In this project, titled "Energy Price Prediction using Machine Learning," I have undertaken an in-depth analysis of various machine learning techniques for accurate and efficient energy price forecasting. The primary objective is to develop robust predictive models and compare their performance to identify the most effective approach. Specifically, I have implemented and evaluated three regression models: Linear Regressor, Random Forest Regressor, and XGBoost Regressor.
The Linear Regressor is a fundamental model in machine learning that assumes a linear relationship between the input features and the target variable. It fits a straight line to the training data by estimating the coefficients through a process called ordinary least squares. While the Linear Regressor may not capture intricate nonlinearities in the data, it provides valuable insights into the relationship between input variables and energy prices. It is computationally efficient and interpretable, making it an excellent starting point for our analysis.
The Random Forest Regressor is an ensemble model that combines the predictions of multiple decision trees to make accurate forecasts. By constructing a forest of decision trees using different subsets of the data and random subsets of features, the Random Forest Regressor overcomes the limitations of the Linear Regressor. It captures complex interactions and nonlinear relationships in the data, leading to improved prediction accuracy. Moreover, the model's ensemble nature allows it to handle outliers and reduce overfitting.
The XGBoost Regressor, an optimized implementation of gradient boosting, is a state-of-the-art algorithm renowned for its exceptional performance in various machine learning tasks. It employs an ensemble of weak learners, typically decision trees, in a sequential manner. The model gradually minimizes the errors by focusing on the examples that were previously mispredicted. XGBoost incorporates regularization techniques and hyperparameter tuning to enhance the model's generalization capability and prevent overfitting. It handles missing values and categorical variables effectively, making it a powerful tool for energy price prediction.
In this project, I have extensively evaluated these three regression models using appropriate performance metrics such as mean squared error, mean absolute error, and R-squared. I have also considered factors like training time, interpretability, and robustness to outliers. By conducting a comprehensive analysis, I aim to determine the model that achieves the highest prediction accuracy while considering practical considerations.
The insights gained from this project have implications for energy market participants, including traders, investors, and policymakers. Accurate energy price predictions enable them to optimize trading strategies, mitigate risks, and make informed decisions. By leveraging machine learning techniques, we can enhance the efficiency and reliability of energy price forecasting, contributing to a more sustainable and resilient energy market.