A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting

• Published in: Energy conversion and management Vol. 136; pp. 439 - 451
• Main Authors: Zhang, Wenyu, Qu, Zongxi, Zhang, Kequan, Mao, Wenqian, Ma, Yining, Fan, Xu
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.03.2017; Elsevier Science Ltd
• Subjects: Adaptive algorithms; Algorithms; Coastal zone; Combined model; Complete ensemble empirical mode decomposition adaptive noise; Data processing; Decomposition; Electric power grids; Forecasting; Mathematical models; Neural networks; Optimization; Pollination; Preprocessing; Security; Stochasticity; Studies; Weight coefficient optimization; Wind farms; Wind power; Wind power generation; Wind speed; Wind speed prediction; artificial neural network; average of absolute error; genetic simulated annealing algorithm; flower pollination algorithm with chaotic local search; feed-forward neural network; Combined model; average absolute forecast error of n times forecast results; Elman neural network; genetic algorithm; chaotic particle swarm optimization; no negative constraint theory; auto regressive moving average; root mean-square forecast error; complete ensemble empirical mode decomposition adaptive noise; back propagation; intrinsic mode function; particle swarm optimization; China National Renewable Energy Center; Diebolde-Mariano (DM) test; wavelet neural network; differential evolution; radial basis function; support vector machine; Wind speed prediction; autoregressive integrated moving average; general regression neural network; Weight coefficient optimization; square sum of the error
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2017.01.022

[Display omitted] •A CEEMDAN-CLSFPA combined model is proposed for short-term wind speed forecasting.•The CEEMDAN technique is used to decompose the original wind speed series.•A modified optimization algorithm-CLSFPA is proposed to optimize the weights of the combined model.•The no negative constraint theory is applied to the combined model.•Robustness of the proposed model is validated by data sampled from four different wind farms. Wind energy, which is stochastic and intermittent by nature, has a significant influence on power system operation, power grid security and market economics. Precise and reliable wind speed prediction is vital for wind farm planning and operational planning for power grids. To improve wind speed forecasting accuracy, a large number of forecasting approaches have been proposed; however, these models typically do not account for the importance of data preprocessing and are limited by the use of individual models. In this paper, a novel combined model – combining complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), flower pollination algorithm with chaotic local search (CLSFPA), five neural networks and no negative constraint theory (NNCT) – is proposed for short-term wind speed forecasting. First, a recent CEEMDAN is employed to divide the original wind speed data into a finite set of IMF components, and then a combined model, based on NNCT, is proposed for forecasting each decomposition signal. To improve the forecasting capacity of the combined model, a modified flower pollination algorithm (FPA) with chaotic local search (CLS) is proposed and employed to determine the optimal weight coefficients of the combined model, and the final prediction values were obtained by reconstructing the refined series. To evaluate the forecasting ability of the proposed combined model, 15-min wind speed data from four wind farms in the eastern coastal areas of China are used. The experimental results of this study show that: (a) the proposed CEEMDAN-combined model can take advantages of individual models and has the best performance among single models and the benchmark model; (b) the proposed CLSFPA is superior to FPA according to test functions and is effectively applied in optimizing the combined model; (c) the proposed algorithms are effective in high-precision wind speed predictions.