Study of potential impact of wind energy on electricity price using regression techniques
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 fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | http://eprints.utm.my/107350/1/HasmatMalik2023_StudyofPotentialImpactofWindEnergy.pdf |
| _version_ | 1811132521155395584 |
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| author | Kumar, Neeraj Tripathi, Madan Mohan Gupta, Saket Alotaibi, Majed A. Malik, Hasmat Afthanorhan, Asyraf |
| author_facet | Kumar, Neeraj Tripathi, Madan Mohan Gupta, Saket Alotaibi, Majed A. Malik, Hasmat Afthanorhan, Asyraf |
| author_sort | Kumar, Neeraj |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-09-24T00:05:36Z |
| format | Article |
| id | utm.eprints-107350 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-24T00:05:36Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1073502024-09-03T06:21:29Z http://eprints.utm.my/107350/ Study of potential impact of wind energy on electricity price using regression techniques Kumar, Neeraj Tripathi, Madan Mohan Gupta, Saket Alotaibi, Majed A. Malik, Hasmat Afthanorhan, Asyraf TK Electrical engineering. Electronics Nuclear engineering 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. MDPI 2023-10 Article PeerReviewed application/pdf en http://eprints.utm.my/107350/1/HasmatMalik2023_StudyofPotentialImpactofWindEnergy.pdf Kumar, Neeraj and Tripathi, Madan Mohan and Gupta, Saket and Alotaibi, Majed A. and Malik, Hasmat and Afthanorhan, Asyraf (2023) Study of potential impact of wind energy on electricity price using regression techniques. Sustainability (Switzerland), 15 (19). pp. 1-17. ISSN 2071-1050 http://dx.doi.org/10.3390/su151914448 DOI:10.3390/su151914448 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Kumar, Neeraj Tripathi, Madan Mohan Gupta, Saket Alotaibi, Majed A. Malik, Hasmat Afthanorhan, Asyraf Study of potential impact of wind energy on electricity price using regression techniques |
| title | Study of potential impact of wind energy on electricity price using regression techniques |
| title_full | Study of potential impact of wind energy on electricity price using regression techniques |
| title_fullStr | Study of potential impact of wind energy on electricity price using regression techniques |
| title_full_unstemmed | Study of potential impact of wind energy on electricity price using regression techniques |
| title_short | Study of potential impact of wind energy on electricity price using regression techniques |
| title_sort | study of potential impact of wind energy on electricity price using regression techniques |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/107350/1/HasmatMalik2023_StudyofPotentialImpactofWindEnergy.pdf |
| work_keys_str_mv | AT kumarneeraj studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques AT tripathimadanmohan studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques AT guptasaket studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques AT alotaibimajeda studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques AT malikhasmat studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques AT afthanorhanasyraf studyofpotentialimpactofwindenergyonelectricitypriceusingregressiontechniques |