgrade measures of the building envelope such as type of insulation, windows, and doors to consider their effects on reducing energy consumption and carbon emissions for buildings that have their energy consumption dominated by HVAC systems.

References

[1] U.S. Environmental Protection Agency, Buildings and their Impact on the Environment, A Statistical Summary. (2009).

[2] U.S. Environmental Protection Agency, Transforming Existing Buildings Into High Performance Sustainable Buildings, Greening EPA. (2013). http://www.epa.gov/oaintrnt/projects/guidingprinciples.htm (accessed September 18, 2014).

[3] The Institute for Building Efficiency, Why Focus on Existing Buildings?, Building Performance Management. (2014). http://www.institutebe.com/Existing-Building-Retrofits/Why-Focus-On-Existing-Buildings.aspx (accessed September 19, 2014).

[4] R.G. Bloomquist, The Economics of Geothermal Heat Pump Systems for Commercial and Institutional Buildings, Economy of GHP Systems. (2001). http://www.soundgt.com/economyofghp.pdf(accessed November 21, 2013).

[5] A. Chiasson, Life-Cycle Cost Study of a Geothermal Heat Pump System, Geo-Heat Center, Oregon Institute of Technology, Klamath Falls, OR, USA, 2006. http://geoheat.oit.edu/toa/toa1task2.pdf.

[6] G. Phetteplace, Geothermal Heat Pumps, Journal of Energy Engineering. 133 (2007) 32-38. http://cedb.asce.org/cgi/WWWdisplay.cgi?156686 (accessed March 24, 2014).

[7] P. Das, Z. Chalabi, B. Jones, J. Milner, C. Shrubsole, M. Davies, et al., Multi-objective methods for determining optimal ventilation rates in dwellings, Building and Environment. 66 (2013) 72-81. doi:10.1016/j.buildenv.2013.03.021.

[8] RUUD LIGHTING, LED Lighting Systems in Sustainable Building Design, (2010). www.cree.com/ (accessed March 23, 2014).

[9] N. Narendran, Y. Gu, Life of LED-Based White Light Sources, Journal of Display Technology. 1 (2005) 167-171. doi:10.1109/JDT.2005.852510.

[10] E. International, I. Conference, DElight: A DA YLIGHTING AND ELECTRIC LIGHTING SIMULATION ENGINE Robert J . Hitchcock and William L . Carroll Environmental Energy Technologies Division Lawrence Berkeley National Laboratory University of California Berkeley , CA 94720 USA DELIGHT VERSION , (2003) 483-490.

[11] B. Von Neida, D. Maniccia, A. Tweed, An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems, Journal of the Illuminating Engineering Society. 30 (2001) 111 -122.

[12] H. Matthews, G. Cicas, J. Aguirre, Economic and environmental evaluation of residential fixed solar photovoltaic systems in the United States, Journal of Infrastructure Systems. (2004) 105-110. http://ascelibrary.org/doi/abs/10.1061/(ASCE)1076-0342(2004)10:3(105) (accessed January 28, 2014).

[13] P. Chapman, P. Wiczkowski, Wind-powered electrical systems-highway rest areas, weigh stations, and team section buildings, Illinois Center for Transportation, Urbana, 2009. http://128.174.2.147/publications/report files/FHWA-ICT-09-034.pdf (accessed July 14, 2014).

[14] State Energy Conservation Office, Feasibility of Photovoltaic Systems, Fact Sheet. (2006). http://files.harc.edu/Projects/CultivateGreen/Events/20070212/FeasibilityPhotovoltaicSystems.pdf.

[15] T. James, A. Goodrich, M. Woodhouse, R. Margolis, S. Ong, Building-Integrated Photovoltaics ( BIPV ) in the Residential Sector : An Analysis of Installed Rooftop System Prices, Golden, Colorado, USA, 2011. http://www.nrel.gov/docs/fy12osti/53103.pdf.

[16] K.M. Hasan, M. Saleem, M. Abid Qureshi, M. Riaz Moghal, M. Shabir Mirza, S. Amin, Effective design of solar water heater, WSEAS Transactions on Information Science and Applications. 1 (2004) 1802 - 5.

[17] M. Raisul Islam, K. Sumathy, S. Ullah Khan, Solar water heating systems and their market trends, Renewable and Sustainable Energy Reviews. 17 (2013) 1-25.

http://www.sciencedirect.com/science/article/pii/S1364032112005084 (accessed November 16, 2013).

[18] M. Abdallah, K. El-Rayes, Optimizing the selection of building upgrade measures to minimize the operational negative environmental impacts of existing buildings, Building and Environment. (2014). doi:10.1016/j.buildenv.2014.10.010.

[19] M. Abdallah, K. El-Rayes, L. Liu, Optimal Selection of Sustainability Measures to Minimize Building Operational Costs, in: Construction Research Congress (CRC), American Society of Civil Engineers, Atlanta, GA., 2014: pp. 2205-2213. http://ascelibrary.org/doi/abs/10.1061/9780784413517.224 (accessed July 18, 2014).

[20] E. Brandt, M.H. Rasmussen, Assessment of building conditions, Energy and Buildings. 34 (2002) 121 -125. doi:10.1016/S0378-7788(01)00102-5.

[21] A. Kaklauskas, E.K. Zavadskas, S. Raslanas, Multivariant design and multiple criteria analysis of building refurbishments, Energy and Buildings. 37 (2005) 361-372. doi:10.1016/j.enbuild.2004.07.005.

[22] Y.-K. Juan, P. Gao, J. Wang, A hybrid decision support system for sustainable office building renovation and energy performance improvement, Energy and Buildings. 42(2010) 290-297. doi:10.1016/j.enbuild.2009.09.006.

[23] Y. Bichiou, M. Krarti, Optimization of envelope and HVAC systems selection for residential buildings, Energy and Buildings. 43 (2011) 3373-3382.

http://www.sciencedirect.com/science/article/pii/S0378778811003811 (accessed November 16, 2013).

[24] A. Fialho, Y. Hamadi, M. Schoenauer, Optimizing Architectural and Structural Aspects of Buildings towards Higher Energy Efficiency, in: GECCO 2011 Workshop on GreenIT Evolutionary Computation, 2011. http://hal.inria.fr/inria-00591930 (accessed November 25, 2013).

[25] U.S. Department of Energy, the QUick Energy Simulation Tool, eQuest. (2013). http://www.doe2.com/equest/ (accessed March 23, 2014).

[26] TranSystems|E.H. Pechan, The emissions & generation resource integrated database for 2012 (eGRID2012) technical support document, U.S. Environmental Protection Agency, Springfield, VA, 2012. http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2012_year09_TechnicalSupportDocument.pdf (accessed March 02, 2014).

[27] H. Aytug, G.J. Koehler, Stopping Criteria for Finite Length Genetic Algorithms, INFORMS Journal on Computing. 8 (1996) 183-191. doi:10.1287/ijoc.8.2.183.

[28] D. Greenhalgh, S. Marshall, Convergence Criteria for Genetic Algorithms, SIAM Journal on Computing. 30 (2000) 269-282. doi:10.1137/S009753979732565X.

[29] P.C. Pendharkar, G.J. Koehler, A general steady state distribution based stopping criteria for finite length genetic algorithms, European Journal of Operational Research. 176 (2007) 1436-1451. doi:10.1016/j.ejor. 2005.10.050.

[30] D. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Longman Publishing Co., Inc., New York, 1989. http://is-this-book-good.info/wp-content/uploads/pdfs/Genetic Algorithms in Search Optimization and Machine Learning by David E Goldberg - Great Start To Your Journey In Genetic Algorithms.pdf (accessed June 05, 2014).

[31] D. Thierens, D.E. Goldberg, A.G. Pereira, Domino convergence, drift, and the temporal-salience structure of problems, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence. (1998) 535-540. doi:10.1109/ICEC.1998.700085.

[32] P. Reed, B. Minsker, D.E. Goldberg, Designing a competent simple genetic algorithm for search and optimization, Water Resources Research. 36 (2000) 3757-3761. doi:10.1029/2000WR900231.