Enterprise AI for Commercial Energy Management

Challenges

A large US-based utility corporation generates power across 16 generating facilities, providing energy to customers including communities, businesses, municipalities, and electricity providers. Between generation and transmission activities, the utility faced increasing challenges to satisfy its goals, ranging from cyber-attacks and weather disruptions to the complexity of integrating renewables to the grid. Meeting these challenges requires a strategy of enterprise-wide digital transformation.

As part of a multi-year digital transformation program, the utility has first implemented widespread deployment of IoT sensors across the grid and next focused on greater customer engagement to help customers lower energy consumption. Prior to engaging C3 AI®, the utility set a goal to decrease customers’ energy consumption by 20% in 2020.

However, unifying all relevant data and providing curated recommendations to customers proved challenging in meeting this goal. There was concern about missing this enforceable target, especially since the utility did not have an AI-enabled ability to automate the necessary data collection and intelligent recommendations to empower customers to leverage data and take action to reduce energy costs. There was also a gap in quality and quantity of data for certain customers that C3 AI would have to address.

Approach

To address this challenge, C3 AI configured C3 AI Energy Management over two phases. The first phase included out-of-the-box C3 AI Energy Management, while the second phase enhanced the application ultimately providing 6 use cases including:

• Benchmarking and analysis of energy usage trends across organizations and facilities
• Project and goal management
• GHG benchmarking and monitoring
• Data anomaly management and alerts
• Virtual energy audit (customer questionnaire about energy usage and facilities) and recommendations
• Self-service reporting with external BI tools

A key cornerstone of the utility’s long-term strategy is to position new energy products to customers, such as its advisory services focused on predictive analytics, fault detection and diagnostics, building alerts, and billing analysis. To support this, C3 AI Energy Management enables the utility to view, analyze, and report on energy trends, identify customer engagement opportunities, and manage and track progress towards goals.

The utility selected C3 AI Energy Management to help achieve its energy reduction goal across all customer segments. With C3 AI Energy Management, the utility can deliver the right information at the right time to their customers with curated AI insights and recommendations that help reduce energy consumption. The application helps customers visualize their energy consumption, identify opportunities to save energy, and track their energy efficiency progress over time. By deploying the application to their customers, the utility surpassed their goal of 20% energy consumption with high customer adoption and a 23% reduction, leading to over $500 million in savings.

Company Objectives

• Integrate and unify data from over 20 disparate data sources (e.g., billing data, metered and sub-metered data, customer data, asset data)
• Apply machine learning algorithms to generate targeted alerts and actionable insights on customer data, energy consumption forecasts, and recommendations for saving energy
• Leverage the white-labeled C3 AI Energy Management UI as a customer web portal for utility customers
• Position utility to offer new energy products and advisory services

About the Company

• $2.7 billion annual revenue in 2021
• 15 generating facilities providing 30 GW-hours annually
• 1000+ customers including communities, businesses, municipalities, and electricity providers
• 2,500 employees
• Majority of electrical power generated comes from renewable sources

Project Highlights

• Over 19,000 facilities and 40,000 service points monitored
• Daily load of 300+ M rows of interval meter data
• More than 2065 organizations participating in the program
• Developed 64 custom analytics for machine learning models and application UI
• Configured the C3 AI Energy Management application user interface

Solution Architecture