A Deep Learning-Based Convolution Neural Networks to Forecast Wind Energy

The demand for power is increasing daily due to an increase in industrial and household energy requirements and consumption. The non-renewable energy generation is considered to be dirty power as it requires oil, gas, coal and reactive substance like uranium for power generation. The non-renewable power generation pollutes the environment and is considered to be more harmful to people making the government and researchers to concentrate on the renewable method of power generation. Wind energy can be harvested round the clock as the wind will be available all time than the availability of sunlight which controls solar power. This paper proposed a novel deep learning-based convolution neural networks (CNN) for forecasting wind power generation one day ahead. The forecasting efficiency is compared with the benchmarking methodologies of Machine learning algorithms and the Root Mean Square Error (RMSE) of the proposed method is determined to be 0.629 which is considered to be efficient for the wind power generation process.