Abstract
Electricity demand and economic growth are closely correlated. Electricity is an important means of production and subsistence and plays an important role in the national economy system. Accurate electricity demand forecasting results could provide the basis for the power grid planning and construction and therefore has important social and economic benefits. In this paper, a long-term electricity demand forecasting model that contains six kinds of Agent is proposed based on multi-agent technology. The model is validated by the electricity consumption data of 2011-2014. Then the industry-wide electricity demand forecasting results from 2015 to 2025 are obtained. Through case study, the results change affected by economic policy is studied. The results show that the electricity demand will increase under loose monetary policy.
“The Fundamental Research Funds for the Central Universities” (E14JB00160).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhao-guang, H., Yan-ping, F.: Analysis on Prospects of Economic Development and Power Demand in China. Electric Power (08), 6–9 (2000)
Jia-hai, Y., Wei, D., Zhao-guang, H.: Analysis on Cointegration and Co-movement of Electricity Consumption and Economic Growth in China. Power System Technology (09), 10–15 (2006)
Zhong-fu, T., Jin-liang, Z., Liang-qi, W., Ya-wei, D., Yi-hang, S.: A Model Integrating Econometric Approach With System Dynamics for Long-Term Load Forecasting. Power System Technology (01), 186–190 (2011)
Singh, A.K., Ibraheem, I., Khatoon, S., Muazzam, M., Chaturvedi, D.K.: Load forecasting techniques and methodologies: a review. In: 2012 2nd International Conference on Power, Control and Embedded Systems (ICPCES), Allahabad, pp. 20121–10 (2012)
Zhi-heng, Z., Shi-jie, Y.: Long term load forecasting and recommendations for china based on support vector regression. In: 2011 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), Shenzhen, pp. 2011597–602 (2011)
Jia-hai, Y., Wei, D., Zhao-guang, H.: A Critical Study of Agent Based Computational Economics and Its Application in Research of Electricity Market Theory. Power System Technology (07), 47–51 (2005)
Chong-qing, K., Jian-jian, J., Qing, X.: Theoretical Fundamental and Concepts of Electricity Market Simulation Based on Agents’ Belief Learning. Power System Technology (12), 10–15 (2005)
Tesfatsion, L.: Agent-based computational economics: Modeling economies as complex adaptive systems. Inform. Sciences 149(4), 263–269 (2003)
Zhao-guang, H.: Study on the baseline space of sustainable power development. Electric Power (04), 6–9 (2004)
Min-jie, X., Zhao-guang, H.: Simulating Impact of Macroeconomic Policy on Electricity Consumption Based on Multi-agent. Journal of Systems & Management (05), 539–548 (2011)
Jian-wei, T., Zhao-guang, H., Jun-yong, W., Xiao, X., Min-jie, X.: Dynamic Economy and Power Simulation System Based on Multi-agent Modelling. Proceedings of the CSEE (07), 85–91 (2010)
Wei, D., Zhao-guang, H., Si-zhu, W., Yu-hui, Z., Ming-tao, Y.: Dynamic Simulation of Economic Policy and Electricity Demand by Agents Response Equilibrium Model. Proceedings of the CSEE (07), 1206–1212 (2014)
Hai-gang, L., Qi-di, W.: Summary on Research of Multi-agent System. Journal of Tongji University (06), 728–732 (2003)
Woodridge, M., Jennings, N.R.: Intelligent agents theory and practice. Knowl. Eng. Rev. 10(2), 115 (1995)
Holland, J.H.: Hidden order: How adaptation builds complexity. Basic Books (1995)
Bollinger, L.A., Davis, C., Nikolic, I., Dijkema, G.P.J.: Modeling Metal Flow Systems: Agents vs. Equations. J. Ind. Ecol. 16(2), 176–190 (2012)
Shao-ping, Z., Feng, D., Cheng-zhi, W., Qin, Z.: Summary on Research of Multi-Agent System. Complex Systems and Complexity Science (04), 1–8 (2011)
Laboratory, S.N.: Aspen’s information page (2002). http://www.cs.sandia.gov
Jia-hai, Y., Zhao-guang, H.: A Multi-agent Based Negotiation Simulation System for Electricity Contract Market. Power System Technology (11), 49–53 (2005)
Zhao-guang, H.: Study on Agents Response Equilibrium Models. Energy Technology and Economics (06), 9–15 (2011)
Zhao-guang, H., Wei, D., Xiao, X., Jian-wei, T.: Derivation of China’s 2010 Input-Output Table Based on Agent Response Equilibrium (ARE) Model. Energy Technology and Economics (11), 8–14 (2011)
Council, C.E.: Annual Statistics of China Power Industry (2015). http://www.cec.org.cn/guihuayutongji/tongjxinxi/
Administration, N.E.: National Energy Administration released the total electricity consumption in 2014 (2015). http://www.nea.gov.cn/2015-01/16/c_133923477.htm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jian, Z., Zhao-guang, H., Yu-hui, Z., Wei, D. (2015). Long Term Electricity Demand Forecasting with Multi-agent-Based Model. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-20466-6_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20465-9
Online ISBN: 978-3-319-20466-6
eBook Packages: Computer ScienceComputer Science (R0)