CAJ | 학술논문

由于产业结构的调整、居民消费能力消费结构的变化和市场化等因素的影响,城区中长期电力负荷预测具有相当的难度.建立一个基于遗传算法和BP算法相结合的神经网络预测模型,以南昌市为例做实证,并与传统BP神经网络和模拟退火预测结果做对比,验证了该模型的准确性.最后对城区未来十几年的基本用电负荷进行了预测和分析.
유우산업결구적조정、거민소비능력소비결구적변화화시장화등인소적영향,성구중장기전력부하예측구유상당적난도.건립일개기우유전산법화BP산법상결합적신경망락예측모형,이남창시위례주실증,병여전통BP신경망락화모의퇴화예측결과주대비,험증료해모형적준학성.최후대성구미래십궤년적기본용전부하진행료예측화분석.
Due to the industrial structure adjustment,the change of resident consumption ability and pattern of consumption,and market-oriented and so on,long-term/mid-term power load forecasting for urban plans faces considerable difficulties. In the past two years,the methods that combined genetic algorithm and Back Propagation (BP) algorithm have been used for short-term power load forecasting rather than long-term/mid-term power load forecast of electricity. In this paper,a neural network prediction model with combination of genetic algorithm and BP neural network was established;the example in Nanchang was given to validate the accuracy of the algorithm,by comparing with the traditional BP neural network and Simulated Annealing (SA) prediction. Then the basic electricity load of Nanchang in the next dozens of years was forecasted and analyzed.