Using GPT to Analyze IAMMETER Cloud Energy Data

📘 Introduction 🧩 Step 1 — Preparation 1️⃣ Log in to IAMMETER Cloud 2️⃣ Get Your API Token 3️⃣ Connect to GPT ⚙️ Step 2 — Retrieve Site and Meter Information 🔎 Step 3 — Select a Site to Analyze 📊 Step 4 — Run the Power Analysis 🌱 Step 5 — Example AI Energy Optimization Report ⚡ Household Energy Insights 💡 Optimization Suggestions 🧠 Step 6 — Optional Smart Control Integration 🏁 Step 7 — Summary 📎 Example Data Source ✅ Benefits of IAMMETER + GPT Integration 📚 Related IAMMETER Resources

📘 Introduction

This tutorial demonstrates how to connect your IAMMETER Cloud account to ChatGPT (or the IAMMETER Assistant), retrieve your smart energy meter data via IAMMETER’s open API, and automatically generate an AI-powered energy optimization report.

It works for all IAMMETER products, including:

🏠 WEM3080 — Single-phase home energy monitor
⚙️ WEM3080T / WEM3080TD — Three-phase energy meters
☀️ Solar PV monitoring systems

🧩 Step 1 — Preparation

1️⃣ Log in to IAMMETER Cloud

👉 https://www.iammeter.com/login

2️⃣ Get Your API Token

After logging in, click your profile icon (top-right corner)
Choose “Settings->Token”
Copy your token — it looks like this (example only):
```
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
```

3️⃣ Connect to GPT

In ChatGPT (or IAMMETER Assistant), type:

My IAMMETER API Token is xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

GPT will connect to your IAMMETER Cloud account and list all available sites and meters.

⚙️ Step 2 — Retrieve Site and Meter Information

GPT will return an overview like this:

Site Name	Type	Real-Time Power	Monthly Energy	PV Enabled
Home Energy Monitor	Single phase	2400 W	272 kWh	No
Solar PV System	PV	–870 W	211 kWh	✅ Yes

🔎 Step 3 — Select a Site to Analyze

Tell GPT:

I want to analyze the Home Energy Monitor site

GPT will ask for your meter serial number (SN). You can find it in IAMMETER Cloud → Device List, for example:

70B3D5XXXXXX

📊 Step 4 — Run the Power Analysis

GPT will call the official IAMMETER Cloud API endpoint: 👉 System API Documentation

Example:

GetPowerAnalysis(sn="70B3D5XXXXXX", startTime="2025-11-21", endTime="2025-11-28")

Example result:

Metric	Value
Average Power	497.2 W
Maximum Power	5598 W
Minimum Power	64 W
Average Daytime Power	480.8 W

🌱 Step 5 — Example AI Energy Optimization Report

Below is an example GPT-generated energy efficiency report based on IAMMETER Cloud data.

⚡ Household Energy Insights

Average load ≈ 500 W
Higher night-time load — likely from water heater or air conditioner
Peak load up to 5.6 kW — short high-consumption events detected

💡 Optimization Suggestions

Category	Recommendation	Potential Savings
Standby Power	Turn off idle plugs with smart sockets	~8–10%
Water Heater Control	Schedule operation at off-peak or solar hours	~10–15%
Peak Load Management	Avoid running multiple heavy devices at once	~5–8%

Total saving potential: ≈ 20–25% (≈ 1,700 kWh/year, ≈ $120–150 USD)

🧠 Step 6 — Optional Smart Control Integration

You can combine IAMMETER data + AI insights for automated control using open platforms:

Platform	Description
🏠 Home Assistant	Real-time control via MQTT integration
🧩 Node-RED	Build smart logic flows (e.g., cut power above 3 kW)
☁️ ThingsBoard	Create dashboards and forecast trends
🔌 WPC3700 Wi-Fi Power Controller	Use PV surplus energy for heating control

🏁 Step 7 — Summary

Step	Action	GPT Capability
1	Obtain API Token	Authenticate user
2	Connect to IAMMETER Cloud	List sites and meters
3	Provide Meter SN	Identify device
4	Run Power Analysis	Retrieve power data
5	Generate Report	AI-based energy insights
6	(Optional) Integrate Control	Enable automation

📎 Example Data Source

IAMMETER Cloud account (demo user)
Site: Home Energy Monitor
Meter SN: 70B3D5XXXXXX
Date Range: 2025-11-21 → 2025-11-28

✅ Benefits of IAMMETER + GPT Integration

Zero-code AI energy analytics
Instant optimization reports for homeowners
Seamless connection between IoT metering and AI decision-making