Abstract
In a smart grid, load forecasting has a crucial role in the efficient and sustainable operation of the power system by reducing uncertainties and enabling a pre-coordination to avoid any potential problems. This study proposes day-ahead load forecasting based on a statistical approach using the records of the previous days’ pre-processed through a smoothing approach. A prediction exploiting the latest available observations has the advantage of recency effect. However, the direct use of a full-day profile may cause a wide variation in the predicted outcomes owing to noisy and correlated observations. To avoid an undesired correlation effect, binning is employed to smooth the profile of the previous day, thereby enhancing the inter-day correlations. For a performance evaluation, the 3.5-year record of the 15-min sampled electric power of a campus was investigated for day-ahead load forecasting based on a correlation analysis between days. The results indicate that the inter-day correlation in a smoothed profile is improved compared to that in a raw data and that the day-ahead load forecasting yields smaller prediction errors on average with less variability through the proposed binning approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Global Energy Statistical Yearbook. Enerdata (2018). https://yearbook.enerdata.net. Accessed on September 2018
Energy technology perspectives 2017: Catalysing energy technology transformations, IEA (2017)
Zhang, Y., Mao, M., Ding, M., Chang, L.: Study of energy management system for distributed generation systems. In: Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, DRPT 2008, pp. 2465–2469. IEEE, Piscataway (2008)
Siano, P.: Demand response and smart grids: a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)
O’Connell, S., Riverso, S.: Flexibility analysis for smart grid demand response (2017). Preprint arXiv:1704.01308
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., Zhao, X.: A review of data-driven approaches for prediction and classification of building energy consumption. Renew. Sustain. Energy Rev. 82, 1027–1047 (2018)
Hong, T., Gui, M., Baran, M.E., Willis, H.L.: Modeling and forecasting hourly electric load by multiple linear regression with interactions. In: 2010 IEEE Power and Energy Society General Meeting, pp. 1–8. IEEE, Piscataway (2010)
Yarbrough, I., Sun, Q., Reeves, D.C., Hackman, K., Bennett, R., Henshel, D.S.: Visualizing building energy demand for building peak energy analysis. Energy Build. 91, 10–15 (2015)
Amber, K.P., Aslam, M.W., Mahmood, A., Kousar, A., Younis, M.Y., Akbar, B., Chaudhary, G.Q., Hussain, S.K.: Energy consumption forecasting for university sector buildings. Energies 10(10), 1579 (2017)
Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017)
Taylor, J.W.: Triple seasonal methods for short-term electricity demand forecasting. Eur. J. Oper. Res. 204(1), 139–152 (2010)
Dudek, G.: Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, 139–147 (2016)
Jalil, N.A.A., Ahmad, M.H., Mohamed, N.: Electricity load demand forecasting using exponential smoothing methods. World Appl. Sci. J. 22(11), 1540–1543 (2013)
Acknowledgements
This work was supported by GIST Research Institute (GRI) grant funded by the GIST in 2018 and by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20171210200810).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Park, S., Park, K., Hwang, E. (2020). Day-Ahead Load Forecasting Based on Conditional Linear Predictions with Smoothed Daily Profile. In: José, R., Van Laerhoven, K., Rodrigues, H. (eds) 3rd EAI International Conference on IoT in Urban Space. Urb-IoT 2018. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-28925-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-28925-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28924-9
Online ISBN: 978-3-030-28925-6
eBook Packages: EngineeringEngineering (R0)