INTRODUCTION
In the coming years, the world will watch as AI, machine learning, and data science transform the economy and our day-to-day lives. Perhaps no AI-enabled change has greater implications for mankind, however, than the reshaping of the energy sector. The energy sector is generally regarded as conservative in mindset and therefore slow to adopt digital technology. After all, much of the technology that powers modern life –– coal, oil, the electrical grid –– has remained largely unchanged since the late 19th century. Yet a 2017 McKinsey report classified resources and utilities as in the middle of the pack in terms of digitization, above retail, education, and health care but behind financial services, automotive and manufacturing, and, of course, the technology sector.
However, the last few years have seen significant technological changes in the energy economy:
- Oil and natural gas prices are low because of pioneering technology that allows companies to affordably access resources that were previously considered uneconomical.
- Electric utilities are using machine learning to better understand their customers and deploy their resources more efficiently, cutting costs for the utility and consumers alike.
- Meanwhile, the prospect of a renewable revolution becomes more realistic by the day largely because of major advances in AI that help generators maximize the impact of the sunshine and wind they are harnessing.
The drive to make utilities more efficient through AI, machine learning, and data science has resulted in major benefits for every actor in the energy sector, including generators, distributors, the environment, taxpayers, and consumers. There is still much to do, however, resource and utility companies that hope to remain competitive in the coming years should be aggressively pursuing the next technological frontier. This white paper will cover some of the highest-value use cases in the utilities and energy industries as well as suggested paths to scale up AI competencies within these organizations.
AI is most certainly going to play a major role in boosting renewable energy. While renewable sources, notably solar and wind power, are on the rise, they are still not capable of being the dominant energy sources due to their intermittent nature. While there have been promising advances in battery storage technology that allows utilities to store power generated from intermittent sources and dispatch it when needed, the devices remain too expensive for widespread adoption. That explains why, despite investing heavily in renewable projects, China also continues to build new coal plants at breakneck speed. And while renewables have surpassed coal in the United States, they still lag far behind natural gas, which is both cheap and dispatchable.
Data is the key to helping utilities make the most of available renewable sources. For example, AI-powered predictive analytics based on historical data help utilities forecast the weather with a precision that would have been unfathomable until recently.
Being able to predict how much sun or wind will be available two hours from now allows utilities to determine how much generated energy they should store. Predictive analytics will also help utilities optimize the search for wind or solar-generating properties so that they can know exactly how much power a given parcel of land is expected to produce. Money is pouring into ventures aimed at making renewable power more cost-effective through digital technology and AI. In fact, conventional energy giants, such as ExxonMobil, Southern Company, and Tokyo Gas are investing in renewables in anticipation of a greener energy economy in the coming decades.
In Texas, the longtime heart of America’s oil and gas economy is also the source of much of the country’s emerging wind sector; ExxonMobil is powering its oil operations in the Permian Basin with wind energy.(Indrawan Vpp)
