An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition

Abstract

Accurate ultra-short-term wind speed prediction is extremely important for the power control of wind farms, the safe dispatch of power systems, and the stable operation of power grids. At present, most wind farms mainly rely on supervisory control and data acquisition systems to obtain operation and maintenance data which includes operating characteristics of wind turbines. In the ultra-short-term wind speed prediction, a long short-term memory network is one of the commonly used deep learning methods. To address the problem that improper selection of long short-term memory network's hyperparameters may affect the prediction results, In the present study, a hybrid prediction model based on the long short-term memory and the modified tuna swarm optimization algorithm was established, and was used to predict after the wind speed sample data had been decomposed by successive variational mode decomposition method. The experimental results reveal that the proposed model effectively improved the accuracy of wind speed prediction for wind farms compared with the support vector regression, deep belief networks, and long short-term memory models optimized by particle swarm optimization algorithm.

Vertical wind speed extrapolation model using long short‐term memory and particle swarm optimization

“…The proposed procedure is compared with other optimization methods like tuna swarm optimization (TSO), 23,24 grey wolf optimization (GWO), 25 sparrow search algorithm (SSA), 26 and bald eagle search (BES) 27 from modern techniques and genetic algorithm (GA) 7 from classical one. The statistical numerical experimental results are summarized in Table 2 where minimum and maximum values are shown in bold and italic font, respectively.…”

An overview of deterministic and probabilistic forecasting methods of wind energy

“…For example, Tuerxun et al. (2022) 315 developed the modified tuna swarm optimization algorithm to achieve ultra-short-term wind speed prediction. Wei et al.…”

Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter

“…Tese techniques are used to monitor the MPP of PV systems in order to optimize their performance. Some of the bioinspired algorithms such as "particle swarm optimization (PSO)" [9], "frefy algorithm (FA)" [10], "marine predator algorithm (MPA)" [11], "mayfy algorithm (MF)" [12], "jellyfsh optimization (JS)" [13], grey wolf optimization (GWO) [14], artifcial bee colony (ABC) algorithm [15], and ant colony optimization (ACO) [16] are used efectively in the deployment of MPP search strategies and the trajectory tracking control. Particle swarm optimization (PSO) is a computational approach for solving problems by iteratively improving a proposed solution.…”