Evaluation methodology of Model Predictive Controllers for building's energy systems

TL;DR

This paper presents a comprehensive methodology for evaluating and ranking Model Predictive Controllers (MPCs) in building energy systems using a test protocol, scenarios, and performance indicators, demonstrated through a case study with a digital twin.

Contribution

It introduces a systematic evaluation framework for MPCs in building energy management, including test protocols, scenarios, and performance metrics, validated by a practical case study.

Findings

The methodology effectively tests and ranks MPCs in various scenarios.

The case study demonstrates the approach's ability to evaluate controller performance.

The framework provides valuable feedback for optimizing building energy control.

Abstract

Climate change poses a serious threat to the Earth's ecosystems, fueled primarily by escalating greenhouse gas emissions. Among the main contributors, the building sector stands out due to its significant energy demand. Addressing this challenge requires innovative techniques in the control of energy systems in buildings. This paper deals with the formulation of a methodology designed to evaluate the performance of these controllers. The evaluation process involves the establishment of a comprehensive test protocol and a diverse set of scenarios to evaluate the controllers. Key performance indicators are used to quantify their effectiveness based on the test results. A practical case study is presented as an application to introduce this methodology, focusing on the integration of Model Predictive Controllers (MPCs) with the Dimosim thermal simulation platform. The digital twin of the Greener building in Grenoble is used as a model for emulation. The paper demonstrates the ability of the proposed methodology to test and rank MPCs in different test scenarios, providing valuable feedback on their performance capabilities. The paper highlights the importance of the developed approach in systematically evaluating and ranking MPCs for optimized building energy management.