Electricity Spot Prices Forecasting Based on Ensemble Learning

Efficient modeling and forecasting of electricity prices are essential in today's competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecast...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.150984-150992
Main Authors: Bibi, Nadeela, Shah, Ismail, Alsubie, Abdelaziz, Ali, Sajid, Lone, Showkat Ahmad
Format: Article
Language:English
Subjects:
Algorithms
autoregressive
Autoregressive processes
Biological neural networks
Economic forecasting
Electricity prices
Electricity pricing
Electricity supply industry
Ensemble learning
Forecasting
IPEX
Machine learning
Mathematical models
Predictive models
Prices
semi-parametric
Stochastic processes
Time series
Time series analysis
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Summary:Efficient modeling and forecasting of electricity prices are essential in today's competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3126545