Energy transition, price volatility and intensified competition are just some of the many challenges facing this sector. To compete, energy and utilities companies need to be able to reliably predict and understand a sector in flux. But conventional analytics fail in dynamic, complex environments.
Causal AI identifies the cause-and-effect relationships that govern the energy value chain, enabling industry players to solve their biggest challenges.
Grid managers, energy firms and utilities will have to grapple with changes driven by the greater complexity of supply from an increased renewables mix and demand due to new, connected technologies. Energy and utilities may look to Machine Learning to help them adapt, but current AI techniques are often caught out by change:
The growth of PEVs represents one of the most important trends for the future of the grid. In contrast to current stationary loads, PEVs are mobile loads that may appear at any charging point, which many current grid and load forecasting methods are unable to model. causaLens’ Causal AI methods build accurate models even in highly non-stationary environments. And thanks to the true explainability of causal models, the electric power and transportation sectors can use them as a common basis for much-needed collaboration to improve capital investment planning.
As solar and wind generate more of the world’s electricity, utilities will have to manage intermittent and highly variable supply. Current machine learning methods struggle to generalize out of their training distributions. Without accurate forecasts, risks for power outages and surplus surges are multiplied. causaLens uses Causal AI algorithms that are truly generalizable: they can model even rare behaviours like extreme weather conditions to automatically optimize renewable energy ROI, for example by calculating the amount of energy that can be redirected into the power grid or stored.
Efficient predictive maintenance can only be achieved with root cause analysis. Existing correlation-based predictive maintenance models often fail to distinguish between cause-and-effect relationships and spurious correlations. At the same time they fail to adapt to changing conditions in the environment. causaLens autonomously discovers the causal relationships in real-time enabling businesses to optimize the maintenance of their systems avoiding costly downtime.
Current machine learning approaches are unable to accurately model the complexity of the energy market. Spurious correlations undermine their insights and predictions. causaLens uses its proprietary Causal Discovery methods to cut through the enormous number of potentially relevant variables and identify the true drivers of commodity prices. With track record of success in many of the world’s leading investment banks, causaLens allows energy traders to reduce their cost per trade to the levels enjoyed by the leading financial institutions.
Optimization of oil exploration demands domain knowledge, but most current machine learning approaches have no way to incorporate it effectively. As a result, their recommendations add little value. causaLens’ causal AI engages directly with a company’s domain experts, building models that incorporate the scientific knowledge of geologists. Thanks to a unique and intuitive graphical interface, causaLens’ CausalNet inference engine can be taught true geological relationships by domain experts and use this knowledge to inform its analysis of huge geo-data sets, providing actionable insights from the most profitable wells to precision drilling support to geosteerers.