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In order to solve the thermal power plant load randomness, short-term power load prediction accuracy poor, long calculation time and other problems, the paper proposes a prediction method combined with Improved Beetle Antennae Search（IBAS）algorithm and BP neural network. Model in the history of the thermal power plant active load, season, date type and weather data as input factors, by introducing elite strategy, the optimization of single beetle is extended to group optimization, and the search step length of beetle is improved. So that BP parameters could be effectively optimized within the search range of IBAS, to optimize the weight of BP neural network, enhance its search and optimization ability, and improve the performance and accuracy of the prediction network. Using four standard test functions, the improved model compared with standard BAS algorithm, the introduction of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the precision evaluation index of PSO-BP network, BAS-BP model, IBAS-BP prediction model for evaluation. The experimental results show that compared with other kinds of model calculation results, the IBAS-BP model has better prediction performance.The load forecast result of thermal power plant is taken as the input of the plant level load optimization distribution system (plant level AGC), and the predicted value of the future load of a single unit is obtained through the load optimization distribution system, so as to minimize the coal consumption of power supply and improve the operation economy of thermal power plant units.