Improve Asset Health and Increase Customer Engagement
BDA helps utilities use all relevant data sources that underpin machine learning models to enhance grid asset management and forecasting systems, boost energy-efficiency initiatives, and enrich customer service offerings and engagement, all through real-time predictive insights.
BDA predicts asset failures across generation, transmission, and distribution systems to reduce costs and maximize uptime.
BDA predictive maintenance uses supervised and unsupervised machine learning algorithms to analyze streaming data across the sensor, and asset management systems, as well as technician notes and weather, to detect anomalies and predict malfunctions before they occur.
BDA improves utility program effectiveness, increase customer engagement, and enable new products and services to participate in critical initiatives.
BDA empowers customers to reduce energy costs and improve building operations through real-time tracking, analytics, and optimization.
BDA energy management uses machine learning techniques to enable accurate forecasting, benchmarking, building optimization, demand response, and anomaly detection to lower costs, improve building operations, and meet utilities’ and their customers’ shared energy-efficiency goals.
BDA identifies instances of energy theft to protect core revenues, at higher accuracy and lower cost than conventional rules-based approaches.
BDA uses supervised machine learning algorithms to correlate disparate enterprise systems and high-frequency transactions to pinpoint fraudulent activity and support advanced pipeline management for efficient resolution and revenue recovery.
BDA uses supervised and unsupervised machine learning techniques to prioritize meters projected to malfunction and enable a managed workflow for efficient triage and resolution of issues.