基于改进自适应遗传算法综合能源规划的研究与分析
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摘要
为了解决日益严峻的社会环境和日趋枯竭资源问题,让不同能源结构能够得到优化配置,本文采用了改进的自适应遗传算法来对能源进行预测分析,借助计量自动化系统提供的大量电力负荷数据,基于用户群体分析与识别,改进自适应遗传算法等大数据技术对负荷进行预测,并对不同行业和部门,不同能源结构进行深入的分析与研究与探讨。对比传统的几种预测算法,得出改进的自适应遗传算法具有更加准确的预测能力,研究结果表明,提前做好相关能源的预测,对能源结构进行过综合的规划是很有必要的,可以引领能源模式走入一种全新的模式,开拓能源互联网新时代。为能源结构的转型升级做好必要的工作。能源的综合规划能缓解现在面临的能源危机和环境污染等严重的问题。
In order to solve the problem of increasingly severe social environment and increasingly exhausted resources,so that different energy structures can be optimized,this paper adopts an improved adaptive genetic algorithm to predict and analyze energy resources,With the help of a large number of power load data provided by metrology automation system,based on user group analysis and identification,improved adaptive genetic algorithm and other data technology,load forecasting is carried out,and different industries and departments,different energy structures are analyzed and discussed in depth. Compared with several traditional prediction algorithms,the improved adaptive genetic algorithm has more accurate prediction ability. The research results show that it is necessary to do a good job of related energy prediction in advance and make a comprehensive planning of energy structure. It can lead the energy model into a new mode and open up a new era of energy internet. To do the necessary work for the transformation and upgrading of energy structure. Comprehensive energy planning can alleviate the serious problems such as energy crisis and environmental pollution and other serious problems.
In order to solve the problem of increasingly severe social environment and increasingly exhausted resources,so that different energy structures can be optimized,this paper adopts an improved adaptive genetic algorithm to predict and analyze energy resources,With the help of a large number of power load data provided by metrology automation system,based on user group analysis and identification,improved adaptive genetic algorithm and other data technology,load forecasting is carried out,and different industries and departments,different energy structures are analyzed and discussed in depth. Compared with several traditional prediction algorithms,the improved adaptive genetic algorithm has more accurate prediction ability. The research results show that it is necessary to do a good job of related energy prediction in advance and make a comprehensive planning of energy structure. It can lead the energy model into a new mode and open up a new era of energy internet. To do the necessary work for the transformation and upgrading of energy structure. Comprehensive energy planning can alleviate the serious problems such as energy crisis and environmental pollution and other serious problems.
引文
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[2]SHAO XING,WANG RUCHUAN,HUANG HAIPING,et al.Load balanced coding aware multipath routing for wireless mesh networks[J].Chinese Journal of Electronics,2015,24(01):8-12.
[3]JI YIMU,YUAN YONGGE,HAN ZHIJIE,et al.An analysis and proof on self-similarity property of flash P2P internet video traffic[J].Chinese Journal of Electronics,2015,24(01):26-32.
[4]HE WEI,ZHAO RUILIAN,ZHU QUNXIONG.Integrating evolutionary testing with reinforcement learning for automated test generation of object-oriented software[J].Chinese Journal of Electronics,2015,24(01):38-45.
[5]ZHOU AIPING,LIU LIJUN,ZHU HUISHENG,et al.Parallel sketch based super node detection with traceability[J].Chinese Journal of Electronics,2018,27(06):1133-1140.
[6]和敬涵,罗易萍,罗国敏,等.基于小波多分辨分析和支持向量机的柔性直流系统单端保护方案[J].电网技术,2018,42(12):4022-4030.HE Jinghan,LUO Yiping,LUO Guomin,et al.A single-ended protection algorithm based on wavelet multi-resolution analysis and support vector machine for flexible DC systems[J].Power System Technology,2018,42(12):4022-4030.
[7]李鹏,何帅,韩鹏飞,等.基于长短期记忆的实时电价条件下智能电网短期负荷预测[J],电网技术,2018,42(12):4045-4052.LI Peng,HE Shuai,HAN Pengfei,et al.Short-term load forecasting of smart grid based on long-short-term memory recurrent neural networks in condition of real-time electricity price[J].Power System Technology,2018,42(12):4045-4052.
[8]杨跃,刘友波,刘俊勇,等.基于神经网络预测校核的暂态稳定预防控制[J].电网技术,2018,42(12):4076-4082.YANG Yue,LIU Youbo,LIU Junyong,et al.Preventive transient stability control based on neural network security predictor[J].Power System Technology,2018,42(12):4076-4082.
[9]周明,宋旭帆,涂京,等.基于非侵入式负荷监测的居民用电行为分析[J].电网技术,2018,42(10):3268-3274.ZHOU Ming,SONG Xufan,TU Jing,et al.Residential electricity consumption behavior analysis based on non-intrusive load monitoring[J].Power System Technology,2018,42(10):3268-3274.
[10]何永秀,陈倩,费云志,等.国外典型辅助服务市场产品研究及对中国的启示[J],电网技术,2018,42(09):2915-2922.HE Yongxiu,CHEN Qian,FEI Yunzhi,et al.Typical foreign ancillary service market products and enlightenment to China[J].Power System Technology,2018,42(09):2915-2922.
[11]邓东林,徐冘,陈剑,等.智能用电数据的采集与预处理[J].电力大数据,2019,22(03):81-86.DENG Donglin,XU Yin,CHEN Jian,et al.Acquisition and preprocessing of smart electric appliance network power data[J].Power Systems and Big Data,2019,22(03):81-86.
[12]耿俊成,张小斐,郭志民,等.电力通信网大数据应用场景开发及试点应用[J].电力大数据,2019,22(02):88-92.GENG Juncheng,ZHANG Xiaofei,GUO Zhimin,et al.Development and application of big data application scenario in the electric power communication network[J].Power Systems and Big Data,2019,22(02):88-92.
[13]苏晓龙,夏鹏,肖波,等.基于BP神经网络的PMS电流互感器设备状况评价系统[J].电力大数据,2019,22(01):59-65.SU Xiaolong,XIA Peng,XIAO Bo,et al.PMS current transformer equipment status evaluation system based on BP neural network[J].Power Systems and Big Data,2019,22(01):59-65.
[14]张全,齐红涛,刘鲲鹏,等.国外电力企业大数据需求侧应用分析[J].电力大数据,2018,21(12):26-31.ZHANG Quan,QI Hongtao,LIU Kunpeng,et al.Analysis of big data demand side application of foreign power enterprises[J].Power Systems and Big Data,2018,21(12):26-31.
[15]汪兴.“互联网+”模式下电网企业智能化建设与管理[J].电力大数据,2018,21(12):43-46.WANG Xing."Internet+"mode of intelligent construction and management of power grid enterprises[J].Power Systems and Big Data,2018,21(12):43-46.
[16]贺红燕.基于大数据的智能电网关键技术研究[J].电源技术,2016,40(08):1713-1714.HE Hongyan.Research on key technology of smart grid based on big data[J].Chinese Journal of Power Sources,2016,40(08):1713-1714.