Smart Home Energy Consumption Forecasting using CNN-Based Hybrid Deep Learning Models

A Accurate forecasting of household energy consumption is critical component of smart home energy management systems, enabling efficient energy utilization, cost reduction, and demand-side planning. This work presents a deep learning–based framework for short-term energy consumption forecasting using real-world household power usage data. The proposed approach integrates one-dimensional Convolutional Neural Networks (CNN) with sequential learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Units (GRU)—to capture both local temporal patterns and long-term dependencies in time-series energy data. The dataset is preprocessed through hourly resampling and Min–Max normalization, followed by sequence generation using a 24-hour sliding window. Model performance is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) on unseen test data. Comparative analysis demonstrates that hybrid CNN-based architectures outperform standalone temporal models by effectively learning spatial–temporal features. Among the evaluated models, the best-performing architecture achieves the lowest RMSE and MAE, indicating strong predictive capability. The results confirm the suitability of CNN RNN hybrid models for smart home energy forecasting and provide a scalable foundation for future intelligent energy management systems. Key Words: Smart Home Energy Management, Energy Consumption Forecasting, Time Series Prediction, Deep Learning, Convolutional Neural Network (CNN), LSTM, BiLSTM, GRU, Hybrid Models, Real-Time Energy Data, RMSE, MAE