Next day load curve forecasting using hybrid correction method
Next day load curve forecasting using hybrid correction method
复制标题
DOI：
10.1109/tpwrs.2004.831256
复制
发表时间：
2005-01
影响因子：
6.6
通讯作者：
T. Senjyu;P. Mandal;K. Uezato;T. Funabashi
中科院分区：
文献类型：
--
作者：
T. Senjyu;P. Mandal;K. Uezato;T. Funabashi
本文提出了一种基于混合校正法的短期负荷预测方法。传统的基于人工神经网络的短期负荷预测技术存在局限性，尤其是当天气变化具有季节性时。因此，我们提出了一种负荷校正方法，通过使用模糊逻辑的方法，其中的模糊逻辑，基于相似的日子，校正神经网络的输出，以获得第二天的预测负荷。具有加权因子的欧几里得范数用于选择类似的日子。提出了新相似日生成的负荷修正方法。神经网络在处理负荷预测曲线的非线性部分方面具有优势，而模糊规则是基于专家知识构造的。因此，通过结合这两种方法，测试结果表明，所提出的预测方法可以提供一个相当大的改善的预测精度，特别是因为它显示了如何减少神经网络预测误差在测试期间通过应用模糊逻辑校正的23%。通过对日本冲绳电力公司实际负荷数据的应用，说明了所提出的方法的适用性。
This work presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using a fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23% through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan.