No abstract

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.

Telemetry data is the important data for the ground station to obtain the working status and environmental parameters of the aircraft system. Its fusion processing is the key technology to select the best selection of multiple channels of data and improve the reliability and accuracy of the entire recording. Due to the large amount of telemetry data, there is a delay error in the transmission process, and the phenomenon of frame loss and code error is accompanied by severe challenges for the alignment and optimization of data fusion. The research starts from the application background of fusion technology, introduces and analyzes the characteristics of telemetry data and the difficulties of fusion technology; outlines the current alignment and optimization related research results and development process, according to engineering requirements and technical points, from real-time and after-event From the perspective, the alignment algorithms and quality evaluation algorithms involved are classified and analyzed in detail; finally, the shortcomings of the existing methods are summarized, and the future development direction is looked forward to provide references for related researchers.

No abstract