作者：叶興, 薛家祥

作者单位：华南理工大学机械与汽车工程学院, 广东 广州 510640

关键词：光伏发电;长短期记忆网络;深度学习;循环神经网络

摘要：

针对现有光伏发电功率预测技术存在的未能充分考虑气象因素、提取特征不充分等导致预测精度较低的问题，基于深度学习理论，提出一种基于改进型LSTM网络的光伏发电功率预测方法。根据长短期记忆神经网络的特点，从循环神经网络（RNN）推导出其一般计算过程，阐述该预测方法的优越性和可行性。提出基于改进型长短期记忆（LSTM）网络的光伏发电率预测模型，该模型充分考虑并优化神经网络带来的过拟合问题，且引入RMSProp算法获取模型最佳的损失函数值，确保得到最佳的预测结果。综合考虑对光伏发电功率产生影响的多种气象因素，并将气象因素做标准化处理后作为模型的初始输入量，在Spyder软件上对预测模型进行仿真验证。最后将上述模型与单一输入因素进行比较，结果显示充分考虑气象因素的预测结果明显优于单一因素，仿真结果证明该模型具有较好的预测精度。

Research on photovoltaic power generation prediction based on improved LSTM network
YE Xing, XUE Jiaxiang
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Abstract: In view of the fact that the existing photovoltaic power prediction technology fails to fully consider the meteorological factors and insufficient extraction characteristics, the prediction accuracy is low. Based on the deep learning theory, a photovoltaic power generation prediction based on the improved LSTM network method is proposed. According to the characteristics of long short-term memory neural networks, the general calculation process is derived from the recurrent neural networks, and the superiority and feasibility of the prediction method are expounded. A photovoltaic power generation rate prediction model based on improved long short-term memory (LSTM) network is proposed. The model fully considers the over-fitting problem caused by neural network optimization, and introduces RMSProp algorithm to obtain the optimal loss function value of the model to ensure the best prediction results. Considering a variety of meteorological factors that affect the power generation of photovoltaic power generation, and standardizing the meteorological factors as the initial input of the model, the prediction model is simulated and verified on the Spyder software. Finally, the above model is compared with a single input factor. The results show that the prediction result of meteorological factors is obviously better than the single factor. The simulation results show that the model has better prediction accuracy.
Keywords: photovoltaic power generation;long short-term memory neural networks;deep learning;recurrent neural networks
2019, 45(11):14-20  收稿日期: 2019-01-10;收到修改稿日期: 2019-03-11
基金项目: 福建省自然科学基金项目（2018J01541）；2015东莞市引进第三批创新科研团队项目（2017360004004）；广州市南沙区科技计划项目（2017CX009，2016CX010）；广东省交通厅科技项目（科技-2017-02-041）
作者简介: 叶興(1994-),男,福建宁德市人,硕士研究生,专业方向为仪器仪表工程
参考文献
[1] 冉晓洪, 苗世洪, 刘阳升, 等. 考虑风光荷联合作用下的电力系统经济调度建模[J]. 中国电机工程学报, 2014, 34(16):2552-2560
[2] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1):1-14
[3] 吴坚, 郑照红, 薛家祥. 深度置信网络光伏发电短时功率预测研究[J]. 中国测试, 2018, 44(5):6-11
[4] MUHAMMAD W A, MONJUR M, YACINE R. Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression[J]. Energy, 2018, 164:465-474
[5] DE G M G, CONGEDO P M, MALVONI M. Photovoltaic power forecasting using statistical methods:impact of weather data[J]. Iet Science Measurement & Technology, 2014, 8(3):90-97
[6] 单英浩, 付青, 耿炫, 等. 基于改进BP-SVM-ELM与粒子化SOM-LSF的微电网光伏发电组合预测方法[J]. 中国电机工程学报, 2016, 36(12):3334-3343
[7] 张雨金, 周杭霞. Stacking-SVM的短期光伏发电功率预测[J]. 中国计量大学学报, 2018, 29(2):121-127
[8] 张春露, 白艳萍. 基于TensorFlow的LSTM模型在太+原空气质量AQI指数预测中的应用[J]. 重庆理工大学学报(自然科学), 2018, 32(8):137-141
[9] 陈卓, 孙龙祥. 基于深度学习LSTM网络的短期电力负荷预测方法[J]. 电子技术, 2018, 47(1):39-41
[10] 赵淑芳, 董小雨. 基于改进的LSTM深度神经网络语音识别研究[J]. 郑州大学学报(工学版), 2018, 39(5):63-67
[11] SRIVASTAVA N, HINTONG G, KRIZHEVSKY A, et al. Dropout:A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1):1929-1958
[12] 张慧. 深度学习中优化算法的研究与改进[D]. 北京:北京邮电大学, 2018.