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A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting
Neurocomputing ( IF 6 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.neucom.2019.09.110
Zhile Yang , Monjur Mourshed , Kailong Liu , Xinzhi Xu , Shengzhong Feng

Abstract Solar power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent power output due to the uncertain solar irradiance significantly challenges the economic integrations of solar generation within the existing power system, which calls for effective forecasting methods to improve the solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term solar power generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the solar power generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting.

中文翻译:

一种用于短期太阳能发电预测的新型竞争群优化 RBF 神经网络模型

摘要 太阳能是一种重要的可再生能源,是替代化石燃料发电机和减少碳排放的主要贡献者。然而,由于太阳辐照度不确定而导致的间歇性电力输出对现有电力系统中太阳能发电的经济整合提出了重大挑战,需要有效的预测方法来提高太阳能预测的准确性。在本文中,提出了一种新的改进径向基函数神经网络模型,并将其应用于短期太阳能发电预测。采用最近提出的称为竞争群优化的元启发式方法来训练径向基函数神经网络模型的非线性和线性参数。所提出的模型已在非线性基准函数中得到验证,然后用于预测荷兰实际案例研究的太阳能发电。数值结果表明,与其他同类模型相比,所提出的竞争群优化径向基函数神经网络模型可以获得更高的精度,从而为太阳能预测提供了有用的工具。
更新日期:2020-07-01
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