摘要
提出了一种新的能计及不确定性因素的空间负荷预测结果综合调整的区间方法。首先建立了多层分区的空间负荷预测区间模型,将预测单元分为总量层、数据收集层和仿真层,既能结合趋势法和仿真法的优点,又能在保证足够土地划分解析度和预测精度的前提下有效控制数据收集的工作量。然后提出了基于该模型的空间负荷预测综合调整区间方法,这是一种在信息不完备条件下的负荷分布估计方法,解决了实际中空间负荷预测结果综合调整的难题。最后通过实例说明了该方法的实用性和有效性。
This paper presents a new interval-based calibration method for spatial load forecasting (SLF), in which can deal with uncertainties in data collection and forecasting efficiently. Firstly, an interval-based SLF model with the multi-layer decomposition structure is set up, which divides forecasting units into gross load layer, data collection layer and simulation layer. This model incorporates both advantages of trending and simulation methods of SLF; and can effectively reduce work in data collection and maintenance as well as assure adequate spatial resolution in land decomposition and precision. Further, an interval-based algorithm based on the model is addressed to resolve the spatial load calibration problem, which is extremely tough and time-consuming in practical SLF processes. The proposed method can estimate load distribution with incomplete information, which provides a powerful tool for calibration of spatial load forecasting results and decision-making. A practical SLF case has shown the applicability and the effectiveness of the proposed method.
出处
《电力系统自动化》
EI
CSCD
北大核心
2004年第12期12-17,共6页
Automation of Electric Power Systems
基金
科技部科技型中小企业技术创新基金资助项目(99C26221200375)
关键词
空间负荷预测
多层分区
综合调整
区间方法
spatial load forecasting
multi-layer decomposition
calibration
interval algorithm
