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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.
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Keywords:
- photovoltaic /
- artificial intelligence /
- convolutional neural network /
- long short-term memory neural network
[1] Wang K, Qi X, Liu H 2019 Appl. Energy 251 113315
Google Scholar
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Google Scholar
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Google Scholar
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[5] Liu L, Zhao Y, Chang D, Xie J, Ma Z, Sun Q, Wennersten R 2018 Appl. Energy 228 70
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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)
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[10] https://www.pkbigdata.com/common/cmptIndex.html[2019-12-20]
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Google Scholar
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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
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Google Scholar
[25] Ip W C, Hu B Q, Wong H, Xia J 2009 J. Hydrol. 379 284
Google Scholar
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图 1 CNN-LSTM混合算法模型
Figure 1. CNN-LSTM hybrid algorithm model.
图 2 一维卷积神经网络结构[14]
Figure 2. One dimensional convolutional neural network structure.
图 3 LSTM神经网络结构[17]
Figure 3. LSTM neural network structure.
图 4 遗传算法优化流程
Figure 4. Optimization process of genetic algorithm.
图 5 CNN模型预测功率图
Figure 5. Power diagram of CNN model prediction.
图 6 LSTM模型预测功率图
Figure 6. Power diagram of LSTM model prediction.
图 7 CNN-LSTM模型预测功率图
Figure 7. Power diagram of CNN-LSTM model prediction.
图 8 GA-CNN-LSTM模型预测功率图
Figure 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 
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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
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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
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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
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