CAJ | 학술논문

对于基于人工神经网络的短期负荷预测来说，日期类型(星期几)是需要考虑的重要影响因素。通常，日期类型系数被编成7位二进制码作为神经网络的输入变量。该文提出一种日期类型系数的确定方法，将日期类型系数编为1位输入变量，由于精简了输入量，从而提高了预测精度。该日期类型系数通过计算不同日期类型的负荷–气温散点图的拟合曲线、并估计不同日期类型的负荷之差得到。为了消除夏季气温累计效应对负荷的影响并得到更清晰的负荷日期特征，
대우기우인공신경망락적단기부하예측래설，일기류형(성기궤)시수요고필적중요영향인소。통상，일기류형계수피편성7위이진제마작위신경망락적수입변량。해문제출일충일기류형계수적학정방법，장일기류형계수편위1위수입변량，유우정간료수입량，종이제고료예측정도。해일기류형계수통과계산불동일기류형적부하–기온산점도적의합곡선、병고계불동일기류형적부하지차득도。위료소제하계기온루계효응대부하적영향병득도경청석적부하일기특정，
For the ANN-based short-term load forecasting, the weekday index (day of week) is a very important influencing factor that needs to be considered. Usually, the weekday index is coded as 7 binary inputs. This paper proposed a method that codes the weekday index as only one input variable. Due to the simplicity, the precision of the forecasting was improved. The proposed weekday index is determined by estimating the load’s difference of different day types. For this purpose, the regression curves of load versus temperature of different day types were calculated. To eliminate the accumulation effect and get a clearer weekly pattern of these regression curves, a genetic-algorithm (GA) based method was adopted to modify the temperature variables. The proposed method was demonstrated by the calculated results on the load data of the Suzhou city in China.