Electrical Energy Demand Forecasting using Time Series in LSTM and CNN-LSTM Models in Deep Learning Applications

Introduction: Forecasting electrical energy demand is crucial for predicting future energy consumption patterns, which aids in effective energy management and distribution. Various forecasting methods have been developed, yet this study explores univariate time series analysis using Bidirectional Long Short-Term Memory (BiLSTM) and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model. These deep learning techniques are designed to capture both temporal dependencies and spatial patterns, improving predictive performance in energy forecasting. Objectives: This study aims to evaluate the forecasting performance of deep learning models in univariate time series energy demand prediction. Specifically, it seeks to: Implement and compare the forecasting performance of Bidirectional LSTM and hybrid CNN-LSTM models using a publicly available dataset from Transmission Service Operators (TSO). Preprocess the dataset using appropriate data preparation techniques, such as normalization, handling missing values, and feature selection, before training the models. Assess predictive accuracy by evaluating both models using key performance metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-Squared (R²). Methods: The dataset used in this study was obtained from a public portal for Transmission Service Operators (TSO). Before training, the data underwent preprocessing techniques such as normalization, handling missing values, and feature selection to improve model performance. Two deep learning models—BiLSTM and CNN-LSTM—were implemented and trained on the dataset. The performance of each model was evaluated using four key metrics: Mean Absolute Error (MAE) – measures the average magnitude of errors, Mean Absolute Percentage Error (MAPE) – represents error as a percentage of actual values, Root Mean Squared Error (RMSE) – penalizes larger errors more heavily than MAE, R-Squared (R²) – indicates how well predictions align with actual data. Results: Experimental findings reveal that the hybrid CNN-LSTM model outperformed the BiLSTM model across all evaluation metrics. The CNN-LSTM model achieved a lower MAE of 499.08 compared to 780.56 in BiLSTM, a lower MAPE of 1.80% versus 2.52%, and a reduced RMSE of 671.37 compared to 1,042.20. Additionally, the CNN-LSTM model obtained a slightly higher R² score of 0.97 compared to 0.94 in BiLSTM, indicating a better fit for the data. Conclusion: The results demonstrate that integrating CNN with LSTM significantly improves predictive accuracy in univariate time series energy demand forecasting. The CNN component enhances feature extraction, allowing the LSTM layers to capture complex temporal dependencies more effectively. Consequently, the hybrid CNN-LSTM model emerges as a more robust approach compared to BiLSTM alone, making it a valuable tool for accurate energy demand forecasting. Further research can explore additional deep learning architectures or hybrid models to optimize forecasting performance further.