Short-Term Load Forecasting

Short-term load forecasting generally encompasses 24-hour daily load predictions and 168-hour weekly load forecasts. The primary objective of this study is to predict average daily load patterns to gain better insights into energy consumption trends. To achieve this goal, we will employ advanced data analysis techniques such as machine learning and deep learning algorithms, implemented through Python libraries like scikit-learn and TensorFlow. Key factors including weather conditions and population mobility patterns will be integrated into our models using feature engineering techniques to enhance prediction accuracy. Through this methodology, we aim to provide energy planners and decision-makers with comprehensive and detailed information, supporting more effective policy formulation and strategic planning. The implementation involves data preprocessing, model training with historical load data, and validation using cross-validation techniques to ensure robustness.