Solar Energy Monitoring and Control Platform Development

At Adequate Infosoft, we take pride in building long-term technology solutions that continue to evolve with our clients' business needs. One such success story is our Solar Energy Monitoring and Controlling System , developed for a European client and actively running in production for more than 10 years.

Originally conceived as a solar monitoring platform, this platform has seen many generations of both physical (hardware, firmware, and cloud architecture) and Internet of Things technology innovations as they've matured.

Today, it serves as a comprehensive energy management solution, helping monitor, control, and optimize solar installations at scale.

As the number of solar monitoring platforms kept growing and being improved with new technology "Sunsystem" has become a key player in the Denmark energy industry providing proof that a high quality IoT system has the potential to evolve from a first concept to a successful, mature and market leading product.

Embedded C/C++, STM32 and ESP32-WROOM (C, IAM IDE) are used for this project, with various communication protocols including: ZigBee, Bluetooth Low Energy (BLE), Wi-Fi, Modbus and M-Bus implemented as part of a modern design along with 330W of 24V solar panels and BENY micro-inverters.

The client required a detailed monitoring system for their solar power that monitored all the important measures for their system including: AC and DC output of energy, energy generated, voltage, current, temperature and overall system performance.

The client needed an easy-to-use interface that would allow the user to view the output of the solar panels and the amount of energy consumed in real time. The interface needed to provide the user with a visual representation of the kWh produced and the time interval of production so they could find trends in how much energy their system is producing and be able to make more effective decisions as an operator.

The new system would also allow the user to calculate the amount of CO₂ that has been reduced by using this renewable energy source, thus giving the user a good way to see their contribution to protecting the environment and supporting the environment's goals for sustainability.

The evolution of the "Sunsystem" platform over time has led it to become much more than just a conventional solar monitoring system. It has expanded to incorporate features such as: artificial intelligence (AI), predictive maintenance, advanced Internet of Things (IoT) and intelligent automation.

This has allowed users to use the platform not only for monitoring but also for user forecasting of energy production, identifying possible problems with their equipment prior to equipment malfunctioning, and ultimately improving their overall system performance.

Previously, the Sunsystem platform was considered a traditional data acquisition system, however, due to the many capabilities mentioned above, it has evolved into an intelligent energy management solution that provides users with actionable insight, increased operational efficiencies, and lower maintenance costs and enables solar owners/operators to make data-driven decisions about their solar assets.

Predictive Analytics & AI/ML Layer

Using an AI-based prediction engine, solar production forecasts for the next 24 to 48 hours can be determined by utilizing both current and past weather patterns (cloud cover and temperature) and weather forecasts in addition to past weather patterns.

When a forecast is provided, an application can be created to help users determine when to schedule heavy loads of power that will coincide with peak production hours (for example, when the battery banks of an electric vehicle are being charged or when water pump systems are being operated).

This application could be achieved using a variety of methods, including using TensorFlow Lite Micro on an ESP32 or utilizing Python (FastAPI) on a cloud-based microservice.

Edge Computing & Firmware OTA

Utilize edge computing to move beyond just capturing data. Using an ESP32 device's edge computing capabilities enables detection of anomalies (i.e., voltage drops from shading or soiling) at the device level, as well as sending alerts with no cloud latency. Enhance the capabilities of your IoT device(s) by implementing over-the-air (OTA) firmware updates for all your devices using either Azure Device Update so that new features and security patches can be installed via the Internet.

Green Credits on Blockchain

Use a blockchain technology layer to create a digitally-signed record of energy generation (e.g. number of kWh produced, amount of CO₂ reduced) using Hyperledger Fabric or Ethereum to allow for unalterable certificates of Green Credits that can be traded for carbon offsets.

Digital Twin Dashboard

Created a Three.js-based 3D digital version of the solar array. Overlay real-time data (temperature for each panel, inverter status, MPP tracking) over the actual layout; give viewers instant visual access to the status of underperforming panels.

Future suggestions for IoT include the following:

• A Dust & Soiling Sensor (Nevada Nano OT-1) triggers automated cleaning alerts
• A Solar Tracker Controller (Zilla 1240) is used to maximize MPP efficiency on dual-axis panels.
• BMS Integration (Daly or JK BMS) allows you to monitor cell voltages, temperature, and state of charge (SOC) for battery storage added to your system.

The upgraded dashboard introduces advanced AI, analytics, and blockchain-driven features to improve visibility, prediction accuracy, and system transparency:

AI Forecast vs Actual Analysis:

Interactive line charts with confidence intervals to compare predicted and real-time solar energy generation.

Panel-Level Health Map:

Heatmap visualization of voltage and temperature data for quick identification of underperforming or faulty panels.

Carbon Credit Ledger:

Blockchain-verified tracking system to record and manage carbon credits earned through renewable energy generation.

Edge Anomaly Logging:

Edge log event logging will allow users of an ESP32-based edge device to view any time there has been an event logged by their device in real-time. This allows for instant fault detection and instant diagnosis at the edge.

At Adequate Infosoft, we want our clients to get the best possible result from the software and apps we are designing. To design this digital hoarding management software, we followed the below process:

1. 01

   Determine the requirements:

   In this step, we identified the parameters that needed to be measured and how we wanted to analyze the data. For instance, we want to measure the voltage and power generated by solar panels. The client also wants to track the energy generated over time and analyze the data to see if the solar panels are performing as expected or not.

2. 02

   Select the sensors:

   In the next step, we select the sensors to install in the system. Here, we used sensors like a current transformer or voltage transducer to calculate voltage and current. Apart from this, we used a wattmeter or power analyzer to measure the power generated by the solar panels.

3. 03

   Choose a data acquisition system:

   After selecting sensors, we selected a data acquisition system. This system will be responsible for collecting data from the sensors and storing it for analysis. Depending on the requirements, the data acquisition system can range from a simple data logger to a more complex computer-based system.

4. 04

   Install the sensors:

   Once the sensors and data acquisition system have been selected, they are installed in the system. We mounted the sensors on the solar panels and connected them to the data acquisition system.

5. 05

   Configure the data acquisition system:

   After installing the sensors, our developers configured the data acquisition system. This involved setting up the system to collect data from the sensors and store it for analysis.

6. 06

   Test and calibrate the system:

   Once the system is installed and configured, we test and calibrate it to check the accuracy of the collected data. This will involve comparing the measurements taken by the system to those taken by a reference standard to ensure that the system is accurate and reliable.

7. 06

   Analyze the data:

   Finally, the data collected by the system is analyzed to determine the energy generated by the solar panels. The data can be used to calculate the total energy generated over a given period of time, as well as track the performance of the solar panels over time.

• For Control Box ( ESP 32 Wroom ): C, IAM IDE
  
• For Web-based Applications: C#, .Net Framework 4.5 , Asp.Net , HTML5, Ajax, JavaScript, J-Query
• For Backend: ASP Core, SQL Server 2012
• Firmware Development: Embedded C, C++
• Wireless Protocols: ZigBee, BLE, WIFI , MODBUS , MBUS
• Others: 24V 330W Polycrystalline Solar Panel, BENY Solar Microinverter, STM32

We developed the following modules for this project:

Pictures of the installation:

• This screen shows a picture of the installed solar panels.

Energy production

• On this screen, the user can check the overall power generated by the solar panels.
• Users can calculate the overall power generated by the solar panels per hour, per day, per year, and the total power generated to date.

Inverter Status

This segment is divided into four different parts:

• The first part shows the Number of inverters working in the system/the total number of inverters installed in the system.
• The second panel shows the number of MPPs running/total number of MPP installed.
• The third panel shows the stopped inverter/total number of inverter.
• Fourth-panel show the number if inverter will error/total number of inverters installed in the system

Production

The production screen shows the overall power produced by the solar panels.

CO2 saving

This panel shows the amount of CO2 emissions saved by the generation of solar power.

Energy production MPP

Here, MPP stands for "maximum power point." The maximum power point is the point on the current-voltage curve of a solar panel at which it is operating at its maximum power output.

The Sunsystem platform has been a live and continuously serviced solution since 2016, delivering reliable solar energy monitoring and control services in real-world environments.

The system is actively used across web and mobile platforms, providing users with seamless access to energy insights and performance tracking. You can explore the live system here: Sunsystem Web Portal, along with its mobile applications available on Android Google Play Store and iOS Apple App Store.