Smart Grid AI: Machine Learning-Based Smart Grid Demand Forecasting and Power Dispatch System

Smart Grid AI is an intelligent grid management system combining machine learning and rule optimization algorithms. It predicts power demand using a random forest regression model and achieves efficient multi-region power allocation based on priority rules. The project is developed and maintained by dhej84, with source code hosted on GitHub (hackfest26-smart-grid, released on June 16, 2026). It is an end-to-end smart grid management demonstration system.

With the large-scale integration of renewable energy and the growth of power demand, traditional grids face issues such as supply volatility, demand uncertainty, and complex multi-region coordination. AI technologies (machine learning + optimization algorithms) provide new ideas to solve these problems, which can improve grid operation efficiency and reduce overload risks. Smart Grid AI is a practice of this idea.

The system adopts a front-end and back-end separation architecture: the back-end uses Python+Flask (RESTful API), Scikit-learn (random forest model), Pandas+NumPy (data processing); the front-end uses HTML+CSS+JS, Bootstrap (responsive layout), Chart.js (visualization). The technology selection balances development efficiency and functional integrity.

The demand forecasting module is based on the random forest regression algorithm. The features used include time (hours of the day), meteorology (temperature), supply (renewable energy/grid supply), and region (target region identifier). Model performance: Mean Absolute Error (MAE) is about 6MW, and the coefficient of determination (R²) is about 0.97, which can support dispatch decisions.

Optimization engine strategies: 1. Priority evaluation (ensure critical area load); 2. Available power allocation (optimal solution calculation); 3. Minimize overload risk; 4. Improve grid utilization. The system provides a web dashboard that displays real-time supply/demand monitoring, prediction curves, regional allocation analysis, alarm information, etc.

Data processing flow: 1. Data collection (read simulated CSV dataset); 2. Demand forecasting (ML model outputs future demand for regions); 3. Optimization calculation (solve optimal allocation under rule constraints); 4. Power dispatch (generate execution instructions); 5. Monitoring and alarm (track execution anomalies); 6. Visualization display (dashboard presents key indicators).

Significance: Technical verification (feasibility of combining ML and optimization in the dispatch field), education and training (clear architecture suitable for teaching cases). Future expansion directions: real-time smart meter integration, IoT monitoring, real-time data stream processing, renewable energy forecasting, smart city deployment.

Smart Grid AI demonstrates the application potential of AI in the smart grid field. Through precise ML prediction and rule optimization, it provides a feasible technical path for the intelligent transformation of grids. For researchers in energy internet, smart grids, or ML applications, it is an open-source project worth referencing.