The Sparrow search (SSA) algorithm is easy to fall into the local optimal solution in the optimization of microgrid scheduling, and the Sparrow algorithm (T-GSSA) based on the adaptive t distribution and the improved golden sine has a better performance in the optimization of scheduling. The golden sine function in mathematics is used to optimize the iteration and improve the global search ability. At the same time, the golden section coefficient is added in the process of position updating to search the local area and balance the local and global search ability. It is easier to jump out of the local optimal solution by using the adaptive T-distribution variation method to change the individual target position. With minimum operation and maintenance cost and maximum environmental benefit as optimization objectives, a multiobjective optimal scheduling model was established under the condition of power balance and output constraints of each generating unit, and SSA and t-GSSA algorithms were solved and compared. The results show that t-GSSA algorithm is superior to SSA algorithm in convergence speed and precision, which improves the overall operation efficiency of microgrid to a certain extent.
KEYWORDS: Data modeling, Education and training, Statistical modeling, Mathematical optimization, Engineering, Process modeling, Machine learning, Statistical analysis, Power grids, Lithium
Because the deep learning model is highly dependent on data, and the extraction effect of data sample features will directly affect the prediction accuracy. Based on this, in order to improve the accuracy of short-term power load forecasting, a load forecasting method based on time series generation antagonism network TimeGAN and short-term memory network LSTM is proposed for data enhancement. First, in order to optimize the effect of model feature extraction, the sample six-dimensional feature data is reconstructed into nine-dimensional feature data according to the date and weather characteristics. Then, analyze the correlation between historical data and sample distance, use TimeGAN model to enhance the data, and then reconstruct the data set. Finally, the prediction model of long and short-term memory network is created to import the reconstructed data to predict the electric load in the next 24 hours. The experimental results show that this method is superior to the prediction methods of CNN-LSTM, CNN-BiLSTM and LSTM models, and TimeGan-LSTM has higher prediction accuracy.
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