Net-zero ready building control: Benchmarking Decision Transformers against deep reinforcement learning for PV–battery buildings

The integration of Building-Integrated Photovoltaic and Battery (BIPVB) systems is a critical pathway toward carbon-neutral buildings, yet their optimal control is challenged by the stochastic nature of renewable generation and complex load dynamics. While Deep Reinforcement Learning (DRL) has emerged as a promising model-free control approach, traditional value-based and policy-gradient methods often suffer from training instability and lack explicit long-horizon optimization capabilities. This study explores the application of the Decision Transformer (DT) to Building-Integrated Photovoltaic and Battery (BIPVB) control by reformulating the task as a return-conditioned sequence modeling problem. By leveraging the Transformer architecture, the proposed method treats the control problem as a supervised learning task, predicting optimal actions based on historical states and desired future returns (Return-to-Go). We conduct a comprehensive benchmark using the CityLearn dataset, comparing DT against a Rule-Based Controller (RBC) and six state-of-the-art DRL algorithms, including SAC, PPO, and TD3. Simulation-based benchmark results show that the DT agent achieves the lowest operational cost across the tested scenarios, yielding an 18.5% cost reduction compared to RBC and outperforming the strongest DRL baseline. Furthermore, the DT shows competitive and relatively consistent performance under varying initial battery conditions. These results suggest that sequence-modeling approaches are a promising direction for offline building energy control and merit further investigation in broader settings.