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摘要
光伏发电受天气与地理环境影响, 呈现出波动性和随机多干扰性, 其输出功率容易随着外界因素变化而变化, 因此预测发电输出功率对于优化光伏发电并网运行和减少不确定性的影响至关重要. 本文提出一种基于遗传算法(GA)优化的卷积长短记忆神经网络混合模型(GA-CNN-LSTM), 首先利用CNN模块对数据的空间特征提取, 再经过LSTM模块提取时间特征和附近隐藏状态向量, 同时通过GA优化LSTM训练网络的超参数权重与偏置值. 在初期对历史数据进行归一化处理, 以及对所有特征作灰色关联度分析, 提取重要特征降低数据计算复杂度, 然后对本文提出来的经GA优化后的CNN-LSTM混合神经网络(GA-CNN-LSTM)算法模型进行光伏功率预测实验. 同时与CNN, LSTM两个单一神经网络模型以及未经GA优化的CNN-LSTM混合神经网络模型的预测性能进行比较. 结果显示在平均绝对误差率(MAPE)指标下, 本文提出的GA-CNN-LSTM算法模型比单一神经网络模型最好的结果减少了1.537%的误差, 同时比未经优化的CNN-LSTM混合神经网络算法模型减少了0.873%的误差. 本文的算法模型对光伏发电功率具有更好的预测性能.-
关键词:
- 光伏发电 /
- 人工智能 /
- 卷积神经网络 /
- 长短记忆神经网络
Abstract
Photovoltaic power generation is affected by weather and geographical environment, showing fluctuations and random multi-interference, and its output power is easy to change with changes in external factors. Therefore, the prediction of output power is crucial to optimize the grid-connected operation of photovoltaic power generation and reduce the impact of uncertainty. This paper proposes a hybrid model of both convolutional neural network (CNN) and long short-term memory neural network (LSTM) based on genetic algorithm (GA) optimization (GA-CNN-LSTM). First, the CNN module is used to extract the spatial features of the data, and then the LSTM module is used to extract the temporal features and nearby hidden states. Optimizing the hyperparameter weights and bias values of the LSTM training network through GA. At the initial stage, the historical data is normalized, and all features were analyzed by grey relational degree. Important features are extracted to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network model (GA-CNN-LSTM) is applied for photovoltaic power prediction experiment. The GA-CNN-LSTM model was compared with the single neural network models such as CNN and LSTM, and the CNN-LSTM hybrid neural network model without GA optimization. Under the Mean Absolute Percentage Error index, the GA-CNN-LSTM algorithm proposed in this paper reduces the error by 1.537% compared with the ordinary single neural network model, and 0.873% compared with the unoptimized CNN-LSTM hybrid neural network algorithm model. From the perspective of training and test running time, the GA-CNN-LSTM model takes a little longer than the single neural network model, but the disadvantage is not obvious. To sum up, the performance of GA-CNN-LSTM model for photovoltaic power predicting is better.-
Keywords:
- photovoltaic /
- artificial intelligence /
- convolutional neural network /
- long short-term memory neural network
作者及机构信息
Authors and contacts
文章全文 : translate this paragraph
参考文献
[1] Wang K, Qi X, Liu H 2019 Appl. Energy 251 113315 Google Scholar
[2] Kushwaha V, Pindoriya N M 2019 RenewableEnergy 140 124 Google Scholar
[3] Shi J, Lee W J, Liu Y, Yang Y, Wang P 2012 IEEE Trans. Ind. Appl. 48 1064 Google Scholar
[4] Liu Y, Zhao J, Zhang M, et al. 2016 The 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Changsha, August, 2016 p29
[5] Liu L, Zhao Y, Chang D, Xie J, Ma Z, Sun Q, Wennersten R 2018 Appl. Energy 228 70
[6] Gao M, Li J, Hong F, Long D 2019 Energy 187 115
[7] Abdel-Nasser M, Mahmoud K 2019 Neural Comput. Appl. 31 2727 Google Scholar
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Wei X H 2019 M. S. Thesis (Lanzhou: Lanzhou University) (in Chinese)
[9] SrivastavaN, Hinton G, Krizhevsky A, et al. 2014 J. Mach. Learn Res. 15 1929
[10] https://www.pkbigdata.com/common/cmptIndex.html[2019-12-20]
[11] Ashburner J, Friston K J 1999 Hum. Brain Mapp. 7254
[12] Wei G W 2011 Expert Syst. Appl. 38 4824 Google Scholar
[13] Wang K, Qi X, Liu H 2019 Energy 189 116225 Google Scholar
[14] Chua L O 1997 Int. J. Bifurcation Chaos 7 2219 Google Scholar
[15] SajjadM, Khan S, Hussain T, Muhammad K, Sangaiah A K, Castiglione A, Baik S W 2019 Pattern Recognit. Lett. 126 123 Google Scholar
[16] Xiao F, Xiao Y, Cao Z, Gong K, Fang Z, Zhou J T 2019 Tenth International Conference on Graphics and Image Processing, Chengdu, China, 2018 p11069
[17] Hüsken M, Stagge P 2003 Neurocomputing 50 223 Google Scholar
[18] Qing X, Niu Y 2018 Energy 148 461 Google Scholar
[19] Ordóñez F, Roggen D 2016 Sensors 16 115 Google Scholar
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[21] Gensler A, Henze J, Sick B, et al. 2016 IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, October 9−12, 2016 p002858
[22] Zhou X, Wan X, Xiao J 2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Laguage Proccessing, Texas, USA, November 1−5, 2016 p247
[23] Goldberg D E, Samtani M P 1986 In Electronic Computation American Society of Civil Engineers, American, February 1986 p471
[24] Willmott C J, Matsuura K 2005 Clim. Res. 30 79 Google Scholar
[25] Ip W C, Hu B Q, Wong H, Xia J 2009 J. Hydrol. 379 284 Google Scholar
施引文献
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图 1 CNN-LSTM混合算法模型
Fig. 1. CNN-LSTM hybrid algorithm model.
图 2 一维卷积神经网络结构[ 14]
Fig. 2. One dimensional convolutional neural network structure.
图 3 LSTM神经网络结构[ 17]
Fig. 3. LSTM neural network structure.
图 4 遗传算法优化流程
Fig. 4. Optimization process of genetic algorithm.
图 5 CNN模型预测功率图
Fig. 5. Power diagram of CNN model prediction.
图 6 LSTM模型预测功率图
Fig. 6. Power diagram of LSTM model prediction.
图 7 CNN-LSTM模型预测功率图
Fig. 7. Power diagram of CNN-LSTM model prediction.
图 8 GA-CNN-LSTM模型预测功率图
Fig. 8. Power diagram of GA-CNN-LSTM model prediction.
表 1 灰色关联度分析值
Table 1. Grey relational analysis value.
变量特征 风速 风向 温度 压强 湿度 实发辐照度 Y 0.34 0.28 0.45 0.01 0.62 0.97 表 2 模型预测误差指标
Table 2. Error index of model prediction.
模型 CNN LSTM CNN-LSTM GA-CNN-LSTM MAE 0.34765 0.36681 0.28763 0.21424 MSE 0.65034 0.63447 0.60437 0.58529 RMSE 0.80643 0.77431 0.69321 0.61213 MAPE 0.06013 0.06233 0.05439 0.04476 表 3 模型运行时间
Table 3. Model running time.
模型 CNN LSTM CNN-LSTM GA-CNN-LSTM 训练时间/s 456.434 51.576 611.880 503.740 测试时间/s 1.130 1.220 3.690 2.770 PHP网站源码泰州网站优化按天扣费推荐马鞍山百度网站优化多少钱甘南至尊标王推荐海口网站搜索优化哪家好大连网站制作哪家好楚雄建网站推荐龙华网站制作设计鹰潭关键词排名民治网站搭建价格张家界网站改版推荐长沙网站关键词优化多少钱娄底网络营销公司通化优化多少钱濮阳外贸网站建设多少钱昆明百搜标王价格白山如何制作网站娄底建设网站诸城关键词按天计费昌吉关键词排名哪家好泉州网络推广价格大运seo排名推荐惠州百度网站优化价格延边企业网站制作多少钱漳州网站优化按天计费哪家好大鹏网站推广系统多少钱郴州关键词按天扣费多少钱常州网站设计模板贺州百度网站优化报价南京优秀网站设计推荐榆林网站优化排名报价歼20紧急升空逼退外机英媒称团队夜以继日筹划王妃复出草木蔓发 春山在望成都发生巨响 当地回应60岁老人炒菠菜未焯水致肾病恶化男子涉嫌走私被判11年却一天牢没坐劳斯莱斯右转逼停直行车网传落水者说“没让你救”系谣言广东通报13岁男孩性侵女童不予立案贵州小伙回应在美国卖三蹦子火了淀粉肠小王子日销售额涨超10倍有个姐真把千机伞做出来了近3万元金手镯仅含足金十克呼北高速交通事故已致14人死亡杨洋拄拐现身医院国产伟哥去年销售近13亿男子给前妻转账 现任妻子起诉要回新基金只募集到26元还是员工自购男孩疑遭霸凌 家长讨说法被踢出群充个话费竟沦为间接洗钱工具新的一天从800个哈欠开始单亲妈妈陷入热恋 14岁儿子报警#春分立蛋大挑战#中国投资客涌入日本东京买房两大学生合买彩票中奖一人不认账新加坡主帅:唯一目标击败中国队月嫂回应掌掴婴儿是在赶虫子19岁小伙救下5人后溺亡 多方发声清明节放假3天调休1天张家界的山上“长”满了韩国人?开封王婆为何火了主播靠辱骂母亲走红被批捕封号代拍被何赛飞拿着魔杖追着打阿根廷将发行1万与2万面值的纸币库克现身上海为江西彩礼“减负”的“试婚人”因自嘲式简历走红的教授更新简介殡仪馆花卉高于市场价3倍还重复用网友称在豆瓣酱里吃出老鼠头315晚会后胖东来又人满为患了网友建议重庆地铁不准乘客携带菜筐特朗普谈“凯特王妃P图照”罗斯否认插足凯特王妃婚姻青海通报栏杆断裂小学生跌落住进ICU恒大被罚41.75亿到底怎么缴湖南一县政协主席疑涉刑案被控制茶百道就改标签日期致歉王树国3次鞠躬告别西交大师生张立群任西安交通大学校长杨倩无缘巴黎奥运
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[1] Wang K, Qi X, Liu H 2019 Appl. Energy 251 113315 Google Scholar
[2] Kushwaha V, Pindoriya N M 2019 RenewableEnergy 140 124 Google Scholar
[3] Shi J, Lee W J, Liu Y, Yang Y, Wang P 2012 IEEE Trans. Ind. Appl. 48 1064 Google Scholar
[4] Liu Y, Zhao J, Zhang M, et al. 2016 The 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Changsha, August, 2016 p29
[5] Liu L, Zhao Y, Chang D, Xie J, Ma Z, Sun Q, Wennersten R 2018 Appl. Energy 228 70
[6] Gao M, Li J, Hong F, Long D 2019 Energy 187 115
[7] Abdel-Nasser M, Mahmoud K 2019 Neural Comput. Appl. 31 2727 Google Scholar
[8] 魏小辉 2019 硕士学位论文 (兰州: 兰州大学)
Wei X H 2019 M. S. Thesis (Lanzhou: Lanzhou University) (in Chinese)
[9] SrivastavaN, Hinton G, Krizhevsky A, et al. 2014 J. Mach. Learn Res. 15 1929
[10] https://www.pkbigdata.com/common/cmptIndex.html[2019-12-20]
[11] Ashburner J, Friston K J 1999 Hum. Brain Mapp. 7254
[12] Wei G W 2011 Expert Syst. Appl. 38 4824 Google Scholar
[13] Wang K, Qi X, Liu H 2019 Energy 189 116225 Google Scholar
[14] Chua L O 1997 Int. J. Bifurcation Chaos 7 2219 Google Scholar
[15] SajjadM, Khan S, Hussain T, Muhammad K, Sangaiah A K, Castiglione A, Baik S W 2019 Pattern Recognit. Lett. 126 123 Google Scholar
[16] Xiao F, Xiao Y, Cao Z, Gong K, Fang Z, Zhou J T 2019 Tenth International Conference on Graphics and Image Processing, Chengdu, China, 2018 p11069
[17] Hüsken M, Stagge P 2003 Neurocomputing 50 223 Google Scholar
[18] Qing X, Niu Y 2018 Energy 148 461 Google Scholar
[19] Ordóñez F, Roggen D 2016 Sensors 16 115 Google Scholar
[20] Tan Z X, Goel A, Nguyen T S, Ong D C 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition, Lille, France, May 14−18, 2019 p1
[21] Gensler A, Henze J, Sick B, et al. 2016 IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, October 9−12, 2016 p002858
[22] Zhou X, Wan X, Xiao J 2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Laguage Proccessing, Texas, USA, November 1−5, 2016 p247
[23] Goldberg D E, Samtani M P 1986 In Electronic Computation American Society of Civil Engineers, American, February 1986 p471
[24] Willmott C J, Matsuura K 2005 Clim. Res. 30 79 Google Scholar
[25] Ip W C, Hu B Q, Wong H, Xia J 2009 J. Hydrol. 379 284 Google Scholar
目录
- 第69卷,第10期 - 2020年05月20日
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