Accurate and efficient prediction of energy consumption in district building groups is a critical foundation for effective energy management and achieving building energy-saving and emission reduction goals. In recent years, deep learning methods have been widely applied to building energy consumption prediction. However, most existing approaches rely heavily on abundant historical data for model training. In practice, data scarcity, due to factors such as data privacy concerns or newly constructed buildings, poses a significant challenge. Additionally, many methods fail to account for spatial dependencies between buildings. To address these limitations, this paper proposes a novel prediction framework based on a spatiotemporal graph convolutional network integrated with adversarial domain adaptation, specifically designed for energy consumption prediction in district building groups under data-scarce conditions. The approach combines a graph convolutional network (GCN) based on an adaptive adjacency matrix with a graph attention network based on a dynamic time warping (DTW) correlation matrix to capture the spatiotemporal features of energy consumption, across the building group. The proposed model is first pre-trained on a data-rich source domain. Then, adversarial domain adaptation is employed to extract domain-invariant spatiotemporal features from both the source and target domains. This enables a "multi-buildings to multi-buildings" transfer of predictive knowledge from the source domain to the target domain. Extensive experiments were conducted using historical energy consumption data from two real-world district building groups to validate the effectiveness and robustness of the proposed method. Results demonstrate that adversarial domain adaptation can successfully leverage domain-invariant knowledge to improve prediction accuracy in data-scarce scenarios. Compared to models trained directly on the target domain without transfer learning, the proposed method reduces prediction error by 2.36% to 16.08%. Furthermore, when benchmarked against various mainstream transfer learning methods, the adversarial domain adaptation approach more effectively bridges the domain gap, yielding superior predictive performance. Additionally, to simulate real-world data collection scenarios, the impact of varying training data ratios on the proposed model performance was also evaluated.

The complex structures of distributed energy systems (DES) and uncertainties arising from renewable energy sources and user load variations pose significant operational challenges. Model predictive control (MPC) and reinforcement learning (RL) are widely used to optimize DES by predicting future outcomes based on the current state. However, MPC’s real-time application is constrained by its computational demands, making it less suitable for complex systems with extended predictive horizons. Meanwhile, RL’s model-free approach leads to suboptimal data utilization, limiting its overall performance. To address these issues, this study proposes an improved reinforcement learning-model predictive control (RL-MPC) algorithm that combines the high-precision local optimization of MPC with the global optimization capability of RL. In this study, we enhance the existing RL-MPC algorithm by increasing the number of optimization steps performed by the MPC component. We evaluated RL, MPC, and the enhanced RL-MPC on a DES comprising a photovoltaic (PV) and battery energy storage system (BESS). The results indicate the following: (1) The twin delayed deep deterministic policy gradient (TD3) algorithm outperforms other RL algorithms in energy cost optimization, but is outperformed in all cases by RL-MPC. (2) For both MPC and RL-MPC, when the mean absolute percentage error (MAPE) of the first-step prediction is 5%, the total cost increases by ~1.2% compared to that when the MAPE is 0%. However, if the accuracy of the initial prediction data remains constant while only the error gradient of the data sequence increases, the total cost remains nearly unchanged, with an increase of only ~0.1%. (3) Within a 12 h predictive horizon, RL-MPC outperforms MPC, suggesting it as a suitable alternative to MPC when high-accuracy prediction data are limited.