With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate l...
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With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data support for load management in diverse stations, this study proposes a load sequence noise reduction method. Initially, wavelet noise reduction is performed on the multiple types of load sequences collected by the power system. Subsequently, the northerngoshawkoptimization is employed to optimize the parameters of variational mode decomposition, ensuring the selection of the most suitable modal decomposition parameters for different load sequences. Next, the SSA-KELM model is employed to independently predict each sub-modal component. The predicted values for each sub-modal component are then aggregated to yield short-term load prediction results. The proposed load forecasting method has been verified using actual data collected from various types of power terminals. A comparison with popular load forecasting methods demonstrates the proposed method's higher prediction accuracy and versatility. The average prediction results of load data in industrial stations can reach RMSE = 0.0098, MAE = 0.0078, MAPE = 1.3897%, and R2 = 0.9949. This method can be effectively applied to short-term load forecasting in multiple types of power stations, providing a reliable basis for accurate demand-side power load management and decision-making.
To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, ...
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To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, RF is utilized to assess the importance of input features, selecting key factors that significantly influence dam displacement. Then, CNN is employed to perform deep feature extraction on the input data, mining effective information. Subsequently, the Informer model integrated with a residual attention mechanism establishes the mapping relationship between the extracted features and dam displacement, enhancing the focus on critical features. Finally, the northerngoshawkoptimization (NGO) algorithm is adopted to optimize the model's hyperparameters. Experimental results on actual engineering data demonstrate that the proposed model exhibits superior prediction accuracy and stability compared to other typical models, offering higher precision and reliability.
In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a ...
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In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, northerngoshawkoptimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.
The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northerngoshawk opti...
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The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGOXGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R 2 ), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920 - 0.998, 0.902 - 0.998, 0.078 - 0.623 mm d -1 , 0.058 - 0.430 mm d -1 , and - 0.254 - 0.062 mm d -1 , respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurat
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