@inproceedings{00f9d59d1a874499b4603f7792ae582d,
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 = "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 = "https://doi.org/10.1007/978-3-319-54430-4_29",
language = "American 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",
}