Artificial Intelligence and Meteorology Empowering Renewable Energy

The energy sector relies on accurate meteorological information for multiple uses. With the recent large deployments of renewable resources, high quality weather forecasts can make the integration of these variable renewable resources efficient and economic.

Weather prediction is an important real-time challenge. For renewable energy, it is important to have prediction of weather information on a real-time basis at a variety of timescales. Although physical models provide a basis for this prediction, their latency and their chaotic nature leave room for improvement. The best predictions are either based on artificial intelligence (AI) methods or post-processed using machine learning. AI is used to blend as much of the data, model output, and statistical learning as possible to improve the deterministic forecast and to quantify the uncertainty. Additional observations, when available, can be used in the post-processing step to improve the forecast. Training these post-processing methods requires large amounts of both model and observational data. The best methods blend computational intelligence with the physics and dynamics of the system. Such systems can be quite complex, and this is a problem in Big Data. Here we describe ways that AI is used in both wind and solar power forecasting system as an example of such a Big Data weather system.

Not only does meteorology enable renewable energy, but this renewable energy also helps equalize the various regions of world as most countries have access to these renewable resources. In additional, these initiatives lead to reduced emissions of criteria pollutants and green house gases.