Simulation Research on the Effect of Energy Saving Policy in Office Building based on Dynamic Game
Aiming at the difficulty of energy management in office building and the choice of energy saving policy, this paper used the Dynamic Game Theory to establish the game model between the manager and the user, which is focusing on the effect of energy saving policy in the micro level. Based on the actual situation of the certain office building, this paper makes use of the developed multi-agent simulation model to analyze the effect after the implementation of energy-saving policy. It provides a theoretical tool which has the practical value for the scientific decision-making of the energy-saving policy for the office building manager. The simulation results show that the user's willingness to cooperate with energy-saving policy is a crucial factor affecting the implementation of energy-saving policy and the reduction of energy consumption.
Colmenar-Santos, A. et al. (2013). Solutions to reduce energy consumption in the management of large buildings. Energy and Buildings, 56, 66-77.
Masoso, O. T. et al. (2010). The dark side of occupants’behavior on building energy use. Energy & Buildings, 42(2), 173-177.
Alfonso Pablo Ramallo-González. (2013). Modelling, simulation and optimisation methods for low-energy buildings. Available at: https://ore.exeter.ac.uk/repository/bitstream/handle/10871/14005/Ramallo-GonzalezAP.pdf;sequence=3.
Shimoda, Y. et al. (2007). Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model. Energy, 32(9), 1617-1633.
Murphy, L. et al. (2012). A qualitative evaluation of policy instruments used to improve energy performance of existing private dwellings in the netherlands. Energy Policy, 45(none), 459-468.
Tiwari, P. (2001). Energy efficiency and building construction in India. Building & Environment, 36(10), 1127-1135.
Rolfsman, B. (2002). Co2 emission consequences of energy measures in buildings. Building & Environment, 37(12), 1421-1430.
De Wilde, P. & Tian, W. (2012). Management of thermal performance risks in buildings subject to climate change. Building and Environment, 55, 167-177.
Wang S.F. (2010). Game research on the guarantee mechanism of building energy conservation policy. Science and Technology Management Research, 30(20), 26-28.
Robinson S. (2014). Simulation: The practice of model development and use. (2nd ed.). UK: Palgrave Macmillan.
Huo L. et al. (2017). Rule and camera interest rate policy selection under economic new normal based on multi-agent simulation. System Engineering Theory and Practice, 37(09), 2289-2296
Feng T.Y. et al. (2010). Revenue and expenditure forecast and policy simulation of endowment insurance funds for public service enterprises. Chinese Soft Science, (11), 73-87.
Liu W.B. et al. (2017). Research on the impact of enterprise annuity payment level on labor force - Research method based on dynamic game model. Social Security Research, 3, 12-17.
Copyright (c) 2018 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.