Regression based peak load forecasting using a transformation technique
Regression based peak load forecasting using a transformation technique
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DOI：
10.1109/59.331433
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发表时间：
1994-11
影响因子：
6.6
通讯作者：
T. Haida;S. Muto
中科院分区：
文献类型：
--
作者：
T. Haida;S. Muto
本文提出了一种基于回归的日高峰负荷预测方法，并采用了变换技术。为了准确预测全年的负荷，应考虑季节性负荷变化、年负荷增长和最近的日负荷变化。为了在负荷预测中处理这些特征，提出了一种变换技术。该技术由具有平移和反射方法的变换函数组成。利用前一年的数据点来估计转换函数，以便该函数将数据点转换成一组新的数据点，同时保留前一年的温度-负荷关系的形状。然后，稍微平移该函数，以便转换后的数据点符合当年温度-负荷关系的形状。最后，利用最新的日负荷和气象观测数据，进行多元回归分析，对预测模型进行估计。在春、秋两季的过渡季节，由于天气负荷的非线性特性而引起的预报误差较大。该技术的性能进行了验证与模拟东京电力公司的实际负荷数据。>
This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, one should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preservation of the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis, with the latest daily loads and weather observations, estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described. >