| 摘要: |
| 夏季城市的短期电力负荷预测不仅与过去的电力负荷数据有关，并且受温度、风力、降水量等因素影响明显，存在明显的突变结构。为了对夏季城市的短期电力负荷进行预测，根据协整理论采用SAS软件建立起电力负荷序列与输入序列“温度”之间的ARIMAX模型，充分挖掘序列的内部自相关信息以及序列与序列之间的相关关系。采取最小信息量准则“AIC-SBC”进行比较可知，ARIMAX模型比经典时间序列ARMA模型的信息量要小，相对误差更小，拟合结果更为精确，在存在突变结构且具有显著影响因素的短期电力负荷预测领域具有很高的应用价值。 |
| 关键词: 短期电力负荷预测 时间序列 协整理论 ARIMAX模型 突变结构 |
| DOI：10.7667/j.issn.1674-3415.2015.04.017 |
| 投稿时间：2014-05-12修订日期：2014-08-07 |
| 基金项目:国家自然科学基金项目(70671039) |
|
| Summer short-term load forecasting based on ARIMAX model |
| CUI Herui,PENG Xu |
| (School of Economics and Management, North China Electric Power University, Baoding 071003, China) |
| Abstract: |
| Summer short-term load forecasting in the city is not only related with the past power load data and affected by temperature, wind, precipitation and other factors, etc. Mutation structure is obvious in this data. In order to forecast summer short-term power load in the city, this paper establishes the ARIMAX model between the power load sequence and the input sequence "temperature" based on Cointegration Relation Theory by SAS, fully exploits self-relevant information of the internal sequence as well as the correlation between sequences. It can be seen from the minimum amount of information standards "AIC-SBC", the amount of information about ARIMAX model is smaller than the classic time series method ARMA model and the relative error is smaller. The fitting results are more accurate. This model has a high value under the presence of mutation structure and significant influence factors in the short-term load forecasting field. |
| Key words: short-term load forecasting time series cointegration theory ARIMAX model mutant structure |
