# Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System

arXiv: https://arxiv.org/abs/2306.09080 · HTML: https://mlsanity.com/paper/2306.09080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning](https://mlsanity.com/paper/2503.11469/) (2025)

## Abstract (arXiv)

One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approach