katosteven/2500726350-ML_Project

Regional Electricity Load Prediction Using Hybrid Machine Learning Models Under Limited Data Conditions: A Case Study of Uganda # 2500726350-ML_Project Regional Electricity Load Prediction Using Hybrid Machine Learning Models Under Limited Data Conditions: A Case Study of Uganda This project addresses the challenge of accurately forecasting regional electricity consumption in environments with limited historical data. It demonstrates a methodical approach to time-series forecasting using a diverse set of machine learning models. Project Highlights * Data-Efficient Forecasting: Developed and evaluated models for short-term electricity load prediction using sparse historical datasets. * Comprehensive Model Evaluation: Assessed the performance of various models, including SARIMAX, Linear Regression, Random Forest, XGBoost, LSTM, and a hybrid ensemble. * Emphasis on Feature Engineering: A core finding is the critical impact of meticulous feature engineering, which enabled a simpler, interpretable Linear Regression model to outperform more complex models. * Practical Insights: Provides valuable strategies for achieving robust predictive accuracy in data-constrained time-series analysis. Technologies Used * Python (Pandas, NumPy, Scikit-learn, Statsmodels) * XGBoost * TensorFlow/Keras * Matplotlib, Seaborn How to Explore Refer to the notebook for detailed code implementation, data preprocessing, feature engineering steps, model training, and evaluation results. The ProjectReport.pdf provides a comprehensive overview and discussion of the findings.