Medium-term hydrological streamflow forecasting can guide water dispatching departments to arrange the discharge and output plan of hydropower stations in advance, which is of great significance for improving the utilization of hydropower energy and has been a research hotspot in the field of hydrology. However, the distribution of water resources is uneven in time and space. It is important to predict streamflow in advance for the rational use of water resources. In this study, a Bayesian model average integrated prediction method is proposed, which combines artificial intelligence algorithms, including long-and short-term memory neural network (LSTM), gate recurrent unit neural network (GRU), recurrent neural network (RNN), back propagation (BP) neural network, multiple linear regression (MLR), random forest regression (RFR), AdaBoost regression (ABR) and support vector regression (SVR). In particular, the simulated annealing (SA) algorithm is used to optimize the hyperparameters of the model. The practical application of the proposed model in the ten-day scale inflow prediction of the Three Gorges Reservoir shows that the proposed model has good prediction performance; the Nash–Sutcliffe efficiency NSE is 0.876, and the correlation coefficient r is 0.936, which proves the accuracy of the model.

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.

The optimal scheduling of cascade reservoirs is an important water resource management and regulation method. In the actual operation process, its nonlinear, high-dimensional, and coupled characteristics become increasingly apparent under the influence of multiple constraints. In this study, an integrated multistrategy particle swarm optimization (IMPSO) algorithm is proposed to realize the optimal operation of mid- and long-term power generation in cascade reservoirs according to the solution problem in the scheduling process of cascade reservoirs. In IMPSO, a variety of effective improvement strategies are used, which are combined with the standard PSO algorithm in different steps, among which beta distribution initialization improves population diversity, parameter adaptive adjustment accelerates convergence speed, and the Lévy flight mechanism and adaptive variable spiral search strategy balance the global and local search capabilities of the algorithm. To handle complex constraints effectively, an explicit–implicit coupled constraint handling technique based on constraint normalization is designed to guide the update process into the feasible domain of the search space. The feasibility of the proposed method is verified in the mid- and long-term power generation optimization scheduling of the lower reaches of the Jinsha River–Three Gorges cascade hydropower reservoirs. The results show that the proposed method outperforms the other methods in terms of search accuracy and has the potential to improve hydropower resource utilization and power generation efficiency significantly.

Reliable forecast precipitation can support disaster prevention and mitigation and sustainable socio-economic development. Improving forecast precipitation accuracy remains a challenge. Therefore, a novel method for multi-model forecast precipitation integration considering long lead times was proposed based on deep learning. First, the accuracy of numerical forecast precipitation was evaluated under different lead times. Secondly, an integrated model was built by coupling the attention mechanism and a long short-term memory neural network (LSTM). Finally, integrated forecast precipitation was obtained by taking high-precision numerical forecast precipitation as an input and examining its accuracy and applicability. Considering the example of the Yalong River, the results showed the following: (1) numerical forecast precipitation fails to forecast precipitation of a ≥10 mm/d intensity well, and is less applicable in streamflow forecast; (2) traditional machine learning methods for integrating multi-model forecast precipitation fail to forecast precipitation of a ≥25 mm/d intensity; (3) the LSTM-A integration model formed by attention weighting after the LSTM output can combine the advantages of numerical forecast precipitation under different intensities and improve the forecast precipitation accuracy for 7-day lead times; and (4) the LSTM-A integrated forecast precipitation has the best applicability in streamflow forecast, with an NSE above 0.82 and an MRE below 30% with 7-day lead times. These findings contribute to improving precipitation forecast accuracy at different intensities and enhancing defense against extreme weather events.

Cascade reservoir operation can ensure the optimal use of water and hydro-energy resources and improve the overall efficiency of hydropower stations. A large number of studies have used meta-heuristic algorithms to optimize reservoir operation, but there are still problems such as the inability to find a global optimal solution and slow convergence speed. Lightning search algorithm (LSA) is a new meta-heuristic algorithm, which has the advantages such as high convergence speed and few parameters to be adjusted. However, there is no study on the application of LSA in reservoir operation. In this paper, LSA is used to solve the problem of reservoir operation optimization to verify its feasibility. We also propose an improved LSA algorithm, the frog-leaping–particle swarm optimization–LSA (FPLSA), which was improved by using multiple strategies, and we address the shortcomings of LSA such as low solution accuracy and the tendency to fall into local optima. After preliminary verification of ten test functions, the effect is significantly enhanced. Using the lower Jinsha River–Three Gorges cascade reservoirs as an example, the calculation is carried out and compared with other algorithms. The results show that the FPLSA performed better than the other algorithms in all of the indices measured which means it has stronger optimization ability. Under the premise of satisfying the constraints of cascade reservoirs, an approximate optimal solution could be found to provide an effective output strategy for cascade reservoir scheduling.

Short-term optimal scheduling of cascade hydropower stations enhances their flexible regulation and power generation capabilities. However, nonlinear function relationships and multistage and hydraulic interdependencies present significant challenges, resulting in considerable solution errors, premature convergence, and high computational demands. This study proposes a progressive linear programming method that decomposes nonlinear functions to address these challenges. First, to accurately represent nonlinear functions and mitigate computational complexity, the entire feasible domain is partitioned into multiple contiguous subdomains in which nonconvex nonlinear functions within each subdomain can be equivalently replaced by linear relationships. Second, a progressive linear programming optimization algorithm is devised to prevent premature convergence, utilizing continuous subdomains rather than discrete points as state variables and incorporating the progressive optimality principle. Finally, to increase the solution efficiency, a dimensionality reduction strategy via the feasible domain state dynamic acquisition method is presented and optimized after excluding the infeasible states in each stage. The simulation of three cascade hydropower stations in a river basin in southwest China shows that the proposed method can achieve a superior peak regulation effect compared to the conventional mixed integer linear programming and progressive optimality algorithm. During the dry and wet seasons, the residual load peak–valley differences at the three stations are reduced by 612 MW and 521 MW compared to the MILP and 1889 MW and 2439 MW compared to the POA, which underscores the effectiveness of the method in optimizing the short-term scheduling of cascade hydropower stations.