Deep Learning-Based Optimization of Household Energy Consumption: A Data-Driven Approach to Smart Home Forecasting

From Table 1, we can see the comparative evaluation of multiple deep learning architectures underscores the critical role of model complexity, regularization techniques, and architectural enhancements in accu- rately forecasting household energy consumption pat- terns. Root Mean Square Error (RMSE), a robust in- dicator of predictive performance, was utilized as the primary evaluation metric across all experiments. Among the models tested, the Multi-Layer Per- ceptron (MLP) achieved the lowest RMSE of 0.6648, indicating its superior ability to capture the nonlinear relationships and temporal dependencies inherent in the household energy dataset. This per- formance suggests that MLPs, with their relatively simple yet expressive structure, strike an effective bal- ance between underfitting and overfitting when ap- propriately configured. The Wider Neural Network with Batch Normalization also demonstrated competitive per- formance (RMSE = 0.6854), benefiting from en- hanced gradient stability and faster convergence due to batch normalization layers. This suggests that ar- chitectural modifications aimed at improving internal covariate shift can lead to meaningful gains in model generalization. The Deep Neural Network with Dropout (RMSE = 0.7061) and Simple Neural Network (RMSE = 0.7431) showed reasonable accuracy but were slightly outperformed by their deeper and more regularized counterparts. The modest improvements in RMSE underscore the importance of network depth and dropout-based regularization in mitigat- ing overfitting and enhancing predictive robustness. In contrast, the Conditional Restricted Boltz- mann Machine (CRBM) and its variant FCRBM yielded substantially higher RMSE values (0.9648 and 1.1099, respectively), reflecting limita- tions in their ability to model the complex dependen- cies present in the data. This disparity may be at- tributed to challenges in training energy-based mod- els, their sensitivity to hyperparameters, and their reliance on sampling-based inference methods, which can hinder scalability and convergence. Overall, the experimental findings suggest that feedforward neural architectures, particularly those leveraging layer-wise depth and normaliza- tion strategies, offer a more pragmatic and scalable solution for household energy modeling. These mod- els not only achieve lower prediction errors but also exhibit greater ease of training and integration within broader energy optimization frameworks. Future re- search may explore hybrid approaches that combine the expressive power of generative models with the stability of supervised networks to further enhance performance in dynamic residential energy environ- ments.

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