基于粒子群算法的配电台区售电量精准预测方法

针对不同时段和不同模态下，售电量预测准确性较低的问题，设计一个基于粒子群算法的配电台区售电量精准预测方法。建立灰色模型挖掘数据，输入电力负荷数据，并进行归一化处理，提取容量利用特征，准变预测问题表现形式，建立售电量增长趋势模型，计算曲线拟合问题，采用粒子群算法设计配电台区售电量精准预测过程，优化权重变换函数和粒子权值，完成配电台区售电量精准预测。实验结果表明，该方法在一个月内售电量、一年内售电量、低频模态预测与高频模态预测上，都具有较高的准确性，满足配电台区售电量精准预测需求。

Precise prediction method of electricity sales in distribution station area based on particle swarm algorithm

Aiming at the problem of low accuracy of electricity sales prediction in different periods and modes, an accurate prediction method of electricity sales in distribution station area based on particle swarm optimization algorithm is designed. Establish grey model, mine data, input power load data, normalize, extract capacity utilization characteristics, quasi variable prediction problem expression, establish power sales growth trend model, calculate curve fitting problem, use particle swarm optimization algorithm to design the accurate prediction process of power sales in distribution station area, optimize weight transformation function and particle weight, and complete the accurate prediction of power sales in distribution station area. The experimental results show that this method has high accuracy in one month''s electricity sales, one year''s electricity sales, low-frequency modal prediction and high-frequency modal prediction, and meets the demand of accurate prediction of electricity sales in distribution station area.