PDF(Pubmed)
Abstract：
Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply-demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach\'s performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454-1.54] for the three studied PV stations.
摘要：
将可再生能源集成到当前的发电系统中需要准确的预测以优化和保持电网中的供需限制。由于环境条件的高度随机性，光伏功率的准确预测具有局限性，特别是在长期和短期。因此，这项研究提供了一种新的混合模型，用于基于不同分解技术的多频率信息的融合来预测短光伏功率，这将使预报员能够提供可靠的预测。我们评估并提供了五种多尺度分解算法与深度卷积神经网络（CNN）相结合的性能。此外,我们将建议的组合方法与现有预测模型的性能进行了比较。使用阿尔及利亚的三个并网光伏发电厂进行了详尽的评估，总装机容量为73.1兆瓦。开发的融合策略显示出出色的预测性能。将所提出的组合方法与单机预测模型和其他混合技术进行比较分析,证明了其在预测精度方面的优越性,三个研究的光伏电站的RMSE在[0.454-1.54]范围内变化。
