Abstract
Multi-step forecasting is very challenging and there are a lack of studies available that consist of machine learning algorithms and methodologies for multi-step forecasting. It has also been found that lack of collaborations between these different fields is creating a barrier to further developments. In this paper, multi-step time series forecasting are performed on three nonlinear electric load datasets extracted from Open-Power-System-Data.org using two machine learning models. Multi-step forecasting performance of Auto-Regressive Integrated Moving Average (ARIMA) and Long-Short-Term-Memory (LSTM) based Recurrent Neural Networks (RNN) models are compared. Comparative analysis of forecasting performance of the two models reveals that the LSTM model has superior performance in comparison to the ARIMA model for multi-step electric load forecasting.
Original language | English |
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Title of host publication | 17th International Conference on Artificial Intelligence and Soft Computing |
Subtitle of host publication | ICAISC 2018 |
Editors | Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek Zurada |
Publisher | Springer Nature |
Pages | 148-159 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-319-91253-0 |
ISBN (Print) | 978-3-319-91252-3 |
DOIs | |
Publication status | Published - Jun 2018 |
Event | International Conference on Artificial Intelligence and Soft Computing - Mercure Zakopane Kasprowy Hotel, Zakopane, Poland Duration: 3 Jun 2018 → 7 Jun 2018 Conference number: 17th http://icaisc.eu/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10841 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Artificial Intelligence and Soft Computing |
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Abbreviated title | ICAISC 2018 |
Country/Territory | Poland |
City | Zakopane |
Period | 3/06/18 → 7/06/18 |
Internet address |
Keywords
- ARIMA
- LSTM
- Multi-step forecasting
- Time Series Analysis