Automated Solar Output Forecasting with AI and Vector Stores

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.