Accurate energy consumption forecasting is essential for efficient power system operation, grid stability, and cost management. With billions of smart meters now deployed globally, utilities have access to high-resolution consumption data that enables data-driven prediction models. However, little empirical evidence exists on which deep learning approaches perform best on real-world smart meter data.
Researchers conducted a comprehensive benchmark study comparing nine modern deep learning models, including linear regression, multilayer perceptrons, convolutional neural networks, and Transformer architectures. The evaluation used two publicly available smart meter datasets and examined three critical factors: historical input length, prediction horizon, and model architecture choice.
Key findings revealed that extending historical context improves forecast accuracy, but gains plateau beyond a certain point—additional historical data provides diminishing returns. Conversely, accuracy consistently degrades as forecasting horizons lengthen, a challenge common to time-series prediction tasks. Importantly, lightweight architectures delivered prediction quality comparable to complex deep learning models while requiring substantially less computational resources, making them attractive for utility deployments.
Deep learning models outperformed classical statistical baselines across all tests. However, architectural differences became meaningful only at longer prediction horizons and on heterogeneous datasets. Attention-based models showed modest advantages for underrepresented or behaviorally irregular customer groups, though these benefits were not statistically robust across datasets.
The research provides actionable guidance for utilities selecting forecasting solutions. For most standard applications, simpler, computationally efficient models suffice. The analysis demonstrates that model complexity does not guarantee superior performance and must be weighed against deployment costs, latency requirements, and maintenance demands in operational environments. These insights help utilities balance forecast accuracy with practical implementation constraints when modernizing energy management systems.
