An Improved Metaheuristic Method-Based Neural Network for Predicting Wind Turbine Power

Ahilan, T.; Yoganand, S.; Prasad, D. V. S. S. S. V.

doi:10.1080/01969722.2022.2145663

Cited by 3 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system configuration is illustrated in Table 1. The proposed method is compared with conventional models like RNN, SOA+RNN, CSO+RNN, SSA+DELM, 40 CFU‐COA, 41 and GWO+RNN. A series of tests are run on the suggested solution, taking into account the historical data, based on these data as well as the wind record characteristics on the observed days.…”

Section: Resultsmentioning

confidence: 99%

Forecasting wind power using Optimized Recurrent Neural Network strategy with time‐series data

Kumar,

Prabhakar,

Verma

2024

Optim Control Appl Methods

View full text Add to dashboard Cite

Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization‐based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU‐COA, and GWO+RNN method.

show abstract

Section: Resultsmentioning

confidence: 99%