Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System

TL;DR

This paper investigates how varying levels of data availability influence the performance of data-driven residual estimators in MPC-based building energy management systems, demonstrating that limited data can still yield effective control.

Contribution

It provides a systematic evaluation of data availability impacts on estimator and controller performance in MPC for building energy management, highlighting the benefits of pretraining with historical data.

Findings

Limited data can still achieve acceptable control performance.

Pretraining with historical data significantly improves estimator efficacy.

Simulation confirms data-driven MPC effectiveness with various data scenarios.

Abstract

A major challenge in the development of Model Predictive Control (MPC)-based energy management systems (EMSs) for buildings is the availability of an accurate model. One approach to address this is to augment an existing gray-box model with data-driven residual estimators. The efficacy of such estimators, and hence the performance of the EMS, relies on the availability of sufficient and suitable training data. In this work, we evaluate how different data availability scenarios affect estimator and controller performance. To do this, we perform software-in-the-loop (SiL) simulation with a physics-based digital twin using real measurement data. Simulation results show that acceptable estimation and control performance can already be achieved with limited available data, and we confirm that leveraging historical data for pretraining boosts efficacy.