| 引用本文: | 马临超,齐山成,牛 赛,等.考虑小区发展不均衡性和不确定性的多阶段空间负荷预测[J].电力系统保护与控制,2021,49(1):91-97. |
| MA Linchao,QI Shancheng,NIU Sai,et al.Multi-stage spatial load forecasting considering the imbalance and uncertainty of the development of the sub-area[J].Power System Protection and Control,2021,49(1):91-97 |
|
| 摘要: |
| 为减少小区发展不均衡性和不确定性对空间负荷预测精度的影响，结合聚类分析与马尔科夫理论提出了一种多阶段空间负荷预测模型。首先，提取单位面积最大负荷、用电量、平均负荷百分比作为表征小区发展不均衡性的指标，利用k-means算法对小区聚类，确定各个发展阶段的负荷密度。其次，统计不同发展阶段间的转移概率，形成马尔科夫链的状态转移矩阵，揭示空间负荷变化规律，以处理小区发展不确定性。再次，利用业扩报装信息、分类饱和密度及状态转移向量建立近中远期负荷预测模型。实例验证表明，该模型能够切实有效地考虑经济发展的不确定性及用电水平的差异性，各阶段负荷预测结果均具有较高的可信度。 |
| 关键词: 空间负荷预测 聚类 负荷密度 马尔科夫链 饱和密度 |
| DOI：DOI: 10.19783/j.cnki.pspc.200325 |
| 分类号: |
| 基金项目:国家重点研发计划项目资助(2017YFB0902800)；河南省科技攻关项目资助(182102210258) |
|
| Multi-stage spatial load forecasting considering the imbalance and uncertainty of the development of the sub-area |
|
MA Linchao, QI Shancheng, NIU Sai, SONG Qipeng
|
|
(1. Henan Institute of Technology, Xinxiang 453003, China; 2. Henan Engineering Research Center of New Energy Power
Generation Key Equipment, Xinxiang 453003, China; 3. State Grid Nanyang Power Supply Company,
Nanyang 473000, China; 4. China Electric Power Research Institute, Beijing 100192, China)
|
| Abstract: |
| In order to reduce the impact of imbalance and uncertainty of the sub-area development on spatial load forecasting accuracy, a multi-stage spatial load forecasting model is proposed by combining cluster analysis and Markov theory. First, the maximum load, electricity consumption and average load percentage of unit area are extracted as indicators representing the imbalance of the sub-area development, and a k-means algorithm is adopted to cluster the sub-area to determine the load density of each development stage. Secondly, the transition probability between different development stages is summarized to form the state transition matrix of a Markov Chain, and reveal the change rule of spatial load, so as to deal with the uncertainty of the sub-area development. Thirdly, the short-, medium- and long-term spatial load forecasting models are established using information of industry expansion, the classification saturation density and state transfer vector. Finally, an example proves that the model can effectively consider the uncertainty of economic development and the difference of power consumption level, and the forecasting results of each stage have high reliability.
This work is supported by National Key Research and Development Program of China (No. 2017YFB0902800) and Scientific and Technological Project of Henan Province (No. 182102210258). |
| Key words: spatial load forecasting clustering load density Markov Chain saturated density |
