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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
[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
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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
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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 玻璃钢生产厂家新余玻璃钢家具批发汉中玻璃钢装饰造型厂家直销淮南玻璃钢种植池多少钱辽宁玻璃钢坐凳价格晋城玻璃钢动物雕塑公司武汉玻璃钢花钵制作广元玻璃钢浮雕制作孝感玻璃钢产品哪家好吕梁玻璃钢产品定制南阳玻璃钢外壳价格金华玻璃钢花盆批发宁德玻璃钢餐桌椅多少钱佛山玻璃钢外壳批发怀化不锈钢花盆哪家好盘锦玻璃钢景观雕塑价格台湾玻璃钢座椅公司郴州玻璃钢家具价格安顺商业美陈多少钱中卫玻璃钢花池厂家郑州玻璃钢摆件制作梧州玻璃钢卡通雕塑大庆玻璃钢雕塑定做临沂玻璃钢装饰造型宜昌玻璃钢装饰工程厂娄底玻璃钢机械外壳制作泉州玻璃钢设备外壳定做衡水玻璃钢前台定做衡水玻璃钢装饰造型制作吉安玻璃钢花钵南宁玻璃钢树池坐凳厂家直销香港通过《维护国家安全条例》两大学生合买彩票中奖一人不认账让美丽中国“从细节出发”19岁小伙救下5人后溺亡 多方发声卫健委通报少年有偿捐血浆16次猝死汪小菲曝离婚始末何赛飞追着代拍打雅江山火三名扑火人员牺牲系谣言男子被猫抓伤后确诊“猫抓病”周杰伦一审败诉网易中国拥有亿元资产的家庭达13.3万户315晚会后胖东来又人满为患了高校汽车撞人致3死16伤 司机系学生张家界的山上“长”满了韩国人?张立群任西安交通大学校长手机成瘾是影响睡眠质量重要因素网友洛杉矶偶遇贾玲“重生之我在北大当嫡校长”单亲妈妈陷入热恋 14岁儿子报警倪萍分享减重40斤方法杨倩无缘巴黎奥运考生莫言也上北大硕士复试名单了许家印被限制高消费奥巴马现身唐宁街 黑色着装引猜测专访95后高颜值猪保姆男孩8年未见母亲被告知被遗忘七年后宇文玥被薅头发捞上岸郑州一火锅店爆改成麻辣烫店西双版纳热带植物园回应蜉蝣大爆发沉迷短剧的人就像掉进了杀猪盘当地回应沈阳致3死车祸车主疑毒驾开除党籍5年后 原水城县长再被查凯特王妃现身!外出购物视频曝光初中生遭15人围殴自卫刺伤3人判无罪事业单位女子向同事水杯投不明物质男子被流浪猫绊倒 投喂者赔24万外国人感慨凌晨的中国很安全路边卖淀粉肠阿姨主动出示声明书胖东来员工每周单休无小长假王树国卸任西安交大校长 师生送别小米汽车超级工厂正式揭幕黑马情侣提车了妈妈回应孩子在校撞护栏坠楼校方回应护栏损坏小学生课间坠楼房客欠租失踪 房东直发愁专家建议不必谈骨泥色变老人退休金被冒领16年 金额超20万西藏招商引资投资者子女可当地高考特朗普无法缴纳4.54亿美元罚金浙江一高校内汽车冲撞行人 多人受伤
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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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