Forecasting Short-Term Electric Load with a Hybrid of ARIMA Model and LSTM Network
| Autor: | Rajendra G. Sutar, Nevil Pooniwala |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Electrical load
business.industry Computer science 020209 energy Deep learning 02 engineering and technology Energy consumption computer.software_genre Term (time) Nonlinear system Recurrent neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Autoregressive integrated moving average Data mining Time series business computer |
| Zdroj: | 2021 International Conference on Computer Communication and Informatics (ICCCI). |
| DOI: | 10.1109/iccci50826.2021.9402461 |
| Popis: | Smart Meters in the recent years have led to the generation of large consumer data sets which have enabled more energy forecasting algorithms to be designed. Two such algorithms are discussed in this paper with their minor deficiencies and a hybrid approach is proposed. First algorithm being Autoregressive Integrated Moving Average (ARIMA) model which turns out to be futile in determining nonlinear relationships that are involved. Secondly, Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) can not correctly model seasonal variations in energy consumption. This paper blends Seasonal ARIMA (SARIMAX) with LSTM network by integrating their benefits for a improved electric load forecast. The major contribution is the implementation of the combining algorithm to form the hybrid network. The proposed hybrid implementations provides an almost 13.08% decrease in the mean absolute error when compared with the two algorithms. The slight superior performance of the proposed method in the power load forecasting application is highlighted in the results section. |
| Databáze: | OpenAIRE |
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