Published Paper Details:

Deep learning is one of the Artificial Intelligence methods that work like a human brain to store record and process the data. Machine learning(ML) algorithms have capacity to learn things that are required for particular application. Hence. They have the ability to learn. Investigate and assess the potential of a novel mechanism to boost the effectiveness of intermittent wind energy. It includes the preprocessing of the dataset to eliminate the null values, increase the Wind Power Forecasting (WPF) effectiveness by using Artificial Neural Network (ANN) algorithm. Graph utilize the ANN algorithm that shows the accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) percentages. The proposed system improves the efficiency of short-term WPF. According to experimental results the proposed ANN algorithm has less MAE and RMSE percentage that is 70.01% and 77% less than existing system of short-term WPF. Also, ANN is 70% faster than Support Vector Machine (SVM) and accuracy of ANN is improved by 14% than existing system (SVM), from the experimental evaluations the proposed algorithm performs better in terms of improved accuracy of 14% compared to the existing system, MAE of 70.01% and RMSE of 77%.