Wind Energy Analysis and Prediction using LSTM

This project focuses on analyzing and predicting wind energy production using Long Short-Term Memory (LSTM) networks. It leverages historical wind speed data to forecast future wind energy outputs, aiming to improve the reliability and efficiency of wind energy systems.

Objectives

• Analyse Wind Speed Data: Gain insights into patterns and trends in wind speed data.
• Develop Predictive Models: Build LSTM models to predict future wind energy production based on historical data.
• Improve Energy Forecasting: Enhance the accuracy of wind energy predictions to optimize energy management.

Methodology

1. Data Preprocessing:

• Cleaning and preparing wind speed data for analysis.
• Handling missing values and normalizing the data.

2. Exploratory Data Analysis (EDA):

• Visualizing data trends and distributions.
• Identifying key features that influence wind energy production.

3. Model Development:

• Building LSTM neural networks tailored for time-series forecasting.
• Training and validating models using historical wind speed data.

4. Results Visualization:

• Comparing predicted vs. actual energy outputs.
• Visualizing model performance through plots and charts.
  Outcomes
• Accurate Predictions: The LSTM model provides accurate predictions of future wind energy production, which can be used to optimize energy resource allocation.
• Enhanced Maintenance: Predictive maintenance of wind turbines can be improved based on the forecasted data.
• Energy Efficiency: Optimizing the use of wind energy resources leads to better energy management and sustainability.