Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

Abstract

Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.

Short-term wind power forecasting method based on spatial-temporal graph neural network

“…Zhou et al [7] proposed an improved method of ultra-short interval for regional wind energy forecasting based on nonlinear quantum regression with complex conditions, which considered weighting multiple time series, obtained sample weights by hierarchical clustering method, and completed the calculation of coverage probability, average width and outlier offset according to static differences and dynamic and meteorological differences in time series, thus completing short-term wind power forecasting. Nachimuthu et al [8] proposed multi-stage wind speed and wind speed prediction based on variable factors and intelligent deep learning neural network model. The neural network model of deep learning is constructed to predict the multi-stage wind speed IOP Publishing doi:10.1088/1742-6596/2797/1/012055 2 and wind energy of wind power plants.…”