| 引用本文: | 张志,杜延菱,崔慧军,等.考虑关联因素的智能化中长期电力负荷预测方法[J].电力系统保护与控制,2019,47(2):24-30.[点击复制] |
| ZHANG Zhi,DU Yanling,CUI Huijun,et al.Intelligent mid-long electricity load forecast method considering associated factors[J].Power System Protection and Control,2019,47(2):24-30[点击复制] |
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| 摘要: |
| 为了解决现有中长期负荷预测方法中存在的预测精度欠优及场景适用性较差等问题，提出一种考虑影响因素协调关系及滞后效应的新型中长期负荷预测方法。首先通过关联矩阵筛选得到影响负荷变化的强相关因素，结合计量学的X-12-ARIMA模型对负荷及其影响因素进行季节分解，得到3个特征分解部分。在此基础上，通过时滞效应检验确定滞后期数，结合主成分分析法去除数据噪声影响，进一步提升数据纯度。最后针对月度和季度负荷进行预测算例分析，通过比对其他时序外推方法的预测结果和非线性模型方法应用后的精度提升，验证了所提方法的有效性和适用性。 |
| 关键词: 中长期负荷预测 季节分解 时滞效应 主成分分析 |
| DOI：10.7667/PSPC201862 |
| 投稿时间：2018-06-21修订日期：2018-10-15 |
| 基金项目: |
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| Intelligent mid-long electricity load forecast method considering associated factors |
| ZHANG Zhi,DU Yanling,CUI Huijun,WANG Yang,HE Zhe,LAI Xiaowen |
| (State Grid Jibei Electric Power Co., Ltd, Beijing 100053, China;Beijing Tsintergy Technology Co., Ltd, Beijing 100080, China;Tsinghua University, Beijing 100084, China) |
| Abstract: |
| Since available forecast models have a lack of validity and applicability, a new electric load forecast method, considering co-integration relation and hysteresis effect of influenced factors, is proposed. Firstly, by the use of incidence matrix, this paper obtains the close associated factors which affects load change. Secondly, combined with metrology model, X-12-ARIMA, the data of load and influence factors is decomposed into three different parts. Then, based on the seasonal decomposition, it takes lag steps about time lag effects testing and uses Principle Component Analysis (PCA) for de-noising to enhance data purity further. Finally, according to monthly and seasonal load forecasting, numerical examples are studied to verify effectiveness and applicability of proposed method through comparing with other method forecast results and non-linear model forecasting accuracy improvement. |
| Key words: mid-long term load forecasting seasonal decomposition time lag effects principle component analysis |
