CAJ | 학술논문

传统城市空间负荷密度预测法在实际预测过程中其结果的可信度依赖于大量有效的样本数据，而在实际中收集到较齐全的可行样本数据存在很大的难度。为此提出了一种将混合语言信息群决策方法和 BP 神经网络相结合的城市电力负荷密度预测法。该方法采用基于混合语言信息的群决策方法，通过各决策者的评价，计算城市各小区相应的经济、人口、地理环境的综合评分值，并利用 BP 神经网络，训练各指标综合评分值与相应的小区负荷密度，利用训练后的网络结构和待定小区的各指标综合评分结果，预测城市该小区的负荷密度。通过对城市若干小区的负荷密度及各指标综合评分值做比较分析，预测了部分小区的负荷密度值。结果表明预测计算过程摆脱了需要大量收集特定指标定量数据的问题，并且预测结果具有较高的可信度。
전통성시공간부하밀도예측법재실제예측과정중기결과적가신도의뢰우대량유효적양본수거，이재실제중수집도교제전적가행양본수거존재흔대적난도。위차제출료일충장혼합어언신식군결책방법화 BP 신경망락상결합적성시전력부하밀도예측법。해방법채용기우혼합어언신식적군결책방법，통과각결책자적평개，계산성시각소구상응적경제、인구、지리배경적종합평분치，병이용 BP 신경망락，훈련각지표종합평분치여상응적소구부하밀도，이용훈련후적망락결구화대정소구적각지표종합평분결과，예측성시해소구적부하밀도。통과대성시약간소구적부하밀도급각지표종합평분치주비교분석，예측료부분소구적부하밀도치。결과표명예측계산과정파탈료수요대량수집특정지표정량수거적문제，병차예측결과구유교고적가신도。
In the actual process of forecasting, the credibility of the result of traditional urban space load density prediction method depends on a number of sample data. But, in the actual, collecting a complete feasible data is quite difficult. Therefore this paper puts forward a way which combines the group decision-making method of mixed language information and the BP neural network to forecast the city power load density. This way uses group decision-making method of mixed language information to get the score value of the economy, population, geographic environment in all urban district, then by using BP neural network to train the score and the load density, after that utilizing the net to predict the load density of pending district. The result shows that not only the computation process can get rid of the problem which need large collection of specific indicators quantitative data but also the result is very good.