Smart meter data analytics for energy optimization using a deep learning approach for load profiling and consumption pattern detection

Smart meter data has become a critical asset for understanding consumption behavior and improving energy efficiency in modern smart grids. However, the complexity and high dimensionality of real-time load data require advanced analytical frameworks capable of extracting meaningful patterns, detecting anomalies, and supporting optimized decision-making. This paper presents a deep learning–based smart meter data analytics framework for load profiling and consumption pattern detection aimed at energy optimization. The proposed model integrates convolutional and recurrent neural network components to capture both short-term fluctuations and long-term temporal dependencies in consumption behavior. In addition, clustering analysis and anomaly detection techniques are incorporated to categorize consumers and identify unusual usage events that may impact grid stability. Experimental results demonstrate that the proposed deep learning framework achieves superior performance compared to traditional machine learning approaches, yielding a lower MAE of 0.146 kW and an R² score of 0.972 for short-term load prediction. The clustering model effectively groups consumers into behavior-driven categories, while the enhanced deep autoencoder detects anomalous consumption with a precision of 96.1%. The findings highlight the potential of deep learning–driven smart meter analytics to enhance energy optimization, support demand-side management, and enable more intelligent and resilient power systems.