Optimizing Energy Baseline for Medium Size Office Using Hybrid EnergyPlus-Evolutionary Programming (EP)

N. Y. Dahlan, A. A. M. Aris


This paper presents an optimization approach of developing building energy baseline for medium sized office using Evolutionary Programming (EP) in comparison with direct methods. This paper applies simulation-based approach by coupling Matlab and EnergyPlus to perform energy building simulation and obtain the best energy baseline configuration with minimal error. On the other hand, direct method relies on try-and-error manually key-in methods using OpenStudio EnergyPlus simulation software. The proposed method is applied to a single story Green Energy Research Centre (GERC) office building located in UiTM Shah Alam with characteristic of partially air-conditioned buildings. The office consists of 5 different size rooms with different purposes. In this regard, 3 building parameters are taken as a decision variables including occupancies, lightings and electrical equipment. The EP objective function was set to minimize the difference between simulated and monitored energy consumption. To evaluate accuracy of building energy model, hourly criteria for Normalized Mean Biased Error (NMBE) and Coefficient of Variance Root Mean Squared Error (CV(RMSE)) endorsed by IPMVP were used. It is found that simulation-based approach has lower value of NMBE at 2.775% and CV(RMSE) at 10.949% compared to direct methods where NMBE at 79.964% while CV(RMSE) at 104.848%.


Building Simulation; Energy Baseline; EnergyPlus; Evolutionary Programming (EP); IPMVP;

Full Text:



ASHRAE, Measurement of energy and demand savings. Atlanta, GA 2002.

IPMVP Renewables Subcommitte, Concept and practices for determining energy savings in renewable technologies applications: IPMVP option D: calibrated simulation. ipmvp.org, pp. 13-17. 2003 vol. III.

(2017) EnergyPlus website. [Online]. Available: https://energyplus.net/

IBPSA-USA (2014) Building energy software tools. [Online]. Available: http://www.buildingenergysoftwaretools.com/softwarelisting

Anh-Tuan Nguyen, Sigrid Reiter and Philippe Rigo “A review on simulation-based optimization methods applied to building performance analysis”, Applied Energy., vol. 113, pp. 1043-1058, 2014.

Willy Bernal, Madhur Behl, Truong X. Nghiem, and Rahul Mangharam, “MLE+: a tool for integrated design and deployment of energy efficient building controls,” Toronto, Canada 2012.

Navid Delgarm, Behrang Sajadi and Saeed Delgarm, “Multi-objective optimization of building energy performance and indoor thermal comfort a new method using Artificial Bee Colony (ABC),” Energy and Buildings, Issue 131, pp. 42-53, 2016

Valentina Monetti, Elisabeth Davin, Enrico Fabrizio, PhilippeAndre and Marco Filippi, “Calibration of building energy simulation models based on optimization - a case study,” Energy Procedia, Issue 78, pp. 2971-2976. 2015.

Wetter, M, “Co-simulation of building energy and control systems with the building controls virtual test bed,” Journal of Building Performance Simulation, 4(3), pp. 185-203. 2011.

Haghighat, Laurent Magnier and Fariborz, “Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and artificial neural network,” Building and Environment, vol. 45, pp. 739-746, Nov. 2010.

(2017) Radiance website. [Online]. Available: https://www.radianceonline.org/

(2017) ESP-r website. [Online]. Available: http://www.esru.strath.ac.uk/Programs/ESP-r.htm

V. Siddhartha, P.V. Ramakrishnaa, T. Geethaa and Anand Sivasubramaniam, “Automatic generation of energy conservation measures in buildings using genetic algorithms,” Energy and Buildings. vol. 43, pp. 2718-2726, Nov. 2011.

Jonathan A. Wright, Heather A. Loosemore and Raziyeh Farmani, “Optimization of building thermal design and control by multi-criterion genetic algorithm,” Energy and Buildings, vol. 34, pp. 959-972. 2002.

Fabrizio Ascione, Nicola Bianco, Rosa Francesca De Masi, Gerardo Maria Mauro and Giuseppe Peter Vanoli, “Design of the building envelope- a novel multi-objective approach for the optimization of energy performance and thermal comfort,” Sustainability, vol. 7, 2015.

A. C. Koenig, “A study of mutation methods for evolutionry algorithms,” pp. 1-8, 2002.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131