André Nobre
Co-founder & Managing Director
PV Doctor
PV Doctor offers an AI-driven asset performance management platform for solar PV systems. It monitors, analyzes, and diagnoses underperformance to provide actionable insights, ensuring optimal energy generation and asset health.
Updated 6 months ago
Funding
Funding rounds are not available yet.
Founders
Product
Problem
Solar photovoltaic (PV) systems can experience underperformance due to various factors, leading to reduced energy generation and financial losses for asset owners. Identifying the root cause of these performance degradations and implementing timely corrective actions is crucial for maintaining optimal operational efficiency.
Solution
PV Doctor provides an AI-driven asset performance management (APM) platform designed to monitor, analyze, and optimize solar PV systems. The platform ingests operational data to detect anomalies and underperformance, providing actionable insights for rectification. By offering continuous monitoring and diagnostics, PV Doctor aims to restore and maintain solar assets at their peak energy generation capacity. The service encompasses data analytics, preventive maintenance, and direct intervention for system rectifications.
Target Audience
The primary customers are solar asset owners, developers, and operators who require robust performance monitoring and management solutions to maximize the yield and lifespan of their photovoltaic installations.
Features
- AI-powered data analytics for continuous monitoring of PV system performance metrics.
- Anomaly detection algorithms to identify deviations from expected operational parameters.
- Root cause analysis engine to pinpoint sources of underperformance.
- Predictive maintenance capabilities to anticipate potential failures.
- Reporting dashboard with key performance indicators (KPIs) and system health status.
- Support for various PV asset types including residential, commercial, industrial, and utility-scale solar farms.
- Rectification and investigation services to address identified performance issues.