eureka-viraj / wind-direction-and-speed-prediction

Predicting wind speed and direction using machine learning in R. Enhance wind energy planning and weather forecasting by optimizing wind turbine placement. Explore various algorithms including Linear Regression, Random Forest, XGBoost, and SVM for accurate predictions.

Wind Speed and Wind Direction Prediction Project

Introduction

This project focuses on predicting wind speed and wind direction using data science and machine learning techniques. The ability to forecast these parameters is crucial for various applications, including wind energy planning and weather prediction. By accurately predicting wind conditions, we can optimize the placement of wind turbines for maximum power generation.

Table of Contents

• Introduction
• Project Overview
• Dataset
• Data Preprocessing
• Model Training
• Model Evaluation
• Algorithms
• Results
• Deployment
• Contributing
• License

Project Overview

• Problem Statement: Wind speed and direction prediction for wind energy planning.
• Data Source: Historical wind data.
• Programming Language: R
• Libraries: dplyr, ggplot2, caret, randomForest, xgboost, e1071
• Models: Linear Regression, Random Forest, XGBoost, Support Vector Machine (SVM)

Dataset

The dataset used for this project contains historical wind data, including wind speed, wind direction, temperature, humidity, and other relevant features. The dataset is stored in the file dataset.csv.

Data Preprocessing

• Handling missing values.
• Normalizing or scaling the data.
• Creating new features, such as wind vector components (U and V).

Model Training

We utilized several machine learning algorithms for wind speed and direction prediction:

• Linear Regression
• Random Forest
• XGBoost
• Support Vector Machine (SVM)

We split the data into training and testing sets, trained each model, and fine-tuned hyperparameters as needed.

Model Evaluation

We evaluated model performance using Mean Absolute Error (MAE) as a metric. Each model's predictions were compared to actual values to assess accuracy.

Algorithms

• Linear Regression for baseline modeling.
• Random Forest for ensemble learning and feature importance.
• XGBoost for gradient boosting.
• SVM for non-linear modeling.

Results

• The project achieved satisfactory results in wind speed and direction prediction.
• Models can be used for non-extreme weather conditions and wind energy planning.
• Further improvements can be made with larger datasets and considering additional weather features.

Deployment

The trained models can be deployed for real-time predictions in wind energy planning applications.

Contributing

Contributions to this project are welcome. You can contribute by:

• Improving model performance.
• Expanding the dataset.
• Enhancing data preprocessing techniques.
• Adding more algorithms.
• Creating a user-friendly web interface.