Multi-Domain Monitoring and Optimization Toward Net Zero

Introduction

Achieving Net-Zero is more than just installing solar panels—it's about making smart, data-driven decisions to become truly energy independent. The biggest blocker today is the lack of a system that can accurately tell you, real time energy consumption based on both grid and solar.

Real-Time Insight: The Key to Net-Zero

This project solves this by providing real-time monitoring dashboard of complete energy of a campus:

• Solar Generation: Track exactly how much energy panels are generating right now
• Grid Consumption: We track exactly how much energy consumption from the grid (substation).
• This immediate comparison shows whether campus/building is Net-Positive or Net-Negative and allows to identify energy wastage.

oneM2M

All this data from energy meters and solar inverters sent to Mobius (middleware), which is based on the global oneM2M standard.

• Interoperability: Mobius4 acts as a common service layer, ensuring that data from different devices (Wi-Fi, Wi-SUN) is collected, stored, and managed in a uniform way, avoiding vendor lock-in.
• Security & Scalability: This standardized approach provides robust data management and a secure, scalable foundation for future expansion.

Simulating the Path to Sustainability

Our application goes beyond monitoring by offering a simulation capability. Users can test various interventions against their own energy data to see the predicted outcome (sustain on solar):

• Adding more solar capacity to increase self-sufficiency
• Modeling the impact of environmental factors like adding trees for carbon reduction.
• This allows users to validate potential energy improvements and confidently plan their next steps to minimize grid usage and solidify their commitment to a sustainable, Net-Zero energy future.

Motivation

Our project is based on the conviction that every building must achieve net-zero. We are motivated to provide the missing link: a seamless, standardized system for energy domain.

By integrating real-time grid and solar monitoring through a robust, interoperable platform like Mobius4 (oneM2M), we empower users with two core abilities:

• Real-Time: Instantly see their current emission impact and take immediate corrective action.
• Plan the Future: Use simulation to confidently plan investments (like adding solar or planting trees) that guarantee progress toward 100% net-zero sustainability.

The system focuses on multi-domain monitoring aim for achieving Net Zero by collecting data from energy meters and solar inverters. The data is processed using embedded hardware (Wi-SUN radio board, Raspberry Pi or Arduino Nano 33 IoT) and transmitted through the IIITH Wi-SUN/Wi-Fi network and finally to the oneM2M platform for analysis and visualization.

Architecture

Implementation

1. Grid Energy Monitoring System

For energy monitoring, we designed a compact node using:

• Silicon Labs EFR32FG28 (FG28)
• RS485–TTL converter
• Hi-Link isolated power supply
• LED indicators for status
• Custom RS485 communication library built for FG28

The system begins with the digital energy meter, which provides essential parameters such as voltage, current, power, and energy through an RS485 Modbus interface. Since RS485 uses differential signalling, the data first passes through an RS485-to-TTL converter, which converts the signals into UART-compatible levels.

From here, the EFR32 FG28 microcontroller takes control. Using UART with the Modbus RTU protocol—and powered by a custom RS485 communication library developed specifically for the FG28—the controller polls the energy meter for key parameters including voltage, current, frequency, active power, reactive power, and accumulated energy. After receiving the raw Modbus frames, the FG28 performs local processing, validating the frames, parsing the data, and formatting it into a compact payload suitable for transmission.

After processing the data, the FG28 sends this payload using CoAP (Constrained Application Protocol) over the existing IIITH Wi-SUN network, which is already deployed for smart streetlight control. The Wi-SUN mesh network automatically forwards the CoAP messages hop-by-hop until they reach the border router. The border router acts as the backhaul gateway, forwarding the data to the central server.

On the server side, a Raspberry Pi (Border Router) receives these Wi-SUN CoAP messages and converts the payload into standardized oneM2M resources such as AE (Application Entity), Containers, and ContentInstances. These resources are then pushed into the oneM2M CSE, enabling unified storage, data management, and analytics within the oneM2M ecosystem.

Fig 1 : Interfacing Energy meter to EFRFG28

2. Solar Energy Monitoring System

The solar monitoring system collects real-time data from rooftop solar inverters deployed across the campus. These inverters typically expose their operational parameters such as PV voltage, PV current, generated power, daily energy (kWh) through standard communication interfaces: RS485 Modbus. To read this data, we deploy two types of edge devices depending on the installation location: Raspberry Pi and Arduino Nano 33 IoT. In some locations where higher processing capability is needed, a Raspberry Pi is used, while in smaller or space-constrained areas, an Arduino Nano 33 IoT serves as the data collection node.

Each node periodically communicates with the solar inverter using Modbus RTU, requesting the latest operational parameters. The raw data received from the inverter is validated and then parsed to extract key metrics such as PV voltage, PV current, AC output power, and daily energy generation. The node then prepares a structured payload containing these values.

Unlike the energy monitoring nodes that rely on Wi-SUN, the solar monitoring nodes communicate using Wi-Fi. Each Raspberry Pi or Arduino Nano 33 IoT connects to the available Wi-Fi network and sends the processed solar data directly to the server using HTTP. Once the payload reaches the server, it is converted into standardized oneM2M resources, including AE (Application Entity), Containers, and ContentInstances.

These oneM2M resources are pushed into the oneM2M CSE, making solar generation data accessible for analytics, dashboards, alarms, and performance optimization. By integrating both Raspberry Pi and Arduino nodes over Wi-Fi, the system ensures flexible and scalable solar monitoring across multiple locations, supporting the broader goal of multi-domain optimization toward Net Zero.

Features

• Multi-Domain Monitoring (Energy + Solar)
• Hybrid Edge Devices (FG28, Raspberry Pi, Arduino Nano 33 IoT)
• Standardized Communication Protocols
• Custom RS485 Library for FG28
• Real-Time Monitoring and Alerts

3. Middleware(oneM2M-Mobius)

We have used Mobius the Interoperability Layer. It acts as central framework/ Middleware, facilitating data for visualization and analysis. oneM2M defines a common set of resources and APIs that are uniform regardless of the device manufacturer or communication protocol. Our solar sensors might use Wi-Fi, and our grid sensors might use Wi-SUN, but Mobius4 makes them standardized. With this we can easily integrate a completely new type of meter (like a water or air quality sensor) without rewriting the entire core application.

Advantages

• Multi-domain standardization and support (Wi-SUN/Wi-Fi)​
• Access control policies​
• Historical data subscription​
• Latest data APIs​
• Interoperability​

ResourceTree

Descriptor

• Descriptor container gives details of each parameter(sensor) used in the grid device and its accuracy, resolution, units and datatypes along with node location.

• Descriptor container gives details of each parameter(sensor) used in the solar device and its accuracy, resolution, units and datatypes along with node location.

This provides essential Common Service Functions (CSFs) like Subscription/Notification, and Security as core, pre-built services:

Access Control Policy (ACP)

ACP defines the rules and policies that govern access to resources within a OM2M. It specifies the permissions and restrictions associated with different types of devices and entities. In the architecture we have used acp_solar, acp_energy for posting data from the sensor end and retrieving data as well.

Subscription

The external script will subscribe to container discovered previously according to all the sensors specified in infrastructure. Each sensor will post on each corresponding node in onem2m. After having subscribed, the script will listen for each subscription sector as parameters specified.

After receiving a request, the script will store in the database the new event, depending on what kind of data is transmitted. On the other hand, the script keeps a relation between the database and the http client. The client will ask for data to be displayed and returns the data.

4. Visualization

A dashboard is connected to the database for solar and grid parameter details, providing real-time insights and analytics for net zero sustainability.

Features:

• User-friendly dashboard
• Drill Down capabilities
• Real-time Energy Monitoring
• Data Integration
• Location-based Insights
• Simulation

Simulation

Our simulation feature allows users to model two critical types of interventions against this established baseline to predict the precise impact on energy balance and Co2 emissions.

Our simulation feature uses a systems-based energy balance model that integrates key variables such as resource consumption, operational efficiency, and emission factors. By establishing a dynamic baseline, the model evaluates how different interventions influence overall energy use and CO₂ output. It uses scenario modeling and predictive analytics to quantify the impact of two types of interventions.

1. Adding Trees:

Action: Users can specify count of trees

Prediction: The simulation models then expected carbon reduction percentage by absorbing Co2.

2. Capacity Planning (e.g., Upgrading Solar Inverters/Panels):

Action: Users can input a proposed increase in solar inverter capacity or the number of new panels.

Prediction: The simulation models the increased solar energy generation potential against the building’s historical consumption patterns, resulting in decrease of Co2 emission percentage.

Video

Note: In the actual deployment, we used the FG28 module. However, for the demo video, we used the ZG28 module because the FG28 units are already deployed in the field. The ZG28 is being used solely for demonstration purposes to show how we read data from the energy meter using RS485.