Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

Neeraj Kumar, Madan Mohan Tripathi, Saket Gupta, Majed A. Alotaibi (), Hasmat Malik () and Asyraf Afthanorhan
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Neeraj Kumar: Electrical and Electronics Engineering Department, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Madan Mohan Tripathi: Electrical Engineering Department, Delhi Technological University, New Delhi 110042, India
Saket Gupta: Instrumentation and Control Engineering Department, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Majed A. Alotaibi: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Hasmat Malik: Department of Electrical Power Engineering, Faculty of Electrical Engineering, University Technology Malaysia (UTM), Johor Bahru 81310, Malaysia
Asyraf Afthanorhan: Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Malaysia

Sustainability, 2023, vol. 15, issue 19, 1-17

Abstract: This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

Keywords: price forecasting; renewable energy; grid integration; machine learning; decision tree; random forest; linear regression; LASSO; MAE; MAPE (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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