Climate Change presents a global problem for our generation and the generations that will follow us. The Earth’s abundance has limits and Mother Nature will bend and stretch to provide for her inhabitants, but it may come at a cost. As our society presents advancement after advancement in technology, medicine, and industry, we can harness these investments to reduce the reflexive impact on the environment. At DataRobot, our engineers and data scientists are thinking about how to harness the power of AI and ML to reduce our carbon footprint, detect where unintended energy emissions may occur, predict anomalous and dangerous weather, and ensure we always have enough energy to meet our needs.
Energy speculation is a trillion dollar game. From sourcing fossil fuels through land or sea-based drilling to planning our solar and wind farm construction, the capital investment needed to get these projects into production is gargantuan. This high cost of investment means that prediction of maximum effectiveness is very important. In oil and gas, predictive modeling with soil sampling, drill temperatures, and vibrations levels can inform drill equipment decisions, maintenance work, and extraction. Rig drilling fluids and pump costs make up almost 50 percent of expenditures. AI models can increase efficiency by assisting the decision process to increase net yield and reduce total capital and operating costs for drill rigs.
In solar farm creation, the performance of the photovoltaic cells over time can be modeled with AI to recognize when cell maintenance and replacement is needed for maximum solar absorption. In wind farms, the predictive maintenance for electricity generating turbines can help maintain the performance of the turbine over the life of the wind farm. Current parts and maintenance cost are approximately 20 percent of the total cost of ownership with standard warranties only lasting two years. Conservative estimates demonstrate that AI predictive maintenance can provide 10 percent cost savings, driving significantly more value and lowering the cost of renewable electricity generation.
Emissions leaks of methane and other natural gases cause damage to the environment and also increase the cost of energy transportation. These costs are ultimately passed to the customer through increased prices to the commercial and household consumer. Using sensor data on pipelines, historic emissions data, and overhead imagery, AI algorithms can accurately predict leaked emissions from pipelines and equipment across the United States without requiring continuous costly physical inspections. This proactive approach, driven by AI, reduces potential environmental damage, pollution fines, and overall gas transportation costs for energy providers.
Investment in green technology is changing the way the world generates and consumes power. Individual households are contributing back to the total grid energy through rooftop solar panels. Farmers are installing wind turbines on their farmland, and consumer power usage is dynamically changing as households charge vehicles and electronic devices through home outlets. This dramatic departure from the historical model of centrally generated electricity at coal-fired power plants or gas reciprocating engines distributed on a single path transmission and distribution system will require modernization of grid management for reasons like environmental protection, grid safety and resiliency, and increasing power demands. AI is critical to the modernization of America’s energy grid for the 21st century to accurately forecast demand, increase efficiency in power generation, lighten the load on fossil fuel sourcing, and reduce transmission strain through underpowered circuits.
New growing demands on the electric grid combined with a global need to fight climate change dictate a requirement of new methods to manage power generation and distribution. AI forecasting and decision intelligence can optimize the capture of fossil fuels as humanity transitions to carbon zero energy solutions. AI can also assist in the traditional grid management, providing predictive resiliency in the networks that keep a country’s economic engine moving forward.