Summary

Scalytics Federated strengthens digital operations in utilities by automating analytical tasks, improving situational awareness, and enabling rapid deployment of AI and ML models. Demand-side management and renewable energy integration are two central fields where AI delivers measurable operational value. With a low-code interface and distributed execution across protected data environments, utilities can build advanced energy applications without compromising security or infrastructure isolation.

Our approach is shaped by direct experience. Scalytics CTO Alexander Alten served as Chief Digital Architect at E.ON, where he led the transformation from legacy Hadoop infrastructure to cloud-native big data platforms and architected IoT systems connecting distributed energy assets across Europe. That hands-on work with SCADA integration, grid digitization, and the operational realities of utility AI informed the design of Scalytics Federated from day one.

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Use Case 1: Demand-side Management

Demand-side management (DSM) reduces or shifts electricity consumption during peak periods. AI-driven DSM models use data from smart meters, IoT sensors, weather services, market indicators, and SCADA systems to predict load and coordinate responses that preserve grid stability.

This includes the ability to design flexible tariff mechanisms tailored to the profiles of residential, commercial, or industrial consumers. Incentivizing consumption shifts during off-peak hours reduces pressure on transmission and distribution infrastructure and helps avoid emergency interventions.

With Scalytics Federated, DSM and forecasting models can run directly within decentralized data environments, including private SCADA networks. Utilities maintain control over sensitive operational data while enabling high-accuracy predictions for load peaks, congestion events, and demand fluctuations.

Periods of limited renewable generation—known in the industry as Dunkelflaute—occur several times per year. Accurate forecasting during these events is essential for grid balancing and procurement decisions. AI models help operators plan ahead and reduce exposure to volatile market conditions.

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Real-world examples

PG&E Flex Alerts (California): AI models monitor grid stability and trigger consumer alerts when short-term load reductions are required. Reported reductions can reach up to 10 percent during peak stress.

ERCOT forecasting (Texas): ERCOT applies predictive models to weather and historical load data to plan generation and improve grid efficiency. Reported improvements in forecast accuracy reach double-digit percentages.

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Use Case 2: Renewable Energy Integration

Solar and wind production fluctuate with weather conditions. This variability introduces uncertainty into grid operations. AI helps utilities forecast renewable availability, identify stability risks, and optimize dispatch decisions.

Scalytics Federated allows forecasting and operational models to run on distributed datasets, including edge locations, operational systems, and on-premise data platforms. This approach supports utilities that must operate under strict network separation or security requirements.

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Applications that strengthen renewable integration

Predictive maintenance: AI models detect equipment anomalies and predict failure patterns. This enables timely maintenance scheduling, reduces downtime, and prevents system-wide disruptions.

Asset optimization: Operational models improve the performance of renewable installations. In wind parks, optimizing turbine configurations and reducing aerodynamic interference often yields an additional 2 to 3 percent in energy output.

Demand-side management algorithms AI: identifies flexible consumption segments and supports incentive structures that shift load away from critical periods. This reduces grid stress and stabilizes operations.

Energy forecasting: Forecasting models enable more efficient procurement strategies and ensure that adequate resources are available during high-demand or low-generation periods.

Grid optimization: AI improves the utilization of generators and transmission lines, reducing congestion costs and increasing operational resilience.

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Strengthening Digital Operations in Utilities

Scalytics Federated supports decentralized analytics and allows utilities to build and deploy complex energy models across existing systems. This reduces integration complexity and accelerates the delivery of modern grid intelligence. Operators gain real-time insights, automated workflows, and a consistent decision framework across the entire energy value chain.

Benefits for grid operations

Higher efficiency: AI reduces manual workload and sharpens operational decision-making.

Lower operational costs: Models locate avoidable energy losses and increase asset utilization.

Improved reliability: Forecasting and anomaly detection increase resilience against disruptions and reduce the probability of blackouts.

Real-world examples of AI in utility operations

New York Power Authority (NYPA): AI-based DSM strategies help reduce grid stress during peak hours and lower maintenance costs.

Australian Energy Market Operator (AEMO): AI forecasting improves resource planning, supports renewable integration, and reduces operational expenses.

Danish Energy Agency: AI-optimized grid operations increase efficiency and reduce management costs across national infrastructure.

Why Utilities Choose Scalytics Federated

Utilities face strict security and operational requirements. Core operational data often resides in SCADA systems, isolated networks, or regulated on-premise environments. Centralizing these datasets is costly, slow, or prohibited. Scalytics Federated executes analytics and AI models directly where data resides, enabling operators to modernize grid intelligence without restructuring existing infrastructure.

The platform incorporates practical insights gained from large utility modernization programs. Our founding team includes veterans of E.ON's digital transformation team; work that involved migrating from on-premise Hadoop to cloud-native big data, building unified IoT operating systems across energy assets, and navigating the operational realities of GDPR-compliant data processing at scale. This experience shapes a solution that aligns with real operational constraints and the long-term goals of grid transformation.

For strategic planning, architecture guidance, and next steps, utilities can review our Smart Grid Intelligence overview.

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Links:

[1] NYPA Selects Technology Firms to Help Accelerate Intelligence Capabilities

[2] Using deep learning to forecast renewable energy generation

[3] Application of energy informatics in Danish research projects‍