Wind power is a clean, renewable energy source that is gaining popularity for electricity generation. However, because wind speed can be fluctuating, integrating large amounts of wind power into electrical grids can pose challenges to their stability and uncertainty. This project wants to solve this by making a model that can predict many possible outcomes. The primary goal of this project is to develop and evaluate various ML models for forecasting wind power generation over different time frames. Utilizing weather data, including wind speed and power output from wind farms, the project seeks to identify important features necessary for making both short-term and long-term forecasts.
Objectives
● To train data on different machine learning models that predict many possible outcomes for wind power.
● Perform data analysis and identify the features that are important for forecasting of wind power
● To evaluate different ML models to see which models provide the best forecasting for the wind power.
● To forecast the wind power generation for short-term and long-term durations.
● Compare the short -term and long-term forecasting and investigate which features are weighted in both durations.
● To what extent the forecasting influences the effectiveness of different ML techniques on various data sources
DataSet : https://data.open-power-system-data.org/time_series/
● Data Collection: Collect past weather data like wind speed and direction, along with how much power wind farms produced.
● Data Preprocessing: data will undergo cleaning to address missing values, outliers and normalisation.
● Model Development:
1. Use techniques like Neural Networks to start making the models.
2. Long Short-Term Memory (LSTM) and Temporal Fusion Transformers (TFT) models are well-suited for forecasting tasks like probabilistic wind and climate power prediction for short-term horizons.
3. Combine several models to get better predictions.
● Model Training and Validation: Train the models with wind power temporal data
● Performance Evaluation: Check how good the models are forecasting using specific scores that tell us how accurate the predictions are. Eg RMSE: Root Mean Square Error, CRPS: Continuous Ranked Probability Score, Cross Validation.