Focus/Goal
The primary focus of this master project was the development and implementation of a sustainable energy management system (EMS) for the weather station at the Hochschule RheinMain Wiesbaden campus.
- Building on Previous Work: The project explicitly builds upon the initial bachelor thesis regarding the station setup and cloud integration. It aimed to take the station from a non-operational state (due to hardware failure) to a fully functional, remotely monitored system.
- Core Objective: To increase the efficiency of solar energy usage through dynamic regulation and to ensure continuous operation by intelligently monitoring the battery's state of charge (SoC).
- Integration: To implement a modern IoT infrastructure (MQTT, Node-RED, InfluxDB) for real-time data acquisition, visualization, and remote maintenance.
Changes/Improvements
Hardware Restoration
- Battery Replacement: Replaced the defective, deeply discharged battery with a new Lithium-Ion battery (12.8V, 60Ah, 768Wh).
- Remote Access: Re-established network connectivity by installing a WLAN router with an external antenna, enabling remote maintenance which was previously impossible.
IT Infrastructure Implementation
- Docker-based Architecture: Deployed on the server side for modularity.
- MQTT Broker: Handles lightweight data transmission between the station and the server.
- Node-RED: Processes data flows, calculates threshold values (like SoC), and manages logic.
- InfluxDB: Stores time-series weather and energy data persistently.
- Grafana Dashboard: Visualizes real-time meteorological data (temperature, wind, etc.) and energy metrics (voltage, consumption, solar forecast).
Logic & Algorithms
- State of Charge (SoC): Developed formulas to calculate SoC based on battery voltage.
- Energy Forecasts: Estimating remaining "measurement duration" based on current consumption.
- Voltage Thresholds:
- Deep Discharge Protection: 10.5 V (System Shutdown).
- Maximum Voltage: 14.1 V (Charging Cutoff).
- Nominal Voltage: 12.8 V.
Energy Analysis Findings
The project conducted a detailed analysis of the sensor power consumption, identifying the primary energy drivers:
| Component | Daily Consumption (Wh) | % of Total Sensor Load | Note |
|---|---|---|---|
| Rain Sensor (Heated) | 144 Wh | 82% | Highest consumer due to heating element |
| Wind Sensor | 24 Wh | 13% | |
| Other Sensors | 8.16 Wh | 5% | Temp, Humidity, Radiation |
| Total Sensor Load | 176.16 Wh | 100% | Excluding Router/Logger |
(Source: Table 2 of the paper)
Challenges
- Initial System Failure: The project began with a completely non-functional station (broken battery, no connection), requiring physical repairs first.
- High Power Consumption: The standard WLAN router used was identified as a major energy consumer, drawing too much power for an autonomous solar system, especially during winter or low-light periods.
- Lack of Data Transparency: The system initially lacked precise sensors to measure the actual energy flow (solar yield vs. consumption), meaning energy values had to be calculated theoretically.
Outcome
- Operational Status: The weather station was successfully brought back online and is actively recording and transmitting data.
- Active Monitoring: A functional energy management system is now in place, visualizing battery status and weather data in real-time via Grafana.
- Critical Findings: The project concluded that true long-term sustainability requires replacing the high-consumption router (e.g., with a Raspberry Pi) and installing MPPT solar controllers/shunt modules for precise measurement.