🧹 Data Preprocessing & Cleaning

• Excluded devices not present in the test set to prevent leakage and noise
• Aggregated 5-minute data into daily features with comprehensive statistics for voltage, current, and power factors
• Identified and filtered offline periods by detecting zero-consumption weeks
• Focused exclusively on online days to better match test distribution patterns

🌦️ Feature Engineering (Comprehensive Climate Integration)

• Climate data integration: Incorporated detailed statistics of temperature, dewpoint, precipitation, snowfall, wind components, and snow cover
• Temporal cyclical encodings: Created sine/cosine transformations for day of week, day of year, month, and week to handle cyclical patterns
• Pakistan-specific cultural features: Added holiday flags, Ramadan period indicators, and seasonal delineations specific to Kalam region
• Temperature trend analysis: Generated temperature acceleration, volatility measures, ewm averages, and extreme temperature indicators
• Heating/cooling indicators: Calculated heating and cooling degree days (base 18°C), temperature-dewpoint differences, and day-in-season positions
• Weather interactions: Modeled interactions between temperature and weekday, creating specialized features for each day of the week

🧪 Strategic Data Segmentation

• Divided the dataset into four carefully selected temporal segments:
• Data1: Late summer/early fall (August-September 2024 and October 2023)
• Data2: Winter and mid-summer (November-December 2023 and July 2024)
• Data3: Remaining periods with distinct consumption patterns
• Data4: Complete dataset for a robust global model

🧠 Advanced Ensemble Modeling

• Multi-configuration approach: Trained 7 different LGBM configurations per segment:
• Precise (conservative with deep trees)
• Feature-selective (aggressive feature selection)
• Robust (focused on outlier resistance)
• Deep forest (very deep trees with many estimators)
• Highly regularized (to prevent overfitting)
• Fast learner (high learning rate for quick convergence)
• Balanced (optimized bias-variance tradeoff)
• Bayesian optimization for weights: Used Bayesian optimization to find optimal weights for combining base models instead of a simple meta-model
• K-fold validation: Implemented 5-fold cross-validation with ensemble weight optimization per fold
• Multi-level ensemble: Combined segment-specific models with a sophisticated weighting scheme