Day-ahead electricity price forecasting based on hybrid regression model
Document Type
Article
Publication Date
1-1-2022
Abstract
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.
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
Divisions
fac_eng
Funders
Ministry of Higher Education under the Fundamental Research Grant Scheme [FRGS/1/2020/ICT02/UM/02/2]
Publication Title
IEEE Access
Volume
10
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA