Saad Hayat, Aamir Nawaz, Aftab Ahmed Almani, Ehtasham Mustafa, Zahid Javid, William Holderbaum
<p>This paper compares seven forecasting models for hourly electricity consumption in a commercial office building using data spanning 2024–2025. Models include XGBoost, LSTM, GRU, 1D-CNN, SARIMA, Prophet, and Seasonal Naive baseline. Features encompass temporal indicators (hour, day of week, month), autoregressive lags (1, 2, 24, 168 h), and rolling statistics. Evaluation uses Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a 14-day test set (336 samples) with rigorous hyperparameter tuning via GridSearchCV and TimeSeriesSplit cross-validation. XGBoost achieves superior performance (MAE 6.29 kW, 3.5% MAPE) compared to GRU (10.95 kW), 1D-CNN (11.86 kW), LSTM (14.98 kW), Seasonal Naive (16.15 kW), Prophet (35.72 kW), and SARIMA (48.16 kW). Paired t-tests confirm statistical significance: XGBoost versus GRU (<span></span><math>