Why utilities are using AI digital solutions to reinvent the science of price forecasting

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As the energy marketplace has grown more complex, energy producers, grid operators, and consumers require more intelligence and faster data analysis to interpret pricing patterns. Producers need to know how and when to maximize their energy output during times of the day and week when pricing will work the most to their advantage. Consumers need to accurately perform short-term forecasts of both their consumption and pricing to optimize their purchasing power.

Part 1 of this two-part blog series “Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting” reviews the reasons why the marketplace has evolved and explains how new energy trends are impacting pricing complexity across the industry. This blog post (Part 2) highlights some of the advantages utilities and other energy stakeholders can accrue when they deploy an AI-driven forecasting digital solutions model.

When compared to classic mathematical forecasting models, automated AI models can forecast consumption and production with 20 percent to 30 percent more precision – which leads to direct bottom line savings. In addition, field studies also determine that AI models prevent more large errors than do classic models.

team of professionals reviewing AI digital solutions

Unique abilities of AI digital solutions tools act as accurate differentiators

We at Predictive Layer, a Schneider Electric Energy Management Technology Partner, have developed an AI system to help large enterprises, traditional utilities, and producers of renewable energy resources to better forecast energy consumption and pricing so that customers can save money through smarter energy choices.

Listed below are some of the reasons why energy stakeholders are investing in such AI-driven forecasting digital solutions:

Global application

Many countries today have created their own specific energy demand, supply, and pricing rules. Besides providing energy supply and demand forecasting, AI tools also can learn the unique characteristics of the pricing models of each of these local markets. People who understand these local pricing models are very difficult to replace when they retire. The AI model provides an insurance policy that pricing knowledge is retained and put to good use.

Access to a richer data pool

AI models directly access multiple sources of information, including Transmission System Operator (TSO) energy balancing reference information, multiple weather forecasts, pricing alerts, market prices published in real time, published client activity plans (e.g., vacations, holidays, major events) and any published signals on open platform forums like Schneider Electric Exchange (take a tour of the Schneider Electric Exchange platform). This collection of data enables unique precision and provides stakeholders with enough response time to adjust to marketplace changes.

Increased savings

AI models consider all the variables at play and propose the best purchase or trade plan for the upcoming weeks, months, and quarters. This enables savings of 5% to 10% when compared to exercising a standard purchase plan approach. These gains are possible because the machine learning aspects of the AI tool recompute models and automatically adapt to new patterns in the marketplace much faster and much more accurately than a human remodeling, especially, if the individual has to manage multiple forecasts simultaneously.

Self-monitoring abilities

AI models are constantly self-monitoring the health and accuracy of their own forecasting model. They also suggest the integration of additional inputs that can continue to sharpen the accuracy of the model thereby further reducing risk for error as time goes on.

Organizations that have deployed our AI solution in the field over the last 18 months have developed enough confidence in the tool that they now rely on it as their main resource for pricing-related decisions. Only automated alerts surrounding anomalies are sent to a human manager. Such automation allows the forecasting team to be freed up to both further scale their forecasting systems and to build in more resilience to enable the servicing of new opportunities.

Resources on Artificial Intelligence tools

To learn more about how artificial intelligence tools can better manage grid consumption forecasting, read Part 1 of this blog series ”Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting” or review our Predictive Layer website. You can also find us on the Schneider Electric Exchange platform, a global network of experts and peers in energy management and automation.

Also, access Schneider Electric Exchange to collaborate and co-innovate on trending topics like AI and innovative solutions by joining any of hundreds of Exchange communities.


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