Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market
This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an alwa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/21/9766 |
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| author | Nikolaos Koutantos Maria Fotopoulou Dimitrios Rakopoulos |
| author_facet | Nikolaos Koutantos Maria Fotopoulou Dimitrios Rakopoulos |
| author_sort | Nikolaos Koutantos |
| collection | DOAJ |
| description | This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods. |
| format | Article |
| id | doaj-art-d582dc94e0c44b8d8b979bdfbf4711dd |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d582dc94e0c44b8d8b979bdfbf4711dd2025-08-20T02:49:49ZengMDPI AGApplied Sciences2076-34172024-10-011421976610.3390/app14219766Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy MarketNikolaos Koutantos0Maria Fotopoulou1Dimitrios Rakopoulos2Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Egialias 52, 15125 Athens, GreeceChemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Egialias 52, 15125 Athens, GreeceChemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Egialias 52, 15125 Athens, GreeceThis study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods.https://www.mdpi.com/2076-3417/14/21/9766ensemble-based modelautomated machine learninghyperparameter optimizationload forecasting |
| spellingShingle | Nikolaos Koutantos Maria Fotopoulou Dimitrios Rakopoulos Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market Applied Sciences ensemble-based model automated machine learning hyperparameter optimization load forecasting |
| title | Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market |
| title_full | Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market |
| title_fullStr | Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market |
| title_full_unstemmed | Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market |
| title_short | Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market |
| title_sort | automated machine learning for optimized load forecasting and economic impact in the greek wholesale energy market |
| topic | ensemble-based model automated machine learning hyperparameter optimization load forecasting |
| url | https://www.mdpi.com/2076-3417/14/21/9766 |
| work_keys_str_mv | AT nikolaoskoutantos automatedmachinelearningforoptimizedloadforecastingandeconomicimpactinthegreekwholesaleenergymarket AT mariafotopoulou automatedmachinelearningforoptimizedloadforecastingandeconomicimpactinthegreekwholesaleenergymarket AT dimitriosrakopoulos automatedmachinelearningforoptimizedloadforecastingandeconomicimpactinthegreekwholesaleenergymarket |