【论文推荐】山东大学史月涛等：基于K-means++，最优相似日算法和LSTM神经网络的光伏功率短期预测的混合模型

摘要

光伏发电由于其清洁无污染特性，近年来得到快速发展。然而由于其随机性、间歇性和波动性的特点，大规模光伏电站并入电网可能会影响电网系统的稳定性。因此，对光伏发电功率进行精确预测，保障电力网系统的稳定性的同时，也协助电力部门制定调度计划保证电站的经济运行。基于深度学习的发展及其在能源领域的良好表现，本文提出了一种结合改进的K-Means聚类算法、相似日和LSTM神经网络的短期光伏功率混合预测模型。该混合模型使用历史功率和气象因子数据对第二日的光伏发电功率进行预测。首先使用K-means++聚类算法根据不同的特征矩阵输入探究最优聚类结果，结合气象信息确定各天气类别。然后基于不同寻找相似日的方法的对比，确定GRA和Cosine算法相结合的方法来寻找预报日的最佳相似日；最后采用具有选择性记忆功能的LSTM神经网络对模型训练和测试。将预测结果与其他神经网络模型或单一模型的预测结果进行对比。RMSE、MAPE和MAD三个性能评价指标的值表明，所提出的混合预测模型在预测精度和稳定性方面都表现出了优越的性能。

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

基于K-means++，最优相似日算法和LSTM神经网络的光伏功率短期预测的混合模型

Ruxue Bai¹, Yuetao Shi¹, Meng Yue¹, Xiaonan Du¹

1. Shandong Engineering Laboratory for High-efficiency Energy Conservation and Energy Storage Technology & Equipment, School of Energy and Power Engineering, Shandong University, Jinan Shandong 250061, P. R. China

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Hybrid model based on K-means algorithm,optimal similar day approach,and long short-term memory neural network for short-term photovoltaic power

Abstract

Photovoltaic (PV) power generation is characterized by randomness and intermittency due to weather changes. Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model (i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach, and long short-term memory (LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error (RMSE), normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE (hybrid model combining similar day approach and Elman), HSL (hybrid model combining similar day approach and LSTM), and HKSE (hybrid model combining K-means++, similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

Keywords

PV power prediction; hybrid model; K-means++; optimal similar day; LSTM.

Fig. 1 PV power curves under different weather types

Fig. 2 Determination process of weather type of forecast day

Fig. 3 Information transmission structure diagram of LSTM

Fig. 4 Framework of proposed hybrid model

Fig. 5 Power curves for three weather types

Fig. 6 Predicted and actual output curves for five days

Fig. 7 Performance evaluation indices of different models

Fig. 8 Predicted results and errors of six models for sunny day

Fig. 9 Predicted results and errors of six models for cloudy day

Fig. 10 Predicted results and errors of six models for rainy day

本文引文信息

Bai RX, Shi YT, Yue M, et al (2023) Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction. Global Energy Interconnection, 6(2): 184-196

白如雪，史月涛，岳萌等 (2023) 基于K-means++，最优相似日算法和LSTM神经网络的光伏功率短期预测的混合模型. 全球能源互联网（英文）, 6(2): 184-196

Biographies

Ruxue Bai

Ruxue Bai received her bachelor’s degree from Harbin Business University, Harbin, in 2016. She is studying for a master’s degree at Shandong University, Shandong, China. At present, her research direction is photovoltaic power prediction model based on neural network.

Yuetao Shi

Yuetao Shi received his bachelor’s degree from Shandong University of Technology, Shandong, China, in 1997, the master’s degree in engineering from Shandong University, Shandong, China, in 2000, and the Ph.D. degree from Xi’an Jiaotong University, Xian, China, in 2009. At present, he is a professor of Shandong University. His research interests include data mining and analysis in industrial processes and comprehensive energy load forecasting based on machine learning.

Meng Yue

Meng Yue received his bachelor’s degree from Shenyang Architecure University, Shenyang, in 2018, the master’s degree at Shandong University, Shandong, China, in 2021. Now he is the office director of Harbin Shuangcheng District National Thermal Power Plant, mainly responsible for procurement and personnel management.

Xiaonan Du

Xiaonan Du graduated from Shandong University of Science and Technology in 2017 with a bachelor's degree. She is studying for a master's degree at Shandong University, China. At present, her research direction is the performance analysis and energysaving optimization of the coupled compressed air energy storage system in thermal power plants.

编辑：刘通明

审核：王伟

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