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To enhance the level of refined load decomposition of the power system and make full use of seasonal change information to improve the prediction performance further, this paper proposes a seasonal short-term load combination prediction model of the power system based on modal decomposition and feature fusion multi-algorithm hybrid neural network model, i.e., the characteristics of load components are analysed for different seasons, and the corresponding models are established respectively. Firstly, The ICEEMDAN (Improved complete ensemble EMD) method decomposes the four-season load, and a new sequence is obtained based on the RCMFE (refined composite multiscale fuzzy entropy) reconstruction of each decomposition component. Secondly, the correlation between different decomposition components and different features is measured using the mRMR (Max-Relevance and Min-Redundancy) method to filter out the subset of features with high correlation and low redundancy. Finally, the BiLSTM (Bi-directional Long Short-Term Memory) model based on the Bayesian optimisation algorithm is used to predict different components of different seasonal loads separately, and the predicted values are accumulated to obtain the final results. According to the experimental findings, the proposed method can successfully balance the issues of prediction accuracy and prediction time while having a higher level of prediction accuracy than the current prediction methods. It is confirmed that the proposed method can effectively address the load power variation brought on by seasonal differences in different regions.

In order to explore the influence of textured sawteeth on saw blades cutting and wear performance, linear micro textures perpendicular to the chip flow direction were fabricated on the rake face, main flank face and secondary flank face by laser machining, respectively. The cutting tests were carried out on sawing platform by different cutting speed, and the cutting forces, noise, temperatures, machined surface quality and sawteeth wear were studied. The results indicated that textures fabricated on the secondary flank face can effectively reduce friction with the machined surface and improve the surface quality, and the roughness Ra was decreased about 2.5% ~ 12.5% compared with traditional saw blade. The improvement of saw blades cutting performance by preparing micro textures on the main flank face was greater than the textures fabricated on the rake face. Due to the synergistic action of textures at different cutting faces, the cutting force, noise and temperature of saw blades with textures prepared on all cutting surfaces was the lowest. The main wear forms of sawteeth were tip wear, surface adhesion wear and oxidation wear. Although the derivative cutting phenomenon was occurred, the micro textures were not filled completely, it signified that the micro textures still played the role of stored cutting fluids, reduced contact area and formed lubrication film.