Edge AI Empowers Solar Energy Prediction: A Practical Exploration of TinyML Based on ESP32-S3

This project explores deploying a GRU neural network on the ESP32-S3 microcontroller, using TinyML technology to achieve localized one-hour solar power generation prediction, covering the entire process of model training, optimization, and embedded deployment. The edge AI model solves the problems of cloud dependency and data privacy, providing practical references for intelligent applications in resource-constrained scenarios.

Solar power generation efficiency is affected by multiple factors such as weather, season, and time. Accurate prediction has important economic value for grid dispatching, energy storage management, and energy trading. This project chooses a one-hour prediction window (balancing practicality and feasibility). After comparing simple RNN (vanishing gradient), LSTM (large number of parameters), and GRU (balancing accuracy and complexity), GRU was selected as the deployment solution.

The project uses PVGIS synthetic data (10-minute intervals, including meteorological and power generation parameters). Preprocessing steps: interpolation to fill missing values, statistical methods to correct outliers, normalization to unify feature ranges. Feature engineering innovation: Using sine/cosine transformations to encode hours and months helps the model capture time periodicity (e.g., 23:00 is adjacent to 00:00, December is connected to January).

GRU only contains update gates and reset gates, with about 25% fewer parameters than LSTM, making it suitable for embedded environments. Training is evaluated using RMSE (average deviation), MAE (error magnitude), and R² (goodness of fit). Results show that the loss decreases steadily, the training/validation sets are synchronized without overfitting, and the predicted values match the real values well.

The model is converted via TensorFlow Lite (graph optimization) and quantized (32-bit to 8-bit, volume reduced to 1/4, inference speed increased by 2-4 times). The ESP32-S3 hardware (dual-core 240MHz, 512KB SRAM + 8MB PSRAM) supports operation, but memory allocation needs to be optimized to avoid overflow, and the inference loop needs to be optimized to reduce overhead.

Project demonstration value: Autonomous optimization of power generation strategies in the energy sector, prediction of greenhouse microclimates in agriculture, and predictive maintenance of equipment in industry. It solves the pain points of privacy (data not uploaded) and network dependency (still runs offline). Future direction: NAS and AutoML will promote lighter and more efficient edge AI models.