Leveraging Our Energy Assets
Ambition: Develop collaborative initiatives that apply machine learning and artificial intelligence to address climate change and environmental challenges within the energy and power industries.
Digital innovations such as machine learning and artificial intelligence have the potential to address different challenges within the oil and gas and power industries. Machine learning can be applied in the analysis of historical data, energy production, energy consumption and gathered data in order to turn them into insights and predictive models. These technologies can help organizations turn quality data into insight and advanced analytics into foresight, resulting in more efficiency, money saving, better decision making, and a comprehensive understanding of issues overall.
- There is a need for “bridgers” – those are professionals who have expertise in the energy sector as well as a deeper understanding of machine learning applications. Digital literacy and skills are needed to bridge the gap between energy professionals and technology innovators.
- Machine learning requires big data. Data is not centralized and can be found within different entities within the power and oil and gas industries.
With the support of the RBC Foundation, the EFL has been exploring the intersection between artificial intelligence/machine learning, energy and climate since fall 2018. The EFL has launched the Energy.AI series to explore the challenges and opportunities, recruited Fellows with technology backgrounds to guide the EFL through this stream of work, and partnered with subject matter experts to help with “digital upskilling” and building the Lab’s capacity to eventually identify and advance initiatives. A timeline of workshops is provided below.
- Energy.AI Workshop (Sept 25, 2018). Learn about the outcomes here.
- Energy.AI2 Workshop (June 19, 2019)
- Energy.AI3 Accelerator (Oct 1, 2019). Learn about the outcomes here.