Day-ahead electricity price forecasting based on hybrid regression model

Alkawaz, Ali Najem and Abdellatif, Abdallah and Kanesan, Jeevan and Khairuddin, Anis Salwa Mohd and Gheni, Hassan Muwafaq (2022) Day-ahead electricity price forecasting based on hybrid regression model. IEEE Access, 10. pp. 108021-108033. ISSN 2169-3536, DOI

Full text not available from this repository.


Since the deregulation of the power markets, accurate short term Electricity Price Forecasting (EPF) has become crucial in maximizing economic benefits and mitigating power market risks. Due to the challenging characteristics of electricity price, which comprise high volatility, rapid spike, and seasonality, developing robust machine learning prediction tools becomes cumbersome. This work proposes a new hybrid machine learning method for a day-ahead EPF, which involves linear regression Automatic Relevance Determination (ARD) and ensemble bagging Extra Tree Regression (ETR) models. Considering that each model of EPF has its own strengths and weaknesses, combining several models gives more accurate predictions and overcomes the limitations of an individual model. Therefore, the linear ARD model is applied because it can efficiently deal with trend and seasonality variations; on the other hand, the ensemble ETR model is employed to learn from interactions, and thus combining ARD with ETR produces robust forecasting outcomes. The effectiveness of the proposed method was validated using a data set from the Nord Pool electricity market. The proposed model is compared with other models to demonstrate its superiority using performance matrices, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiment results show that the proposed method achieves lower forecasting errors than other individual and hybrid models. Additionally, a comparative study has been performed against previous works, where forecasting measurement of the proposed method outperforms previous works' accuracy in forecasting electricity price.

Item Type: Article
Funders: Ministry of Higher Education under the Fundamental Research Grant Scheme [FRGS/1/2020/ICT02/UM/02/2]
Uncontrolled Keywords: Predictive models; Forecasting; Autoregressive processes; Long short term memory; Data models; Electricity supply industry; Machine learning; Pricing; Regression analysis; Time series analysis; Electricity price forecasting; electricity market; hybrid regression models; short-term day-ahead prediction; time series analysis
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 29 Aug 2023 06:16
Last Modified: 29 Aug 2023 06:16

Actions (login required)

View Item View Item