Abstract: In order to solve the problem that the accuracy of multi-segment short-term load forecasting is low due to the strong chaotic characteristics of load data and the influence of noise in the power system, a power system multi-segment short-term load forecasting method based on improved particle swarms is proposed. Based on the historical data of the power system, the concept of particle aggregation is introduced, the solution space is established, the global load data is searched in the space, and the original data is introduced into the solution space to determine the data distribution range. The optimal objective function is established, the adaptive load particle weight is calculated using the law of linear decline, and the particle weight is continuously approached to the optimal value with the iterative update function. The local prediction function and the global prediction function are integrated with the improved particle swarm prediction law, and the matrix is adjusted with the largest decision weight coefficient to complete the load forecast. Simulation experiments prove that the proposed method has strong ability to judge and analyze load data, good adaptability, and high degree of fit between the predicted results and actual values.

Key words: multi-segment short-term load, improved particle group, optimal search, solution space, linear decreasing law