With the rapid development of new energy technologies and aiming at the proposal of the “DOUBLE CARBON” goal, the proportion of wind energy and other new sustainable energy power solutions in the power industry continues to increase and occupy a more critical position. However, the instability of wind power output brings serious challenges to safe and stable power grid operations. Therefore, accurate ultra-short-term wind power prediction is of great significance in stabilizing power system operations. This paper presents an ACNN-BiGRU wind power ultra-short-term prediction model based on the Attention mechanism, the fusion of convolutional neural network (CNN), and bidirectional gated recurrent unit (BiGRU). The model takes a single wind turbine as the prediction unit and uses the real-time meteorological data in the wind farm, the historical power data of the wind turbine, and the real-time operation data for parallel training. Then, it extracts the key features of the input data through CNN and uses the BiGRU network to conduct bidirectional modeling learning on the dynamic changes of the features proposed by CNN. In addition, the Attention mechanism is introduced to give different weights to BiGRU implicit states through mapping, weighting, and learning parameter matrix to complete the ultra-short-term wind power prediction. Finally, the actual observation data of a wind farm in Northwest China is used to verify the feasibility and effectiveness of the proposed model. The model provides new ideas and methods for ultra-short-term high-precision prediction for wind power.

Amulti-hop routing protocol based on the Secretary Bird Optimization Algorithm (SBOA) is proposed to address the issues of limited node energy and unbalanced energy consumption in a three-dimensional (3D) Bridge Wireless Sensor Network (BWSN). This protocol offers a comprehensive solution that optimizes network lifetime and energy distribution. The method for calculating the optimal number of cluster heads (CHs) in 3D BWSN is presented. Secondly, the network is clustered using the Fuzzy C-means (FCM) algorithm, and suitable CHs are selected via the entropy weight method. Thirdly, during the inter-cluster routing phase, SBOA is utilized to find the optimal path between CHs and base stations (BS). Finally, a re-clustering mechanism is proposed to control the frequency of updating the cluster structure, enhancing the energy efficiency of the proposed protocol. The experimental results demonstrate that the proposed protocol improves network lifetime, energy efficiency, and energy consumption balance by 135%, 7%, and 59.3%, respectively, compared to current mainstream methods. Therefore, the proposed protocol exhibits good adaptability in 3D BWSN.

The Bridge Health Monitoring System (BHMS) has been widely implemented and advocated globally to replace traditional bridge management and maintenance practices. Employing wireless sensors for collecting and transmitting bridge monitoring data via a self-organizing bridge wireless sensor network (BWSN) is crucial for ensuring the efficient operation and cost-effectiveness of BHMS. To validate and enhance the performance of BWSN, this study develops a bridge space model (SM-SSB) based on the Simple-supported Beam Bridge, and introduces a Critical-TOPSIS Clustering (CTC) routing algorithm on SM-SSB, employing multi-criteria decision-making (MCDM) for optimal cluster head (Optimal-CH) and relay node (Optimal-RN) selection efficiently. The CTC algorithm’s main benefit is its holistic approach to considering factors such as the distance between nodes and the base station (BS), node densityC node residual energy, and the frequency of CH selection. Utilizing the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm enhanced by the CRITIC (CRiteria Importance Though Intercrieria Correlation) method (referred to as CRITIC-TOPSIS) sorting all nodes and then proceeds to quickly opt for the Optimal-CH and the Optimal-RN within the subspace of each bridge space model simultaneously. In the Matlab environment, the CTC algorithm’s performance is benchmarked against LEACH, Fuzzy-TOPSIS, CTC, EEM-CRP, and AROA algorithms, examining metrics like dead node distribution, network life cycle, packet reception rate, and variance of energy consumption. Simulation outcomes indicate that the CTC algorithm achieves a balanced energy consumption across nodes, significantly boosting the energy efficiency of bridge sensor networks and fulfilling the BWSN routing algorithm’s conditions.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.