A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Abstract

Abstract:The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

“…In contrast to our method, Xu et al [6] developed a hybrid approach consisting of a composite Particle Swarm Optimization and Tabu Search combined with an echo state network technique for ultrashort-term and short-term wind power forecasting systems, while our developed hybrid method uses long short-term memory neural networks to adjust the accuracy of the meteorological parameters at the level of each and every wind turbine, subsequently employing a function fitting neural network to forecast both the produced and consumed electricity of the wind farm's production groups in order to surpass the challenges posed by the complex hilly terrain. Regarding the time interval over which the forecasting is conducted, Xu et al [6] address ultrashort-term and short-term wind power forecasting, while our research targets short-term and medium-term predictions.…”

“…Xu et al [6] proposed an echo state artificial neural network (ESN) approach fused with the particle swarm optimization (PSO) and metaheuristic Tabu search algorithms with the aim of improving the prediction accuracy of wind power forecasting by overcoming the drawbacks of the standard ESN. Xu et al proposed this method for ultrashort-term or short-term forecasting horizons for the purpose of enhancing the electricity generation plan, diminishing the abandonment of wind power sites due to lack of appropriate forecasting tools, making appropriate adjustments to the maintenance schedule, and for facilitating the development of monitoring systems that operate in real time.…”

Selection of a Critical Time Scale of Real-Time Dispatching for Power Systems With High Proportion Renewable Power Sources

“…The power prediction error and prediction lead time of URESs follow the law of probability and statistics [33]. The longer the prediction lead time, the larger the prediction error; the shorter the prediction lead time, the smaller the prediction error.…”

A state-of-the-art review on wind power deterministic prediction

“…Artificial neural network is widely used in wind power prediction. The types of these neural networks include back propagation neural network (Hu and Zhang, 2018; Li et al, 2020b; Wang et al, 2015a), radial basis function neural network (Chang, 2013; Ter Borg and Rothkrantz, 2006; Zhang et al, 2016), echo state network (Dorado-Moreno et al, 2017; Gouveia et al, 2018; Wang et al, 2019a; Xu et al, 2015), and extreme learning machine (Mahmoud et al, 2018ba, 2018b; Mohammadi et al, 2015). The artificial neural network has the characteristics of self-adaptive and self-learning, which can deal with complex systems, but it has the problems of slow training speed, difficult to determine the network structure and parameters, and easy to fall into local optimum.…”