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Commercial building load characteristics modeling considering equipment innate laws and various staff behaviors under demand response mechanism

  • Xiaoou Liu ORCID logo EMAIL logo
Published/Copyright: September 16, 2021

Abstract

At present, the traditional load model relies on a large number of historical data, ignoring the relationship between load demand and user behavior and the temporal and spatial distribution of load. To solve this problem, this paper proposes a modeling method of energy consumption characteristics of commercial office building considering the physical characteristics of energy consuming equipment and staff behavior factors. Firstly, the load of commercial office building is classified based on user behavior. Secondly, according to the proposed load classification method, a time distribution model associated with user behavior is established for each type of load energy consumption equipment, and the total load model of commercial office building is obtained. On this basis, the proposed model is expanded in both time and space, so that the total load model of commercial office building can be used to analyze energy consumption in different time scales and different regional areas. Through the combination of non-intrusive load decomposition and Markov chain, the energy consumption behavior of users is analyzed and simulated, and a refined load forecasting method of commercial office building considering equipment and user behavior under demand response mechanism is proposed. Finally, the example analysis shows that the proposed method can no longer depend on huge amounts of similar data for driving, can effectively reduce the impact of the original data on the load feature extraction, and has the ability to achieve load forecasting independently.


Corresponding author: Xiaoou Liu, Tianjin Electric Power Design Institute CO. LTD., Room 506, No. 437, Beijing-Tianjin Highway, Beichen District, Tianjin, 300400, China, Phone: +86 18611550162, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Zeng, M, Yang, Y, Liu, D, Zeng, B, Ouyang, S, Lin, H, et al.. “Generation-grid-load–storage” coordinative optimal operation mode of energy internet and key technologies. Power Syst Technol 2016;40:114–24.Search in Google Scholar

2. Sun, J. Investigation and analysis on energy consumption of typi–cal urban residence in China. Shanghai: Tongji University; 2009.Search in Google Scholar

3. Fan, G-F, Shan, Q, Wang, H, Hong, W-C, Hong-Juan, L. Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies 2013;6:1887–901. https://doi.org/10.3390/en6041887.Search in Google Scholar

4. Chen, YH, Hong, W-C, Shen, W, Huang, NN. Electric load forecasting based on a least squares support vector machine with fuzzy time series and global harmony search algorithm. Energies 2016;9:70. https://doi.org/10.3390/en9020070.Search in Google Scholar

5. Zhu, J, Dong, H, Li, S, Chen, Z, Luo, T. Review of data-driven load forecasting for integrated energy system. Proceedings of the CSEE 2021;1–20. https://doi.org/10.13334/j.0258-8013.pcsee.202337.Search in Google Scholar

6. Yukseltan, E, Yucekaya, A, Bilge, AH. Forecasting electricity demand for Turkey: modeling periodic variations and demand segre– gation. Appl Energy 2017;193:287–96. https://doi.org/10.1016/j.apenergy.2017.02.054.Search in Google Scholar

7. Amjady, N, Keynia, F. Short-term load forecasting of power systems by combination of vavelet transform and neuro systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 2009;34:46–57. https://doi.org/10.1016/j.energy.2008.09.020.Search in Google Scholar

8. He, Y, Liu, R, Han, A. Short⁃term power load probability density forecasting method based on real time price and support vector quantile regression. Proceedings of the CSEE 2017;37:768–75.Search in Google Scholar

9. Collotta, M, Pau, G. An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE. IEEE Trans Green Commun Netw 2017;1:112–20. https://doi.org/10.1109/tgcn.2017.2671407.Search in Google Scholar

10. Pan, Y, Cong, YU, Long, W, Chen, W. Review of district building load and energy consumption prediction. J HV&AC 2015;45:33–40.Search in Google Scholar

11. Subbiah, R, Pal, A, Nordberg, EK, Marathe, A, Marathe, MV. Energy demand model for residential sector: a first principles approach. IEEE Trans Sustain Energy 2017;8:1215–24. https://doi.org/10.1109/tste.2017.2669990.Search in Google Scholar

12. Widén, J, Nilsson, AM, Wäckelgård, E. A combined Markov–chain and bottom–up approach to modelling of domestic lighting demand. Energy Build 2009;41:1001–12. https://doi.org/10.1016/j.enbuild.2009.05.002.Search in Google Scholar

13. Chen, F, Xu, J, Wang, C, Song, Z, Liu, M. Research on building cooling and heating load prediction model on user’s side in energy internet system. Proceedings of the CSEE 2015;35:3678–84.Search in Google Scholar

14. Zhang, H, Wen, F, Zhang, C, Meng, J, Lin, G, Dang, S. Operation opti–mization model of home energy hubs considering comfort level of customers. Autom Electr Power Syst 2016;40:32–9.Search in Google Scholar

15. Widén, J, Lundh, M, Vassileva, I, Dahlquist, E, Ellegard, K, Waeckelgard, E. Constructing load pro- files for household electricity and hot water from time–use datamodelling approach and validation. Energy Build 2009;41:753–68. https://doi.org/10.1016/j.enbuild.2009.02.013.Search in Google Scholar

16. Liu, X. Research on flexibility evaluation method of distribution system based on renewable energy and electric vehicles. IEEE Access 2020;8:109249–65. https://doi.org/10.1109/access.2020.3000685.Search in Google Scholar

17. Liu, M, Guo, J, Gao, L, Zhang, D, Mao, B. Analysis and modeling of private car usage through travel behavior. J Jilin Univ (Eng Technol Ed) 2009;39:25–30.Search in Google Scholar

18. Sovacool, BK, Hirsh, RF. Beyond batteries: an examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition. Energy Pol 2009;37:1095–103. https://doi.org/10.1016/j.enpol.2008.10.005.Search in Google Scholar

19. Wang, C, Qian, X. A fast equivalent testing method for evaluating cycle life of EV batteries. Battery 2002;01:27–8.Search in Google Scholar

20. Zhang, Z. Study on load shedding strategy for power system under emergency situations. Beijing: North China Electric Power University; 2014.Search in Google Scholar

21. Katipamula, NL. Evaluation of residential HVAC control strategies for demand response programs. Build Eng 2006;1:1–12.Search in Google Scholar

22. Muratori, M, Roberts, MC, Sioshansi, R, Marano, V, Rizzoni, G. A highly resolved modeling technique to simulate residential power demand. Appl Energy 2013;107:465–73. https://doi.org/10.1016/j.apenergy.2013.02.057.Search in Google Scholar

23. Zhu, Z, Wei, Z, Yin, B, Zhang, S, Wang, X. A novel approach for event detection in non–intrusive load monitoring. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China. IEEE; 2017:1–5 pp.10.1109/EI2.2017.8245715Search in Google Scholar

24. Li, M-W, Wang, Y-T, Geng, J, Hong, W-C. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dynam 2021;103:1167–93. https://doi.org/10.1007/s11071-020-06111-6.Search in Google Scholar

Received: 2021-03-17
Accepted: 2021-09-07
Published Online: 2021-09-16

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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