Evaluating Machine Learning and Heuristic Optimization Based Surrogates as a Replacement for a Complex Building Simulation Model

Kathrin Kefer, Samuel Haijes, Michael Mörth, Richard Heimrath, Thomas Mach, Valentin Kaisermayer, Christopher Zemann, Daniel Muschick, Bogdan Burlacu, Stephan Winkler, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

Intelligent energy management systems can play a vital role in supporting the much needed energy transition. However, in order to train machine learning models for this task, often very complex and detailed simulation models are needed. This can make the overall training process very slow or even impossible, which is why using resource efficient surrogates of the original simulation model during the training can be a possible solution. This work therefore focuses on the training of surrogates of a very detailed building simulation model using three different algorithms (k-Nearest Neighbour, Random Forest and Genetic Algorithm) and evaluates and compares them for their prediction capabilities, learned behaviours as well as execution time. Results show that the Random Forest algorithm achieves the best overall performance for 28 of the 35 surrogates, can learn the expected behavior and improves the execution speed by a factor of up to 664 compared to the original IDA ICE simulation model.

Original languageEnglish
Title of host publication11th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2023
EditorsAgostino G. Bruzzone, Janos Sebestyen Janosy, Letizia Nicoletti, Gregory Zacharewicz
PublisherCal-Tek srl
ISBN (Electronic)9788885741980
DOIs
Publication statusPublished - 2023
Event11th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2023 - Athens, Greece
Duration: 18 Sept 202320 Sept 2023

Publication series

NameProceedings of the International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE
Volume2023-September
ISSN (Print)2724-0061

Conference

Conference11th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2023
Country/TerritoryGreece
CityAthens
Period18.09.202320.09.2023

Keywords

  • Building Simulation Model Surrogates
  • Energy Management System
  • Heuristic Optimization
  • Machine Learning

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