Search

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval's theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination ( R 2 ) higher than the other models, and it has the smallest mean absolute percentage error ( M A P E ) and root mean square error ( R M S E ). [ABSTRACT FROM AUTHOR]

For process control in engineering applications, the fewer the coding lines of optimization algorithms, the more applications there are. Therefore, this work develops a new straightforward metaheuristic optimization algorithm named the propagation search algorithm (PSA), stirred by the wave propagation of the voltage and current along long transmission lines. The mathematical models of the voltage and current are utilized in modeling the PSA, where the voltage and current are the search agents. The propagation constant of the transmission line is the control parameter for the exploitation and exploration of the PSA. After that, the robustness of the PSA is verified using 23 famous testing functions. The statistical tests, comprising mean, standard deviation, and p-values, for 20 independent optimization experiments are utilized to confirm the robustness of the PSA to find the best result and the significant difference between the outcomes of the PSA and those of the compared algorithms. Finally, the proposed PSA is applied to find the optimum design parameters of four engineering design problems, including a three-bar truss, compression spring, pressure vessel, and welded beam. The outcomes show that the PSA converges to the best solutions very quickly, which can be applied to those applications that require a fast response. [ABSTRACT FROM AUTHOR]