考虑概率区间的微电网短期负荷多目标预测方法
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摘要
微电网负荷随机性强、波动大,负荷单点预测已经难以满足微电网稳定运行需要.提出一种考虑概率区间的微电网短期负荷多目标预测方法,以循环神经网络为预测模型,以逼近理想解排序策略、网格筛选策略对基本多目标人工蜂群算法进行改进,优化循环神经网络的权值和阈值,避免单目标区间预测中惩罚系数难以选择的问题,对历史负荷数据进行记忆并修正预测结果,有效提高微电网短期负荷区间预测准确性与可靠性.仿真结果表明,本文所构建的考虑概率区间的微电网短期负荷多目标预测方法,预测性能优越、结果准确,可为微电网安全经济调度提供决策依据.
Load of microgrid has characteristics of strong randomicity and large fluctuation, so that single point prediction cannot satisfy the need of microgrid operating stability. In this paper,a modified multi-objective optimization prediction intervals(PIs) method for microgrid load is proposed, recurrent neural network(RNN) is adopted to build load prediction model, technique for order preference by similarity to an ideal solution and the grid selection strategy are introduced to modify multi-objective artificial bee colony algorithm(MMOABC),which optimizes the RNN prediction model, improving the accuracy and reliability of microgrid short-term load intervals prediction. The experiment results show that the proposed method for microgrid load has superior performance,which can provide the decision-making basis for the safety and economy of microgrid operation.
Load of microgrid has characteristics of strong randomicity and large fluctuation, so that single point prediction cannot satisfy the need of microgrid operating stability. In this paper,a modified multi-objective optimization prediction intervals(PIs) method for microgrid load is proposed, recurrent neural network(RNN) is adopted to build load prediction model, technique for order preference by similarity to an ideal solution and the grid selection strategy are introduced to modify multi-objective artificial bee colony algorithm(MMOABC),which optimizes the RNN prediction model, improving the accuracy and reliability of microgrid short-term load intervals prediction. The experiment results show that the proposed method for microgrid load has superior performance,which can provide the decision-making basis for the safety and economy of microgrid operation.
引文
[1]张永明,邓盛川,齐维贵.局域支持向量回归与误差区间估计的概率预报方法及其应用研究[J].电子学报,2010,38(01):190-194.Zhang Yong-ming,Deng Sheng-chuan,Qi Wei-gui.Probabilistic prediction approach based on local support vector regression and interval estimation of its error[J].Acta Electronica Sinica,2010,38(01):190-194.(in Chinese)
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[5]陈益哲,张步涵,王江虹,等.基于短期负荷预测的微网储能系统主动控制策略[J].电网技术,2011,35(08):35Chen Yi-zhe,Zhang Bu-han,Wang Jiang-hong,et al.Active control strategy for microgrid energy storage system based on short-term load forecasting[J].Power System Technology,2011,35(08):35-40.(in Chinese)
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[10]汤庆峰,刘念,张建华,等.基于EMD-KELM-EKF与参数优选的用户侧微电网短期负荷预测方法[J].电网技术,2014,38(10):2691-2699.Tang Qing-feng,Liu Nian,Zhang Jian-hua,et al.A shortterm load forecasting method for micro-grid based on EMD-KELM-EKF and parameter optimization[J].Power System Technology,2014,38(10):2691-2699.(in Chinese)
[11]陈民铀,朱博,徐瑞林,等.基于混合智能技术的微电网剩余负荷超短期预测[J].电力自动化设备,2012,32(05):13-18.
[12]万昆,柳瑞禹.区间时间序列向量自回归模型在短期电力负荷预测中的应用[J].电网技术,2012,36(11):77-81.Wan Kun,Liu Rui-yu.Application of interval time-series vector autoregressive model in short-term load forecasting[J].Power System Technology,2012,36(11):77-81.(in Chinese)
[13]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17):1
[14]张弘,朱永佳,范磊磊,等.基于马尔科夫修正的中长期电力负荷组合区间预测[J].华东电力,2013,41(01):33-36.Zhang Hong,Zhu Yong-jia,Fan Lei-lei,et al.Mid-long term load interval forecasting based on Markov modification[J].East China Electric Power,2013,41(01):33-36.(in Chinese)
[15]方仍存,周建中.应用聚类算法和混沌理论的短期负荷概率性区间预测[J].电网技术,2010,36(11):65-69.Fang Reng-cun,Zhou Jian-zhong.Probabilistic interval forecasting of short-term load on the basis of clustering algorithm and chaos theory[J].Power System Technology,2010,36(11):65-69.(in Chinese)
[16]Khosravi A,Nahavandi S,Creighton D,et al.Comprehensive review of neural network-based prediction intervals and new advances[J].IEEE Transactions on Neural Networks,2011,22(9):1341-1356.
[17]Khosravi A,Nahavandi S,Creighton D,et al.Lower upper bound estimation method for construction of neural networkbased prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[18]Quan H,Srinivasan D,Khosravi A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(2):303-315.
[19]SI Yujing,LI Ta,PAN Jielin,YAN Yonghong.A prefix tree based n-best list re-scoring strategy for recurrent neural network language model[J].Chinese Journal of Electronics,2014,23(1):70-74.
[20]Chen P A,Chang L C,Chang F J.Reinforced recurrent neural networks for multi-step-ahead flood forecasts[J].Journal of Hydrology,2013,497:71-79.
[21]陈杰,沈艳霞,陆欣.基于信息反馈和改进适应度评价的人工蜂群算法[J].智能系统学报,2016,02:172-179.Chen Jie,Shen Yan-xia,Lu Xin.Artificial bee colony algorithm based on information feedback and an improved fitness value evaluation[J].CAAI Transactions on Intelligent Systems,2016,02:172-179.(in Chinese)
[22]周黎,周承恩,李海滨.寻求“理想”解的改进多目标粒子群优化算法[J].控制与决策,2015,30(09):1653-1659.Zhou Li,Zhou Cheng-en,Li Hai-bin.Improved multi-objective particle swarm optimization algorithm that can give“ideal”solution[J].Control and Decision,2015,30(09):1653-1659.(in Chinese)
[23]Chen J,Yang D.Constrained handling in multi-objective optimization based on quantum-behaved particle swarm optimization.[A].International Conference on Natural Computation ICNC 2010[C].Yantai,Shandong,China,2010.3887-3891.
[24]高海兵,高亮,周驰,等.基于粒子群优化的神经网络训练算法研究[J].电子学报,2004,32(09):1572-1574.Gao Hai-bing,Gao Liang,Zhou Chi,et al.Particle swarm optimization based algorithm for neural network learning[J].Acta Electronica Sinica,2004,32(09):1572-1574.(in Chinese)
[25]Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[26]Akbari R,Hedayatzadeh R,Ziarati K,et al.A multi-objective artificial bee colony algorithm[J].Swarm&Evolutionary Computation,2012,2(1):39-52.
[2]孙孝峰,吕庆秋.低压微电网逆变器频率电压协调控制[J].电工技术学报,2012,27(8):77-84.Sun Xiao-feng,Lv Qin-qiu.Improved PV control of gridconnected inverter in low voltage micro-grid[J].Transactions of China Electrotechnical Society,2012,27(8):77-84.(in Chinese)
[3]鲁宗相,王彩霞,闵勇,等.微电网研究综述[J].电力系统自动化,2007,31(19):100-107.Lu Zong-xiang,Wang Cai-xia,Min Yong,et al.Overview on microgrid research[J].Automation of Electric Power Systems,2007,31(19):100-107.(in Chinese)
[4]茆美琴,周松林,苏建徽.基于风光联合概率分布的微电网概率潮流预测[J].电工技术学报,2014,19(02):55-63.Mao Mei-qin,Zhou Song-lin,Su Jianhui.Probabilistic power flow forecasting of microgrid based on joint probability distribution about wind and irradiance[J].Transactions of China Electrotechnical Society,2014,19(02):55-63.(in Chinese)
[5]陈益哲,张步涵,王江虹,等.基于短期负荷预测的微网储能系统主动控制策略[J].电网技术,2011,35(08):35Chen Yi-zhe,Zhang Bu-han,Wang Jiang-hong,et al.Active control strategy for microgrid energy storage system based on short-term load forecasting[J].Power System Technology,2011,35(08):35-40.(in Chinese)
[6]周念成,邓浩,王强钢,等.光伏与微型燃气轮机混合微网能量管理研究[J].电工技术学报,2012,27(01):74-84.Zhou Nian-cheng,Deng Hao,Wang Qianggang,et al.Energy management strategy of PV and micro-turbine hybrid micro-grid[J].Transactions of China Electrotechnical Society,2012,27(01):74-84.(in Chinese)
[7]刘小平,丁明,张颖媛,等.微网系统的动态经济调度[J].中国电机工程学报,2011,31(31):77-84.Liu Xiao-ping,Ding Ming,Zhang Ying-yuan,et al.Dynamic economic dispatch for microgrids[J].Proceedings of the CSEE,2011,31(31):77-84.(in Chinese)
[8]Amjady N,Keynia F,Zareipour H.Short-term load forecast of microgrids by a new bilevel prediction strategy[J].IEEE Transactions on Smart Grid,2010,1(3):286-294.
[9]刘念,张清鑫,刘海涛.基于核函数极限学习机的微电网短期负荷预测方法[J].电工技术学报,2015,30(08):218-224.Liu Nian,Zhang Qing-xin,Liu Haitao.Online short-term load forecasting based on ELM with kernel algorithm in micro-grid environment[J].Transactions of China Electrotechnical Society,2015,30(08):218-224.(in Chinese)
[10]汤庆峰,刘念,张建华,等.基于EMD-KELM-EKF与参数优选的用户侧微电网短期负荷预测方法[J].电网技术,2014,38(10):2691-2699.Tang Qing-feng,Liu Nian,Zhang Jian-hua,et al.A shortterm load forecasting method for micro-grid based on EMD-KELM-EKF and parameter optimization[J].Power System Technology,2014,38(10):2691-2699.(in Chinese)
[11]陈民铀,朱博,徐瑞林,等.基于混合智能技术的微电网剩余负荷超短期预测[J].电力自动化设备,2012,32(05):13-18.
[12]万昆,柳瑞禹.区间时间序列向量自回归模型在短期电力负荷预测中的应用[J].电网技术,2012,36(11):77-81.Wan Kun,Liu Rui-yu.Application of interval time-series vector autoregressive model in short-term load forecasting[J].Power System Technology,2012,36(11):77-81.(in Chinese)
[13]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17):1
[14]张弘,朱永佳,范磊磊,等.基于马尔科夫修正的中长期电力负荷组合区间预测[J].华东电力,2013,41(01):33-36.Zhang Hong,Zhu Yong-jia,Fan Lei-lei,et al.Mid-long term load interval forecasting based on Markov modification[J].East China Electric Power,2013,41(01):33-36.(in Chinese)
[15]方仍存,周建中.应用聚类算法和混沌理论的短期负荷概率性区间预测[J].电网技术,2010,36(11):65-69.Fang Reng-cun,Zhou Jian-zhong.Probabilistic interval forecasting of short-term load on the basis of clustering algorithm and chaos theory[J].Power System Technology,2010,36(11):65-69.(in Chinese)
[16]Khosravi A,Nahavandi S,Creighton D,et al.Comprehensive review of neural network-based prediction intervals and new advances[J].IEEE Transactions on Neural Networks,2011,22(9):1341-1356.
[17]Khosravi A,Nahavandi S,Creighton D,et al.Lower upper bound estimation method for construction of neural networkbased prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[18]Quan H,Srinivasan D,Khosravi A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(2):303-315.
[19]SI Yujing,LI Ta,PAN Jielin,YAN Yonghong.A prefix tree based n-best list re-scoring strategy for recurrent neural network language model[J].Chinese Journal of Electronics,2014,23(1):70-74.
[20]Chen P A,Chang L C,Chang F J.Reinforced recurrent neural networks for multi-step-ahead flood forecasts[J].Journal of Hydrology,2013,497:71-79.
[21]陈杰,沈艳霞,陆欣.基于信息反馈和改进适应度评价的人工蜂群算法[J].智能系统学报,2016,02:172-179.Chen Jie,Shen Yan-xia,Lu Xin.Artificial bee colony algorithm based on information feedback and an improved fitness value evaluation[J].CAAI Transactions on Intelligent Systems,2016,02:172-179.(in Chinese)
[22]周黎,周承恩,李海滨.寻求“理想”解的改进多目标粒子群优化算法[J].控制与决策,2015,30(09):1653-1659.Zhou Li,Zhou Cheng-en,Li Hai-bin.Improved multi-objective particle swarm optimization algorithm that can give“ideal”solution[J].Control and Decision,2015,30(09):1653-1659.(in Chinese)
[23]Chen J,Yang D.Constrained handling in multi-objective optimization based on quantum-behaved particle swarm optimization.[A].International Conference on Natural Computation ICNC 2010[C].Yantai,Shandong,China,2010.3887-3891.
[24]高海兵,高亮,周驰,等.基于粒子群优化的神经网络训练算法研究[J].电子学报,2004,32(09):1572-1574.Gao Hai-bing,Gao Liang,Zhou Chi,et al.Particle swarm optimization based algorithm for neural network learning[J].Acta Electronica Sinica,2004,32(09):1572-1574.(in Chinese)
[25]Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[26]Akbari R,Hedayatzadeh R,Ziarati K,et al.A multi-objective artificial bee colony algorithm[J].Swarm&Evolutionary Computation,2012,2(1):39-52.
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