Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2019-04-25 , DOI: 10.1016/j.jclepro.2019.04.331 Ling-Ling Li , Shi-Yu Wen , Ming-Lang Tseng , Cheng-Shan Wang
Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external factors and the output power is characterized by randomness and indirectness, which poses a great challenge to photovoltaic grid-connection. A hybrid improved multi-verse optimizer algorithm (HIMVO) is proposed to optimize the support vector machine for photovoltaic output prediction. HIMVO algorithm introduces chaotic sequences to initialize the population, which significantly enhances the convergence rate of the algorithm compared with the multi-universe optimizer algorithm. This study applied particle swarm optimization algorithm, dragonfly algorithm, multi-universe optimizer algorithm and HIMVO to testify the availability of the hybrid improved multi-verse optimizer support vector machine model (HIMVO-SVM). The results indicate that HIMVO algorithm has better optimization ability and stability. The four models, HIMVO-SVM, multi-verse optimizer support vector machine, particle swarm optimization support vector machine, back propagation and radical basis function neural network are used to predict output in three different weather types. The results indicate that the model has higher prediction accuracy and stability. The mean square error value of the HIMVO-SVM model decreases by at least 0.0026, 0.0030 and 0.0012, and the mean absolute percentage error value decreases by at least 3.6768%, 1.9772% and 2.7165%, respectively. The proposed method is beneficial to the prediction of output power and conduces to the economic dispatch of the grid and the maintenance of the stability of the power system.
中文翻译:
可再生能源预测:光伏输出功率的新型短期预测模型
随着可再生能源发电技术的成熟,光伏发电正逐渐发展成为大规模的电力工业。光伏发电受外部因素的影响很大,输出功率具有随机性和间接性,这对光伏并网提出了巨大的挑战。提出了一种混合改进的多宇宙优化器算法(HIMVO),以优化支持向量机的光伏输出预测。HIMVO算法引入混沌序列来初始化种群,与多宇宙优化器算法相比,显着提高了算法的收敛速度。本研究应用了粒子群优化算法,蜻蜓算法,多宇宙优化器算法和HIMVO来证明混合改进的多宇宙优化器支持向量机模型(HIMVO-SVM)的可用性。结果表明,HIMVO算法具有更好的优化能力和稳定性。使用HIMVO-SVM,多版本优化器支持向量机,粒子群优化支持向量机,反向传播和根基函数神经网络这四个模型来预测三种不同天气类型的输出。结果表明,该模型具有较高的预测精度和稳定性。HIMVO-SVM模型的均方误差值至少降低了0.0026、0.0030和0.0012,平均绝对百分比误差值分别降低了至少3.6768%,1.9772%和2.7165%。