Abstract
Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non - seasonal and two seasonal sliding window-based ARIMA (Auto Regressive Integrated Moving Average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load. The algorithms integrate non - seasonal and seasonal ARIMA models with the OLIN (Online Information Network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.
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
Notes
- 1.
ARIMA models: (000, 001, 100, 101, 010, 011, 111, 221, 222).
- 2.
SARIMA models: (000, 001, 100, 101, 010, 011, 111, 221, 222) (0, 1, 1).
References
Depuru, S.S., Wang, L., Devabhaktuni, V.: Smart meters for power grid: challenges, issues, advantages and status. Renew. Sustain. Energy Rev. 15(6), 2736–2742 (2011)
Gama, J.: Knowledge Discovery from Data Streams. CRC Press, Boca Raton (2010)
Gerwig, C.: Short term load forecasting for residential buildings. In: Hu, J., Leung, V.C.M., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds.) SmartGIFT 2016. LNICSSITE, vol. 175, pp. 69–78. Springer, Heidelberg (2017). doi:10.1007/978-3-319-47729-9_8
Last, M.: Online classification of nonstationary data streams. Intell. Data Anal. 6(2), 129–147 (2002)
Penya, Y., Borges, C., Fernandez, I.: Short-term load forecasting in non-residential buildings. In: AFRICON 2011, vol. 9, no. 3, pp. 1–6. Livingstone (2011)
Acknowledgments
This work was partially supported by the Israel Smart Grid (ISG) Consortium under the MAGNET Program, in the office of the Chief Scientist of the Ministry of Economics in Israel.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Alberg, D., Last, M. (2017). Short-Term Load Forecasting in Smart Meters with Sliding Window-Based ARIMA Algorithms. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-54430-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54429-8
Online ISBN: 978-3-319-54430-4
eBook Packages: Computer ScienceComputer Science (R0)