Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Dima Alberg; Mark Last

doi:10.1007/978-3-319-54430-4_29

Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Dima Alberg, Mark Last

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

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.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
Editors	Satoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
Publisher	Springer Verlag
Pages	299-307
Number of pages	9
ISBN (Print)	9783319544298
DOIs	https://doi.org/10.1007/978-3-319-54430-4_29
State	Published - 1 Jan 2017
Event	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan Duration: 3 Apr 2017 → 5 Apr 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10192 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Country/Territory	Japan
City	Kanazawa
Period	3/04/17 → 5/04/17

Keywords

ARIMA
Incremental learning
Internet of things
Online Information network
Short-term forecasting
Sliding window
Smart grid

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-54430-4_29

Cite this

Alberg, D., & Last, M. (2017). Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. In S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (Eds.), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings (pp. 299-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54430-4_29

Alberg, Dima ; Last, Mark. / Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. editor / Satoshi Tojo ; Le Minh Nguyen ; Ngoc Thanh Nguyen ; Bogdan Trawinski. Springer Verlag, 2017. pp. 299-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e79abaa1eddf45c28e17c05abe941563,

title = "Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms",

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.",

keywords = "ARIMA, Incremental learning, Internet of things, Online Information network, Short-term forecasting, Sliding window, Smart grid",

author = "Dima Alberg and Mark Last",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 ; Conference date: 03-04-2017 Through 05-04-2017",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-54430-4_29",

language = "English",

isbn = "9783319544298",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "299--307",

editor = "Satoshi Tojo and Nguyen, {Le Minh} and Nguyen, {Ngoc Thanh} and Bogdan Trawinski",

booktitle = "Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings",

address = "Germany",

}

Alberg, D & Last, M 2017, Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. in S Tojo, LM Nguyen, NT Nguyen & B Trawinski (eds), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10192 LNAI, Springer Verlag, pp. 299-307, 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, Kanazawa, Japan, 3/04/17. https://doi.org/10.1007/978-3-319-54430-4_29

Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. / Alberg, Dima; Last, Mark.
Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. ed. / Satoshi Tojo; Le Minh Nguyen; Ngoc Thanh Nguyen; Bogdan Trawinski. Springer Verlag, 2017. p. 299-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

AU - Alberg, Dima

AU - Last, Mark

N1 - Funding Information: 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. Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - ARIMA

KW - Incremental learning

KW - Internet of things

KW - Online Information network

KW - Short-term forecasting

KW - Sliding window

KW - Smart grid

UR - http://www.scopus.com/inward/record.url?scp=85018513745&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-54430-4_29

DO - 10.1007/978-3-319-54430-4_29

M3 - Conference contribution

AN - SCOPUS:85018513745

SN - 9783319544298

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 299

EP - 307

BT - Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings

A2 - Tojo, Satoshi

A2 - Nguyen, Le Minh

A2 - Nguyen, Ngoc Thanh

A2 - Trawinski, Bogdan

PB - Springer Verlag

T2 - 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017

Y2 - 3 April 2017 through 5 April 2017

ER -

Alberg D, Last M. Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. In Tojo S, Nguyen LM, Nguyen NT, Trawinski B, editors, Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Springer Verlag. 2017. p. 299-307. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-54430-4_29

Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this