Wind Power Grid Analytics and Future Power Requirement Prediction
This AI/ML project aims to optimize wind power grid efficiency by predicting future power requirements for cities. It leverages statistical modeling techniques such as regression analysis, ARIMA, and neural networks to analyze data from weather stations, wind farms, and city power grids. By examining wind velocity, atmospheric temperature, and historical power consumption, the system forecasts energy needs accurately. The goal is to enhance grid management, support sustainable energy use, and reduce reliance on fossil fuels.
Industry : AgriTech
Manpower : 50+
Location : India
Technical Solution
01. Data Collection
Data is sourced from weather stations (wind velocity and atmospheric temperature), wind farms (energy production data), and city power grids (historical power consumption).
02. Data Preprocessing
The collected data is cleaned, normalized, and merged to form a comprehensive dataset.
03. Feature Engineering
Key features are extracted and engineered to improve the model's predictive accuracy.
04. Model Development
Various AI models are developed and trained.
05. Model Evaluation
The models are evaluated using metrics such as RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error) to ensure high accuracy.
06. Deployment
The best-performing model is deployed on a cloud-based platform for real-time prediction and monitoring.
Statistical Models Used
- Multiple Regression Analysis
- ARIMA (AutoRegressive Integrated Moving Average)
- LSTM (Long Short-Term Memory) Networks
Data Sources
- Weather Stations: Wind velocity and atmospheric temperature data
- Wind Farms: Real-time energy production data
- City Power Grids: Historical power consumption data