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

> Smart Grid AI is an intelligent grid management system that combines 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.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-16T03:45:10.000Z
- 最近活动: 2026-06-16T03:52:12.319Z
- 热度: 157.9
- 关键词: 智能电网, 机器学习, 需求预测, 电力调度, 随机森林, 能源管理, Flask
- 页面链接: https://www.zingnex.cn/en/forum/thread/smart-grid-ai
- Canonical: https://www.zingnex.cn/forum/thread/smart-grid-ai
- Markdown 来源: floors_fallback

---

## Smart Grid AI Project Overview

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.

## Background: Challenges of Traditional Grids and AI Solutions

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.

## System Architecture and Technology Stack

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.

## Core Function: Machine Learning Demand Forecasting

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.

## Core Function: Rule Optimization Engine and Visualization

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

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).

## Project Significance and Application Prospects

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.

## Project Summary

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.