The world is increasing its share of renewable energy supplies, and it is important to guarantee that these clean energy sources offer a stable supply.
FREMONT, CA: Today, the world demands collaborating to achieve clean energy solutions for fighting the global climate crisis. These sustainable development goals adopted by firms aim to ensure access to affordable, reliable, sustainable, and modern clean energy for all. Clean power comes from renewable resources supplied by nature. Their use range from power generation both on a large scale and off-grid to heating/cooling systems and transport. However, renewable sources depend on the weather and are more volatile than conventional sources. As the world increases its share of renewable energy supplies, it is essential to guarantee that these clean energy sources offer a stable supply while replacing fossil fuel based power.
Together with solar, wind power is one of the prominent renewable power sources, providing 4.8 percent of the electricity supply and being responsible for 15 percent of the world's electricity. Wind power is generated by the mechanical power of wind on turbines that create electricity. Because wind has different intensities over time and may stop blowing intermittently, this source's power is combined with other energy sources to enhance reliability and stability.
Energy trade firms play an essential role in evaluating the risk of a shortfall in energy transactions by assisting in predicting the expected power production, especially in the wind, as a non-steady energy source. Energy traders forecast the production of energy on behalf of the power producers, considering various scenarios. Wind energy is dependent on environmental factors like wind speed. Energy traders need to predict wind energy production to maximize profits. By applying deep learning to financial risk, companies aim to make a wind energy forecast model. The goal is to deploy a model that streamlines profits for wind farms, reducing excess shortfalls of energy production. Deep learning for knowing time series has a high potential for affecting many other fields, like identifying diseases spread over time. In particular, in the renewable energies sector, it can also be leveraged for forecasting demand and consumption of energy.