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 va...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.展开更多
基金supported by the project FlexEnergy under the call 09I02-03-V2-Transformation and innovation consortia.
文摘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.
