引用本文: | 周胜瑜,周任军,李红英,等.采用混合语言信息群决策的电力负荷密度预测法[J].电力系统保护与控制,2014,42(7):15-22.[点击复制] |
ZHOU Sheng-yu,ZHOU Ren-jun,LI Hong-ying,et al.Power load density prediction method of using group decision-making of mixed language information[J].Power System Protection and Control,2014,42(7):15-22[点击复制] |
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摘要: |
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。 |
关键词: 混合语言信息群决策方法 城市电力负荷密度预测 BP神经网络 三大类指标 指标综合评分值 |
DOI:10.7667/j.issn.1674-3415.2014.07.003 |
投稿时间:2013-07-01修订日期:2013-08-16 |
基金项目:国家自然科学基金资助(51277016);湖南省高校创新平台开放基金项目(12K074);湖南省研究生科研创新项目立项(CX2011B359) |
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Power load density prediction method of using group decision-making of mixed language information |
ZHOU Sheng-yu,ZHOU Ren-jun,LI Hong-ying,KANG Xin-wen |
(Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha 410004, China) |
Abstract: |
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. |
Key words: group decision-making method of mixed language information urban density of power load forecasting BP neural network three types of indicators comprehensive score values of indicators |