摘要
针对当前电力系统中长期预测模型复杂、适应性差等不足,提出基于大数据的中长期负荷预测方法。该方法以数据为中心,将大区域的负荷进行分层分区及分类,并研究数据之间的关联性。针对各区间负荷结构等特点,在数据分析基础上,建立与数据相适应的负荷预测模型,实现大区域内负荷的精细化预测。最后以某地区电网为例,验证了该方法的有效性。
A mid-long term load forecasting method based on big data theory is proposed because of the present complicated forecasting models and bad adaptability. The method focuses on the data and the load in large area is partitioned to different levels and classified to different types. Then the relationships among data are researched. Based on the characteristic of each partition and analysis of corresponding data, the load forecasting model is established, and thus the elaborate load forecasting is realized. In the end, the validity of the method is proved by practical example in power system.
基金
国家自然科学基金(51107090)。
