Automated Solar Output Forecasting with AI and Vector Stores
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This n8n workflow leverages AI and Supabase vector stores to automate solar energy output forecasting. It uses Langchain nodes to process data and an agent to provide predictions via a webhook.
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About This Workflow
Overview
This n8n workflow is designed to automate the forecasting of solar energy output. It utilizes a combination of AI and vector store technology to process incoming data, generate predictions, and log the results. The core logic involves receiving data via a webhook, splitting it into manageable chunks, creating embeddings for each chunk using HuggingFace, and storing these embeddings in a Supabase vector database. When a query is made (implicitly through the agent's interaction), it retrieves relevant information from the vector store, combines it with chat history and an AI model, and ultimately provides a forecast. This is particularly useful for energy management systems, smart grids, and renewable energy providers looking to optimize operations and predict energy generation.
Key Features
- Webhook Integration: Accepts incoming solar data through a webhook for real-time processing.
- AI-Powered Data Processing: Utilizes Langchain nodes for text splitting, embedding generation, and agent-based decision making.
- Vector Store Persistence: Stores data embeddings in Supabase for efficient retrieval and querying.
- Conversational AI Agent: Employs an AI agent with memory to provide intelligent solar output forecasts.
- Automated Logging: Logs the forecasting process and results to a Google Sheet for analysis.
How To Use
- Configure Webhook: Set up the incoming webhook endpoint to receive solar data.
- Set Up Credentials: Configure HuggingFace API and Supabase credentials within n8n.
- Define Vector Store: Ensure your Supabase instance is set up with the
solar_output_forecasterindex. - Configure AI Nodes: Adjust parameters for text splitting, embeddings (e.g., model choice), and the AI agent's prompt.
- Set Up Google Sheets Logging: Configure the Google Sheets node with your sheet ID and name for logging.
- Activate Workflow: Trigger the webhook with your solar data to initiate the forecasting process.
Apps Used
Workflow JSON
{
"id": "896cf0b4-6f70-4954-a8da-e41047a988e7",
"name": "Automated Solar Output Forecasting with AI and Vector Stores",
"nodes": 0,
"category": "Energy Automation",
"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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