The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity ex...The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.展开更多
This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impac...This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecast- ing accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.展开更多
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
By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for...By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.展开更多
文摘The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.
文摘This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecast- ing accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.
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.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.
