Case study

SolarWatch: AI-Powered IoT Monitoring & Predictive Maintenance for PV Plants

The Problem:

Photovoltaic (PV) plant operators struggled with limited real-time visibility, making it difficult to detect inefficiencies or specific component failures. Without precise data, maintenance was often reactive and expensive, requiring technicians to perform time-consuming manual troubleshooting across vast solar farms.

Our Solution:

To develop a high-performance “Monitoring 2.0” ecosystem—a cloud-based SaaS and mobile platform that provides real-time, module-level visibility. We integrated an AI-powered simulation engine and a Big Data/NoSQL backend to identify error patterns, enabling operators to distinguish between minor fluctuations and critical technical failures in real time.

Technology Stack:

TypeScript, React, Node.js, MongoDB, Apache Kafka, Docker, Kubernetes

Key Results & Value:

Real-Time Module Visibility: Enabled operators to monitor every individual solar module across multiple plants from a single web portal or mobile app.
AI-Enhanced Error Detection: Implemented an AI engine that identifies specific error patterns, reducing the need for expensive, manual on-site troubleshooting.
Dispatch 2.0 Compliance: Ensured full alignment with Germany’s rigorous “Dispatcher 2.0” energy management standards.
Multi-Tenant Architecture: Built a scalable SaaS platform with dynamic message schemas, supporting both commercial and residential solar farms with localized requirements.
Reduced Maintenance Costs: Provided clear, actionable instructions for non-experts, preventing unnecessary technician deployments and streamlining repair workflows.

The problem:

Die „Solar Energy Platform“ ist eine innovative Lösung. Sie nutzt KI-gestützte Simulation, Big Data und eine NoSQL-Datenbank. Diese Kombination identifiziert Fehler und Muster in Echtzeit und bietet Einblick in jedes Modul der Solaranlage. Unser benutzerfreundliches Webportal vereinfacht die Fernüberwachung. Benutzer können problematische Module schnell identifizieren und erhalten klare Anweisungen zur Lösung. Durch diesen Ansatz entfällt die Notwendigkeit einer kostspieligen Fehlersuche vor Ort, wodurch die Wartungsprozesse optimiert und eine optimale Leistung der Solarenergiesysteme sichergestellt wird.

Key features:

Ability to orchestrate multiple solar panels with graphical representation on web portal

Integration with client’s hardware for measuring solar panel efficiency

Compliance with Dispatcher 2.0 standard (Germany)

Mobile apps for commercial and residential solar panel farms

Web SaaS portal with data streaming and real-time analytics

Multi-tenancy backed by dynamic message schemas

Cloud-agnostic and on-premise deployment

Ability to handle and analyze a huge amount of device sensor data

Solution:

This is exactly what our “Solar Energy Platform” is capable of. Combining an AI-powered simulation engine, based on Big Data and a NoSQL database, with unlimited data processing capacity allows the module to accurately find and define not only errors, but also error patterns. Now, using our web portal for remote monitoring, you can see, at a glance and in real-time, exactly which module of the plant is presenting problems, and what needs to done to solve them. You can forget about sending an expensive technician to perform time-consuming, non-precise troubleshooting! Nothing could be clearer or more simple.

Ingmar Kruse

CEO, Sun Sniffer, Germany

We are delighted to acknowledge that Insoftex skillfully programmed our frontend using React, meticulously bringing our design to life. Their adherence to our timelines and effective communication ensured a seamless and productive collaboration.