基于数据驱动的线性聚类ARIMA长期电力负荷预测

针对某些发达城市因负荷波动大而导致的长期电力负荷预测精度低问题,提出了一种基于数据驱动线性聚类(data-driven linear clustering,DLC)的自回归积分滑动平均(auto-regressive integral moving average,ARIMA)预测方法。首先,利用线性特征作为聚类标准对每年的大型变电站负荷数据集进行预处理;然后,对得到的每个子序列构建最优自回归积分滑动平均模型,以预测其相应的未来负荷;最后,汇总所有的模型预测结果从而获得电力系统长期负荷预测结果。从误差分析和应用结果可知,理论和实践都验证了所提出的方法在保证建模精度的同时能够降低随机预测误差,从而获得更稳定、更精准的电力系统负荷预测结果。

Long-term Power Load Forecasting Based on Data-driven Linear Clustering ARIMA

Aiming at the low accuracy of long-term power load forecasting in some developed cities due to large load fluctuation, an auto-regressive integral moving average (ARIMA) forecasting method based on data-driven linear clustering (DLC) is proposed. Firstly, linear features are used as clustering criteria to pre-process the annual load data set of large substations in order to prepare for modeling. Then, the optimal auto-regressive integral moving average model is constructed for each sub-sequence to predict its corresponding future load. Finally, the long-term load forecasting results of the power system are obtained by summarizing all the forecasting results of the models. From the results of error analysis and application, both theory and practice verify that the proposed method can reduce the random prediction error while ensuring the accuracy of modeling, thus achieving more stable and accurate load forecasting results of power system.