Effects of Sorting Schemes on the Most Cost-Effective Number of Energy Conservation Measures

Thosapon Katejanekarn, Narong Promsorn


When several energy conservation measures (ECMs) are to be implemented to earn points from a building energy rating system, it is essential to decide upon the order of their implementation. To answer the question about what sorting scheme should be employed, this study aimed to investigate the effects of different sorting schemes on the most cost-effective point and the corresponding number of ECMs to be implemented. Six sorting schemes comprising energy saving, investment, points, payback period (PB), net present value (NPV), and internal rate of return (IRR) were applied to 10 common ECMs that were to be implemented on four sample buildings. The chosen rating systems were ASHRAE’s Building EQ, and the energy topic in LEED v4, BEAM Plus v1.2, and TREES v1.1. The study’s findings showed that each sorting scheme led to literally the same cost-effective point. If the ECMs were sorted by energy saving or points, a significantly lower number of ECMs would be required. However, this needed a trade-off with high investment in ECMs from the beginning. Conversely, the other four sorting schemes required a gradual increase in investment in ECMs, as well as almost all, or all, ECMs needing to be implemented. Moreover, the more stringent rating systems, such as Building EQ and LEED, tended to have a higher investment cost in ECMs per unit area per %credit. The implementation of expensive ECMs was found to be more economic in larger buildings.


building energy rating systems; energy conservation measures (ECMs); most cost-effective point; sorting scheme

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US Energy Information Administration, 2010. International Energy Outlook 2010. Washington, DC: US Department of Energy.

DEDE, 2017. Energy Situation January–October 2017. Bangkok, Thailand: Department of Alternative Energy Development and Efficiency (DEDE), Ministry of Energy, Thailand.

EPPO, 2016. Energy Statistics of Thailand 2016. Bangkok, Thailand: Energy Policy and Planning Office (EPPO), Ministry of Energy, Thailand.

Lee W.L. and J. Burnett. 2008. Benchmarking energy use assessment of HK-BEAM, BREEAM and LEED. Building and Environment 43(11): 1882-1891.

TGBI, 2012. TREES - NC Version 1.1, Thai’s Rating of Energy and Environmental Sustainability for New Construction and Major Renovation. Bangkok, Thailand: Thai Green Building Institute (TGBI).

ASHRAE, 2015. ASHRAE Building Energy Quotient (bEQ). [On-line], Retrieved April 28, 2017 from the World Wide Web: http://www.buildingenergyquotient.org.

Chen H. and W.L. Lee. 2013. Energy assessment of office buildings in China using LEED 2.2 and BEAM Plus 1.1. Energy and Buildings 63(8): 129-137.

Chen H., Lee W.L. and Wang X., 2015. Energy assessment of office buildings in China using China building energy codes and LEED 2.2. Energy and Buildings 86(1): 514-524.

Droutsa K.G., Kontoyiannidis S., Dascalaki E.G. and Balaras C.A., 2014. Ranking cost effective energy conservation measures for heating in Hellenic residential buildings. Energy and Buildings 70(2): 318-332.

Champion B.R. and S.A. Gabriel. 2015. An improved strategic decision-making model for energy conservation measures. Energy Strategy Reviews 6(1): 92-108.

Hirunyakan N. and L. Tangwichai. 2012. Feasibility analysis for a construction of a green building. Senior Project. Silpakorn University, Nakhon Pathom, Thailand.

Samutsopakul S. and A. Lakboon. 2016. Comparison of financially optimum green building label between LEED 2009 and LEED v.4. Senior Project. Silpakorn University, Nakhon Pathom, Thailand.

Chantrasawang T. and M. Ounwised. 2016. Financially optimum green building level for an office building. Senior Project. Silpakorn University, Nakhon Pathom, Thailand.

USGBC, 2015. LEED v4 for Building Design and Construction. Washington, DC: U.S. Green Building Council.

BSL, 2012. BEAM Plus New Buildings Version 1.2 (2012.07). Hong Kong: BEAM Society Limited (BSL).

Wong N.H., Tan A.Y.K., Tan P.Y. and Wong N.C., 2009. Energy simulation of vertical greenery systems. Energy and Buildings 41(12): 1401-1408.

Chomchuen P. and P. Kesornthong. 2006. The study of energy saving in a school building by using a computer simulation program. Senior Project. Silpakorn University, Nakhon Pathom, Thailand.

Qian D.F., Li Y.F., Niu F.X. and O’Neill Z., 2019. Nationwide savings analysis of energy conservation measures in buildings. Energy Conversion and Management 188(5): 1-18.

Bank of Thailand, 2015. Interest Rate. [On-line], Retrieved May 28, 2015 from the World Wide Web: https://www.bot.or.th/thai/statistics/_layouts/application/interest_rate/in_rate.aspx.

Pan Y.Q., Yin R.X. and Huang Z.Z., 2008. Energy modeling of two office buildings with data center for green building design. Energy and Buildings 40(7): 1145-1152.