Uncertainty-Aware Energy Consumption Forecasting Using LSTM Networks with Monte Carlo Dropout

Accurate forecasting of energy consumption is critical for effective resource management and sustainability in the energy sector. This paper presents an uncertainty-aware deep learning approach using Long Short-Term Memory (LSTM) networks with Monte Carlo Dropout to enhance prediction accuracy and quantify uncertainty. Our model is trained on hourly energy consumption data from the PJM Electricity Market (2015–2020), preprocessed via temporal feature engineering (hour-of-day, day-of-week, month), linear interpolation for missing values, and Z-score-based outlier removal. The proposed framework achieves RMSE: 5005.93, MAE: 4063.75, and MAPE: 13% on the test set, outperforming benchmark models like ARIMA (RMSE: 6500) and Exponential Smoothing (RMSE: 7200). By integrating Monte Carlo Dropout during inference, we generate probabilistic forecasts with 95% confidence intervals, enabling stakeholders to assess prediction reliability. Cross-validation results (average RMSE: 16015.68) highlight the model’s robustness to temporal variability. Our work demonstrates that LSTM networks with uncertainty quantification significantly improve energy forecasting accuracy, offering actionable insights for grid management and policy decisions.