Signal 5: Using AI to forecast and make decisions.
Renewable forms of electricity are emerging as the successors to traditional coal and gas-fired power plants. A key problem with renewable electricity, however, is its unreliability. A cloudy day or a string of calm will cut generation and can create power shortfalls. Conversely, too much energy can be generated.
Using AI to create forecasts for weather and electricity generation can lessen the need for these backup mechanisms, by predicting and managing fluctuations in production. Industry and weather data is used to train AI algorithms to make accurate forecasts, helping to inform power supply and demand.
Controlling the power plant by an AI algorithm might seem difficult to implement. However, it is not. AI has already being used to manage energy production [1].
In Colorado, energy provider Xcel is using AI to make weather-dependent power sources more reliable. Their AI studies a combination of data from satellite reports, weather stations as well as wind farms in the surrounding area. Then the AI can make predictions about how much energy will be produced based on those data sets.
Nnergix is a data mining and web-based energy forecasting platform which pools data from the energy industry.
To expand the scope of the application of this kind of technology. Regulators should at least share their satellite and weather data with companies like Nnergix and Xcel.
[1] Walker, J. (2017, October 08). AI for Weather Forecasting — In Retail, Agriculture, Disaster Prediction. Retrieved from https://www.techemergence.com/ai-for-weather-forecasting/
