CAJ | 학술논문

为了对广东省的能源需求进行准确的预测，首先分析了影响广东省能源需求的各种因素，构建了预测指标体系。在此基础上，针对能源系统非线性等复杂系统特征，结合粒子群算法和 BP 神经网络的优点，构建了改进的 PSO-BP 神经网络的预测模型，并通过主成分分析法对指标体系进行数据降维，以降低神经网络的规模和复杂程度。以广东省1985-2013年的能源需求数据进行模拟与仿真，并对2014-2018年的能源需求量进行预测，理论分析和实证研究表明，该方法能够很好的反映广东省能源需求的特征，预测结果较为准确合理。
위료대광동성적능원수구진행준학적예측，수선분석료영향광동성능원수구적각충인소，구건료예측지표체계。재차기출상，침대능원계통비선성등복잡계통특정，결합입자군산법화 BP 신경망락적우점，구건료개진적 PSO-BP 신경망락적예측모형，병통과주성분분석법대지표체계진행수거강유，이강저신경망락적규모화복잡정도。이광동성1985-2013년적능원수구수거진행모의여방진，병대2014-2018년적능원수구량진행예측，이론분석화실증연구표명，해방법능구흔호적반영광동성능원수구적특정，예측결과교위준학합리。
In order to make accurate forecast for energy demand of Guangdong province,this paper analyzed the various factors which impact on energy demand of Guangdong province,and constructed the predict index system.On this basis,ac-cording to the nonlinear characteristics of the energy system,combined with the advantages of particle swarm optimization algo-rithm and BP neural network,a prediction model was constructed based on PSO-BP neural network.And the method of princi-pal component analysis was used to reduce the dimensions of the prediction index system in order to reduce the size and com-plexity of the neural network.Then,this paper simulated the energy demand data of Guangdong province from 1 985 to 2013, and carried on the forecast energy demand of Guangdong province during 2014 to 2018.The theoretical analysis and empirical study show that this method can reflect the characteristics of energy demand of Guangdong province,and the predicted result is more accurate and reasonable.