Short Term Power Load Forecasting Based on PSVMD-CGA Model

Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize variational mode decomposition (PSVMD–CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load forecasting. To avoid manual selection of VMD parameters, the Sparrow algorithm is adopted to optimize VMD by decomposing short-term power load data into multiple subsequences, thus significantly reducing the volatility of load data. Subsequently, the CNN (Convolution Neural Network) is introduced to address the fact that the GRU (Gated Recurrent Unit) is difficult to use to extract high-dimensional power load features. Finally, the attention mechanism is selected to address the fact that when the data sequence is too long, important information cannot be weighted highly. On the basis of the original GRU model, the PSVMD–CGA model suggested in this paper has been considerably enhanced. MAE has dropped by 288.8%, MAPE has dropped by 3.46%, RMSE has dropped by 326.1 MW, and R2 has risen to 0.99. At the same time, various evaluation indicators show that the PSVMD–CGA model outperforms the SSA–VMD–CGA and GA–VMD–CGA models

Image Encryption Algorithm Based on Chaotic Mapping and Binary Bidirectional Zigzag Transform

With the continuous development of chaotic systems, they have increasingly become the core of the field of image encryption, and the good performance of chaotic systems is crucial for image encryption. Some two-dimensional chaotic maps still have drawbacks such as uneven distribution and small key space, which are prone to destruction. To this end, a new two-dimensional logic infinite folding iterative mapping is proposed, and an encryption algorithm is designed based on this. Experimental analysis shows that the chaotic map has good chaotic characteristics. Secondly, a binary bidirectional zigzag transform image scrambling algorithm is proposed. Compared with traditional zigzag transform, binary bidirectional zigzag transform has more sufficient dislocation effects and greatly reduces the correlation between adjacent pixels in the image. Finally, a bidirectional diffusion algorithm was used to destroy the image completely, making it difficult to be deciphered. Besides, the combination of the SHA-256 algorithm with the plaintext image provided better resistance to plaintext attacks. Experimental simulations illustrate that the encryption algorithm can effectively resist various attacks with high security and is not easy to crack

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04

A coupled discrete element and depth-averaged model for dynamic simulation of flow-like landslides

Flow-like landslides commonly happen in mountainous areas and may threaten people's lives, damage their properties, and create negative impact on the environment. Computer modelling has become an effective tool to support landslide risk assessment and management. Models based on discrete element method (DEM) can capture micro-mechanical behaviour of soils, simulate large deformation and have been widely used for landslide simulations. However, these models are computationally too demanding for large-scale applications. On the other hand, depth-averaged models (DAM) have been well reported for simulation of flow-like landslides over large spatial domains due to its relatively high computational efficiency. To combine the advantages of both types of modelling approaches, this paper develops a novel landslide model by coupling a DEM model with a DAM for landslide simulation, in which the DEM component is employed to better simulate the complex landslide dynamics in the source area and the DAM is adopted to predict the predominantly convective movement in the runout and deposition zone. Finally, the new coupled landslide model is validated against several test cases, including a field-scale event. Satisfactory results have been obtained, demonstrating that the coupled model is able to reproduce the dynamic process of flow-like landslides

The Early Adhesion Effects of Human Gingival Fibroblasts on Bovine Serum Albumin Loaded Hydrogenated Titanium Nanotube Surface

The soft tissue sealing at the transmucal portion of implants is vital for the long-term stability of implants. Hydrogenated titanium nanotubes (H2-TNTs) as implant surface treatments were proved to promote the adhesion of human gingival fibroblasts (HGFs) and have broad usage as drug delivery systems. Bovine serum albumin (BSA) as the most abundant albumin in body fluid was crucial for cell adhesion and was demonstrated as a normal loading protein. As the first protein arriving on the surface of the implant, albumin plays an important role in initial adhesion of soft tissue cells, it is also a common carrier, transferring and loading different endogenous and exogenous substances, ions, drugs, and other small molecules. The aim of the present work was to investigate whether BSA-loaded H2-TNTs could promote the early adhesion of HGFs; H2-TNTs were obtained by hydrogenated anodized titanium dioxide nanotubes (TNTs) in thermal treatment, and BSA was loaded in the nanotubes by vacuum drying; our results showed that the superhydrophilicity of H2-TNTs is conducive to the loading of BSA. In both hydrogenated titanium nanotubes and non-hydrogenated titanium nanotubes, a high rate of release was observed over the first hour, followed by a period of slow and sustained release; however, BSA-loading inhibits the early adhesion of human gingival fibroblasts, and H2-TNTs has the best promoting effect on cell adhesion. With the release of BSA after 4 h, the inhibitory effect of BSA on cell adhesion was weakened

Experimental Investigation of the Steam Ejector in a Single-Effect Thermal Vapor Compression Desalination System Driven by a Low-Temperature Heat Source

The paper presents an experimental investigation of a steam ejector in a single-effect thermal vapor compression (S-TVC) desalination system driven by a low-temperature (below 100 °C) heat source. To investigate the performance of the steam ejector in the S-TVC desalination system, an experimental steam ejector system was designed and built. The influences of the nozzle exit position (NXP), operating temperatures, and the area ratio of the ejector (AR) on the steam ejector performance were investigated at primary steam temperatures ranging from 40 °C to 70 °C, and at secondary steam temperatures ranging from 10 °C to 25 °C. The experimental results showed that the steam ejector can work well in the S-TVC desalination system driven by a low-temperature heat source below 100 °C. The steam ejector could achieve a higher coefficient of performance (COP) by decreasing the primary steam temperature, increasing the secondary steam temperature, and increasing the AR. The steam ejector could also be operated at a higher critical condensation temperature by increasing the primary steam temperature and secondary steam temperature, and decreasing the AR. This study will allow S-TVC desalination to compete with adsorption desalination (AD)