Investigation of Price-Feature Selection Algorithms for the Day-Ahead Electricity Markets
| Autor: | Prakash Ranganathan, Arun Sukumaran Nair, Radhakrishnan Angamuthu Chinnathambi, Mitch Campion |
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| Rok vydání: | 2018 |
| Předmět: |
Set (abstract data type)
Spot contract Multivariate adaptive regression splines 020209 energy Statistics Linear regression Feature extraction 0202 electrical engineering electronic engineering information engineering Feature selection 02 engineering and technology Autoregressive integrated moving average Random forest Mathematics |
| Zdroj: | 2018 IEEE Electrical Power and Energy Conference (EPEC). |
| Popis: | This paper investigates three types of feature selection techniques such as relative importance using Linear Regression (LR), Multivariate Adaptive Regression Splines (MARS), and Random forest (RF) to reduce the forecasts error for the hourly spot price of the Iberian electricity markets. Two pricing datasets of durations three and six months were used to validate the performance of the model. Three different set of features (17, 4, 2) for three and six months duration were used in this study. These selected features were applied to the two-stage hybrid model such as ARIMA-GLM, ARIMA-SVM, and ARIMA- RF. Finally, three variables (or features) that are commonly matched were selected and tested. Considerable reduction in Mean Absolute Percentage Errors (MAPE) values were observed for both three and six-month datasets. |
| Databáze: | OpenAIRE |
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