DEGGENDORF, GERMANY - Human activities are changing Earth's climate. By making more accurate predictions and using resources more efficiently, artificial intelligence (AI) has the potential to help reduce climate change. In contrast, the training and use of large deep learning (DL) models is not energy-efficient, and has started to contribute adversely to climate change too.

Climate change is one of the greatest challenges facing humanity in the 21st century. All nations must make extreme efforts to reduce emissions to achieve the 2-degree target for global temperature rises set in the Paris Agreement in December 2015. In addition to converting energy supplies to renewable sources of energy, technical innovations also play a major role in reducing and avoiding emissions.

To achieve these goals, great hopes are being placed in AI, as it has great potential to make all kinds of processes both more efficient and energy-saving. AI looks promising in its ability to make more accurate predictions and to accelerate the advancement and widespread adoption of new technologies like nuclear fusion. Some 80 percent of all world-wide emissions are energy-related, and thus there is enormous unexploited potential, especially in the energy industry, for saving energy with the help of AI.

AI Apps’ Efficient Resource Use

For example, AI can help find optimal locations for renewable energies, make wind power plants more efficient through accurate weather forecasts, and detect potential problems in smart grids before they occur. With future-oriented topics such as autonomous driving, AI is already present today, especially in the mobility sector. In addition, there are several other use cases in which AI is already being successfully deployed, such as the coordination of groupage freight, dynamic pricing, intelligent route finding, and automated traffic c