BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R2 of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed

Synthesis and Densification of Nanocrystalline Al2O3 and AlN Ceramics with Improved Optical Transparency for Thermally Robust Photonic Applications

Optical ceramics have attracted increasing interest because of their superior mechanical toughness, better homogeneity, and potential for scaling and microstructural design compared to state-of-the-art single crystals. Optical transparency in ceramics were traditionally only achievable using optically isotropic materials because of the birefringent scattering loss in optically anisotropic materials. However, commonly used optically isotropic materials have limited thermal conductivities that fundamentally restrain heat extraction in high power applications. Excessive temperature gradient caused by low thermal conductivity affects the light transmitting properties of these materials and can eventually lead to thermomechanical failure. One solution for this challenge is to use optically anisotropic materials such as aluminum oxide (alumina, Al2O3) and aluminum nitride (AlN) that have better thermal conductivity and superior mechanical toughness. By reducing the grain size of these optically anisotropic materials, scattering loss caused by birefringence can be minimized, thus recover the material transparency.Here, transparent nanocrystalline thulium doped Al2O3 ceramic is produced and characterized, with the highest in-line transparency among rare earth doped Al2O3 ceramics reported. Nanocrystalline AlN powder is synthesized and densified using current-activated, pressure-assisted densification (CAPAD). The resulting AlN nanocrystalline ceramic is the finest-grained fully dense AlN ceramic reported and showed significant transparency improvement compared to transparent AlN ceramics reported. Finally, thulium dopant incorporation into lab synthesized nanocrystalline AlN is studied. These results show the potential of nanocrystalline microstructure in enabling the use of optically anisotropic materials in transparent ceramic applications. The use of nanocrystalline Al2O3 and AlN ceramics may enable thermally and mechanically more robust light transmitting/emitting devices under challenging conditions. The significant improvement in transparency of nanocrystalline AlN ceramic by reducing its grain size to sub-micrometer range also broadens the material selection range for optical ceramics

Methane emissions from newly created marshes in the drawdown area of the Three Gorges Reservoir

The study aimed to understand the methane (CH4) emission and its controlling factors in the Three Gorges Reservoir Region and to explore its implication for large dams. We measured CH4 emissions from four vegetation stands in newly created marshes in the drawdown area of the Three Gorges Reservoir, China, in the summer of 2008. The results showed highly spatial variations of methane emissions among the four stands, with the smallest emission (0.25 +/- 0.65 mg CH4 m(-2) h(-1)) in the Juncus amuricus stand, and the greatest (14.9 +/- 10.9 mg CH4 m(-2) h(-1)) in the Scirpus triqueter stand. We found that the spatial variations of CH4 emissions are caused by difference in standing water depth and dissolved organic carbon (DOC). Results also showed a special seasonal variation of CH4 emissions in this area, i.e., maximal emissions in early July followed by a low and steady value before the winter flooding. The seasonality of CH4 emissions was found closely related to temperature and standing water depth. Because of the large area of the drawdown zones for global dam reservoirs and a large CH4 emission rate, such newly created marshes should not be neglected when estimating CH4 emissions from reservoirs

Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems

In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation

Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems

In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation