arXiv subjects

Kaipeng Xu

Publications and source records attributed to Kaipeng Xu.

3 recordsLinked to original sources

arXiv2026

A Data-Driven Methodology for Scalable Distributed MPC in Heterogeneous Building Aggregation: From Systematic Feature Selection to Convex Optimization

Coordinating large-scale, heterogeneous building aggregations for demand response (DR) is impeded by a dual challenge: the computational intractability of centralized Model Predictive Control (MPC) and the inadequacy of conventional feature selection methods, which fail to address the error-compounding nature of multi-step forecasting required by MPC. This paper proposes a comprehensive, data-driven framework that first employs a systematic, MPC-aware feature selection methodology to ensure robust multi-step prediction, then models the complex building dynamics using a novel Input-Convex Encoder-Only Transformer (IC-EoT) to guarantee a convex optimization problem, and finally solves the resulting constraint-coupled problem (CCP) in a fully distributed manner using the Tracking Alternating Direction Method of Multipliers (ADMM) algorithm. The framework is validated in a high-fidelity co-simulation environment, controlling a heterogeneous aggregation of consumer and prosumer buildings based on the EnergyPlus under a dynamic time-of-use (TOU) tariff. Results demonstrate that the proposed distributed approach achieves near-identical economic optimality and superior thermal comfort compared to a theoretical centralized controller, while exhibiting exceptional computational scalability that overcomes the real-time infeasibility of the centralized approach for large aggregations.

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arXiv2026

Input Convex Encoder-Only Transformer for Computationally Efficient Model Predictive Control in Building Demand Response

Learning-based Model Predictive Control (MPC) has emerged as a powerful strategy for building demand response (DR). However, its practical deployment is often hindered by the non-convex optimization problems induced by standard neural network models. These problems lead to long solver times and a lack of global optimality guarantees, making long-horizon real-time control challenging. Because forecasting building thermal dynamics and energy consumption relies heavily on historical time-series data, Input Convex Neural Networks (ICNNs) tailored for temporal tasks have been developed to address this issue. Notable examples include Input Convex Recurrent Neural Networks (ICRNNs) and Input Convex Long Short-Term Memory (IC-LSTM). Although IC-LSTM improves the modelling capability of simpler ICRNNs, its recurrent architecture remains computationally expensive and, in the experiments reported here, exhibits numerical training instability at longer historical sequence lengths. To address these limitations, this paper introduces the Input Convex Encoder-Only Transformer (IC-EoT), which combines an encoder-only attention architecture with input convexity guarantees. IC-EoT was evaluated in a co-simulation framework using the Energym Python library and the EnergyPlus building simulator, and compared with IC-LSTM and standard non-convex models in residential and commercial testbeds. IC-EoT maintained predictive accuracy comparable to IC-LSTM, showed no non-finite loss events across 60 multi-seed training runs, and reduced mean per-epoch training time by 26.2% and 29.8% in the two testbeds. At the eight-hour prediction horizon, IC-EoT reduced mean MPC solution time by factors of 5.8 and 5.3 relative to IC-LSTM, while providing comparable closed-loop cost and comfort performance.

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arXiv2026

End-to-End Differentiable Predictive Control with Probabilistic Constraint-Satisfaction Guarantees for Building Demand Response

Building Demand Response (DR) benefits from Model Predictive Control (MPC) because thermal flexibility can be optimized over a future horizon while enforcing operational constraints, but the repeated online optimization required by MPC motivates Differentiable Predictive Control (DPC) as a computationally efficient explicit-policy alternative. Conventional DPC, however, separates model identification from policy optimization and remains sensitive to plant-model mismatch. This paper proposes an End-to-End DPC (E2E-DPC) framework that jointly optimizes the learned dynamics model and control policy using prediction, constraint, and economic objectives. Stage-wise conformal prediction (CP) calibrates recursive prediction errors to construct horizon-dependent tightened state constraints, while independent Hoeffding certification provides a high-confidence lower bound on plant-level satisfaction of the original state and input constraints under receding-horizon deployment. The framework is evaluated in a high-fidelity residential building simulation using EnergyPlus through the Energym interface and compared with online MPC and conventional DPC. Constraint tightening eliminates thermal violations for both DPC variants in the stress test and reduces violations by 99.4 percent for online MPC. Compared with conventional DPC, E2E-DPC reduces the mean constraint-tightening margin at the first prediction stage by 31.0 percent and increases the median certified plant-level lower bound from 0 to 0.814. The results further reveal a trade-off between stronger certification of plant-level constraint satisfaction and the ability to maintain nonempty tightened state sets as the prediction horizon increases, with stronger multi-step identification mitigating this trade-off.

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