Using crafted features and polar bear optimization algorithm for short-term electric load forecast system
Short-term load forecasting (STLF) can be utilized to predict usage fluctuation in a short time period and accurate forecasting can save a big chunk of a country's economic loss. This paper introduces the crafting of various features for hourly electric load forecasting on three different datas...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000023 |
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| author | Mansi Bhatnagar Gregor Rozinaj Radoslav Vargic |
| author_facet | Mansi Bhatnagar Gregor Rozinaj Radoslav Vargic |
| author_sort | Mansi Bhatnagar |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) can be utilized to predict usage fluctuation in a short time period and accurate forecasting can save a big chunk of a country's economic loss. This paper introduces the crafting of various features for hourly electric load forecasting on three different datasets using four different models XGBoost, LightGBM, Bi-LSTM, and Random Forest. The importance of crafted features over basic features was analysed by different evaluation metrics MAE, RMSE, R-squared, and MAPE. Evaluation metrics showed that prediction accuracy increased significantly with crafted features in comparison to basic features for all four models. We also showcased the ability of the Polar Bear Optimisation (PBO) algorithm for hyperparameter tuning of the machine learning models in STLF. Optimized hyperparameters with PBO effectively decreased RMSE, MAE, and MAPE and improved the model prediction, showcasing the capability of the PBO in hyperparameter tuning for STLF. PBO was compared with commonly used optimization algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). GA was the least performing with XGBoost, LightGBM, and Random Forest. PSO and PBO were comparable with XGBoost LightGBM and Random Forest while PBO highly surpassed PSO with the Bi-LSTM model. Hence PBO was proved to be highly effective for hyperparameter tuning for implementation in short-term electric load forecasting. |
| format | Article |
| id | doaj-art-c3f04a75e0be4e87b884cc4d7f6c4c88 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-c3f04a75e0be4e87b884cc4d7f6c4c882025-01-27T04:22:23ZengElsevierEnergy and AI2666-54682025-01-0119100470Using crafted features and polar bear optimization algorithm for short-term electric load forecast systemMansi Bhatnagar0Gregor Rozinaj1Radoslav Vargic2Corresponding author.; Slovak University of Technology, Faculty of Electrical Engineering and Information Technology, Bratislava, SlovakiaSlovak University of Technology, Faculty of Electrical Engineering and Information Technology, Bratislava, SlovakiaSlovak University of Technology, Faculty of Electrical Engineering and Information Technology, Bratislava, SlovakiaShort-term load forecasting (STLF) can be utilized to predict usage fluctuation in a short time period and accurate forecasting can save a big chunk of a country's economic loss. This paper introduces the crafting of various features for hourly electric load forecasting on three different datasets using four different models XGBoost, LightGBM, Bi-LSTM, and Random Forest. The importance of crafted features over basic features was analysed by different evaluation metrics MAE, RMSE, R-squared, and MAPE. Evaluation metrics showed that prediction accuracy increased significantly with crafted features in comparison to basic features for all four models. We also showcased the ability of the Polar Bear Optimisation (PBO) algorithm for hyperparameter tuning of the machine learning models in STLF. Optimized hyperparameters with PBO effectively decreased RMSE, MAE, and MAPE and improved the model prediction, showcasing the capability of the PBO in hyperparameter tuning for STLF. PBO was compared with commonly used optimization algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). GA was the least performing with XGBoost, LightGBM, and Random Forest. PSO and PBO were comparable with XGBoost LightGBM and Random Forest while PBO highly surpassed PSO with the Bi-LSTM model. Hence PBO was proved to be highly effective for hyperparameter tuning for implementation in short-term electric load forecasting.http://www.sciencedirect.com/science/article/pii/S2666546825000023Machine learningCrafted featuresPolar bear algorithmsShort term load forecastHyperparameter tunning |
| spellingShingle | Mansi Bhatnagar Gregor Rozinaj Radoslav Vargic Using crafted features and polar bear optimization algorithm for short-term electric load forecast system Energy and AI Machine learning Crafted features Polar bear algorithms Short term load forecast Hyperparameter tunning |
| title | Using crafted features and polar bear optimization algorithm for short-term electric load forecast system |
| title_full | Using crafted features and polar bear optimization algorithm for short-term electric load forecast system |
| title_fullStr | Using crafted features and polar bear optimization algorithm for short-term electric load forecast system |
| title_full_unstemmed | Using crafted features and polar bear optimization algorithm for short-term electric load forecast system |
| title_short | Using crafted features and polar bear optimization algorithm for short-term electric load forecast system |
| title_sort | using crafted features and polar bear optimization algorithm for short term electric load forecast system |
| topic | Machine learning Crafted features Polar bear algorithms Short term load forecast Hyperparameter tunning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000023 |
| work_keys_str_mv | AT mansibhatnagar usingcraftedfeaturesandpolarbearoptimizationalgorithmforshorttermelectricloadforecastsystem AT gregorrozinaj usingcraftedfeaturesandpolarbearoptimizationalgorithmforshorttermelectricloadforecastsystem AT radoslavvargic usingcraftedfeaturesandpolarbearoptimizationalgorithmforshorttermelectricloadforecastsystem |