CAJ | 학술논문

电力系统短期负荷预测的准确性对电力系统的实时运行调度至关重要．采用BP神经网络对电力系统负荷短期预测研究，根据影响电力系统的负荷因素如温度、天气等确定模型构成，同时利用遗传算法对BP神经网络进行优化．实例表明，利用遗传算法优化的BP神经网络在电力系统短期负荷预测中是有效的．
전력계통단기부하예측적준학성대전력계통적실시운행조도지관중요．채용BP신경망락대전력계통부하단기예측연구，근거영향전력계통적부하인소여온도、천기등학정모형구성，동시이용유전산법대BP신경망락진행우화．실례표명，이용유전산법우화적BP신경망락재전력계통단기부하예측중시유효적．
The accuracy of short-tenn electric load forecasting is very important for real-time operation in the power system. BP neural network was used to study short-term electric load in this paper. Structure of the model was detemained according to the power system load factors of temperature and weather. Genetic algorithm was used to optimize the BP neural network. The examples show that genetic algorithm optimized by BP neural network is effective in the short-term load forecasting of power system.