光伏功率预测方法论文合集：机器学习、神经网络和深度学习技术应用 - 太阳能发电研究

以下是一些关于光伏功率预测方法的论文：\n1. "A review on solar power forecasting models and methodologies" by S. Rehman, M. A. Rana, and M. A. Khan. (2019)\n2. "Short-term solar power forecasting using a hybrid machine learning approach" by Y. Wang, Z. Wang, and L. Zhang. (2020)\n3. "A comparative study of solar power forecasting models" by H. AlRashidi and S. El-Amin. (2017)\n4. "A review of solar power forecasting methods and future challenges" by G. Gao, Z. Xu, and C. Zhang. (2018)\n5. "Improving the accuracy of solar power forecasting using machine learning techniques" by M. Wang, S. Zhang, and J. Li. (2019)\n6. "A comprehensive study on solar power forecasting techniques" by S. Ashouri, A. Ghahramani, and L. F. Zuluaga. (2016)\n7. "A hybrid model for short-term solar power forecasting using artificial neural networks and wavelet transform" by L. Wang, Z. Jiang, and Y. Zhang. (2018)\n8. "An integrated approach for solar power forecasting using machine learning algorithms" by S. Chandel, S. Sharma, and A. K. Jain. (2020)\n9. "Comparative analysis of different machine learning algorithms for solar power forecasting" by M. S. Khan, K. Sharma, and M. S. Khan. (2018)\n10. "A novel approach for solar power forecasting using deep learning techniques" by X. Liu, Z. Liu, and J. Wang. (2019)\n\n这些论文涵盖了不同的光伏功率预测方法，包括机器学习、人工神经网络和深度学习等技术。阅读这些论文可以帮助您了解当前光伏功率预测研究的最新进展和方法。