Energy Consumption Anomaly Detector Workflow
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Detect anomalies in energy consumption data using an n8n workflow. This workflow leverages Langchain nodes for text splitting, embeddings, and Supabase vector storage to identify unusual patterns.
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About This Workflow
Overview
This n8n workflow is designed to detect anomalies in energy consumption data. It utilizes a combination of Langchain and n8n-base nodes to process incoming energy data, create embeddings, store them in a Supabase vector database, and then query this database to identify any unusual or anomalous consumption patterns. The workflow is triggered via a webhook, making it suitable for real-time data analysis.
Key Features
- Real-time Anomaly Detection: Detects anomalies in energy consumption data as it arrives.
- Langchain Integration: Leverages powerful Langchain nodes for text processing, embeddings, and agent-based reasoning.
- Vector Database Storage: Utilizes Supabase as a vector store for efficient similarity search and retrieval.
- Webhook Trigger: Can be easily integrated with external systems via a webhook for automated data ingestion.
- Logging: Logs detected anomalies and relevant information to a Google Sheet.
How To Use
- Set up Webhook: Configure the 'Webhook' node to receive incoming energy consumption data.
- Split Text Data: Use the 'Splitter' node to chunk the incoming data for processing.
- Generate Embeddings: Employ the 'Embeddings' node (HuggingFace) to create numerical representations of the data.
- Insert into Vector Store: Use the 'Insert' node to store these embeddings in your Supabase vector database.
- Query for Anomalies: When new data arrives (or at intervals), use the 'Query' node to search for similar patterns or anomalies in the Supabase vector store.
- Agentic Reasoning: The 'Tool' and 'Agent' nodes orchestrate the process, potentially using the 'Chat' node to interpret results and make decisions.
- Log Results: Configure the 'Sheet' node to log any detected anomalies to a Google Sheet for further review.
Apps Used
Workflow JSON
{
"id": "cf1be4e2-d580-4d25-b614-7cf1783df855",
"name": "Energy Consumption Anomaly Detector Workflow",
"nodes": 0,
"category": "Data Analysis & Anomaly Detection",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: cf1be4e2-d580...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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