Artificial Intelligence

Grid Planning and Investment: ADMS & Dynamics 365 Data provides valuable insights for grid planning and investment decisions. This includes identifying areas with high DER penetration, predicting future load growth, and optimizing the placement of new grid infrastructure.

Electrical Price Prediction: AI-powered Production & Price forecasting can be used to adjust energy distribution in real-time based on factors like weather conditions, demand forecasts, and renewable energy availability. This ensures efficient use of resources and maintains grid stability.

Fault Repair Work Order Integrations: Detail Work Order data, including asset, work type, resource, timing and costs allow for centralized Service review in Microsoft Dynamics 365.

Fault Location, Isolation, and Service Restoration (FLISR): Using real-time data and sophisticated algorithms to quickly locate faults, isolate the affected area, and automatically restore service to unaffected customers. This minimizes outage durations and improves grid reliability. – Prioritizes most vulnerable elements of the grid to be worked on to prevent outtages.

DERM Learning: Working with new Distributed Energy Resource Management systems, and Machine Learning, being able to predict the load fluctuations that new DERs put on the Grid

Power Management for Renewables: Inconsistent Sun exposure, Wind speeds, and Weather patterns mean you need intelligent predictive models to anticipate the output of your Renewable Production using Microsoft Fabric Data Aggregation and AI tools.

SCADA Intelligence: Using AI tools Predictive Maintenance with weighted analysis of historical performance, real time SCADA inputs, and real time weather updates. Including real time Renewable energy inputs, you can now create Dynamics Line Ratings; to ensure optimal distribution of the new electricity produced.

Demand Response Optimization: AI analyzes historical consumption patterns and real-time data to predict peak demand periods. Utilities can then incentivize customers to reduce energy usage during peak times, optimizing demand response programs and avoiding costly grid overloads.

24/7 Availability: AI-powered chatbots provide instant responses to customer inquiries and support requests, ensuring round-the-clock availability without human intervention.

Personalization: Utilizes data analytics to personalize customer interactions based on historical data and preferences, enhancing customer satisfaction and loyalty.

Efficiency: Reduces wait times and resolves routine inquiries quickly, allowing human agents to focus on more complex issues, thereby improving overall service efficiency.

Automation: Automates routine tasks such as billing inquiries, account updates, and payment processing, reducing administrative overhead and improving accuracy.

Predictive Insights: Uses AI-driven analytics to forecast customer behavior and anticipate service needs, enabling proactive management and tailored service offerings.

Integration: Integrates seamlessly with existing CRM systems to centralize customer data, ensuring a holistic view of customer interactions and enabling personalized service delivery.

Data Analysis: Analyzes vast amounts of data from smart meters, IoT devices, and operational systems to extract actionable insights, such as energy consumption patterns and grid performance.

Decision Support: Provides executives and operational teams with real-time insights and predictive analytics for strategic decision-making, optimizing resource allocation and operational efficiency.

Risk Management: Identifies potential risks and vulnerabilities in the energy infrastructure through AI-driven anomaly detection and predictive maintenance, ensuring reliability and resilience.