Aimed at the problem of order determination of short-term power consumption in a time series model, a new method was proposed to determine the order and the moving average of the ARMA model by particle swarm optimization (PSO).According to the difference between the predicted value and the real value of the ARMA model, the fitness function of the particle swarm optimization algorithm is constructed, while the optimal solution which satisfies the ARMA model is confirmed by adjusting the inertia weight, population size, particle velocity, and iteration number. Finally, SVR regression is performed by using a support vector machine to correct the residual sequence obtained after the prediction of ARMA. The final prediction result is obtained by adding the predicted values and corrected residual. Based on the data of historical electricity load of a residential district in 2016~2017, the proposed method is compared with the traditional models. The result of the use of MATLAB simulation shows that the method is simple and feasible, greatly improves the model prediction accuracy, and implements the new method for short-term load forecasting of a small sample.

中文翻译：

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基于粒子群算法的PSO-ARMA-SVR短期用电量预测研究

针对时间序列模型中短期功耗的阶次确定问题，提出了一种确定阶次和移动平均的新方法。根据粒子群优化算法（PSO）对ARMA模型进行了预测。根据ARMA模型的预测值与实际值之间的差异，构建了粒子群优化算法的适应度函数，并给出了满足ARMA模型的最优解。可以通过调整惯性权重，总体大小，粒子速度和迭代次数来确定。最后，使用支持向量机对SVR回归进行校正，以校正在预测ARMA之后获得的残差序列。通过将预测值与校正后的残差相加，可以得出最终的预测结果。根据居民区2016〜2017年的历史用电量数据，将该方法与传统模型进行了比较。