Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Choi, Seung Yeoun; Kim, Sean Hay

doi:10.3390/en15186620

Cited by 4 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Galli et al [147] proposed a multi-step methodology based on a clustering algorithm and a LIME to investigate the energy per-formance classes of buildings using a large set of energy performance certificates (EPCs) as input. Choi and Kim [148] investigated the performances of clustering and tree-based algorithms to evaluate energy-efficiency buildings (EBB). they found that the conditional inference tree (CIT) algorithm performed best in terms of interpretability and classification accuracy compared to other decision-tree and clustering models.…”

Section: Clustering and Feature Extractionmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

View full text Add to dashboard Cite

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

show abstract

Section: Clustering and Feature Extractionmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

View full text Add to dashboard Cite

show abstract

Preparation of an Indoor Air Quality Baseline Model for School Retrofitting Using Automated and Semi-Automated Calibrations: The Case Study in South Korea

Sung

Kim

Choi³

2022

Buildings

View full text Add to dashboard Cite

School retrofitting should aim to not only improve its energy performance, but also maintain a good IAQ. An optimal combination of retrofitting measures must be selected by considering the transient state changes of the outdoor and built environments. Although a simulation is an effective platform to evaluate a combination of the retrofitting measure candidates, there is a general lack of practical methods for practitioners to collect the field data and prepare a reliable IAQ baseline model within a project timeline. This study suggests a suite of tools to generate a classroom IAQ baseline, which includes standardized diagnostic scenarios based on common retrofitting practices and measurement protocols of classroom IAQs; the diagnostic scenarios intend to quantify the dilution and filtration capabilities of classrooms through deposition, infiltration, and natural/mechanical ventilations when a high concentration is observed; the first principle model is developed to normalize the measurement, which is fitted against the measurement by adjusting its parameter values. In order to save time and effort for practitioners, automated and semi-automated calibrations that run in a short time are also developed. While the automated calibrations performed better in some cases, the semi-automated calibrations performed better than the automated ones in many cases, the CV-RMSE were smaller, by between −7% and −0.5%. Meanwhile, it took a comparably larger effort and longer time (>1 h for the worst cases) for the heuristic calibrations to have a similar accuracy with the machine-driven calibrations. If the model structure suffers a problem with the measurement, the modeler must intervene in the calibrations. In this case, semi-automation can be a diagnostic tool for a practitioner to intuitively determine from which variables to start the calibration.

show abstract

MODI: A Structured Development Process of Mode-Based Control Algorithms in the Early Design Stage of Building Energy Systems

et al. 2023

View full text Add to dashboard Cite

The growing share of renewable energy sources in building energy systems leads to more complex energy conversion and distribution systems. The current process of developing appropriate control functions for energy systems is insufficient and consequently error-prone. Regarding this problem, a new method is expected to systematically develop appropriate control functions for buildings and reduce design errors in this process. This paper introduces the MODI method, aiming at a structured development process of mode-based control algorithms to reduce errors in the early design stages of buildings. A complete framework and a standardized application process of the MODI method will be established to systematically design mode-based control algorithms described through signal-interpreted Petri nets. Furthermore, we performed a simulation-assisted evaluation approach to test and improve the performance of the control algorithms generated by MODI. In a case study, we applied MODI to develop a mode-based control strategy for an energy system containing heating and cooling supply networks. The desired control strategy was tested and tuned in a simulation phase. Compared to a reference control, the mode-based control algorithm shows an improvement in system efficiency by 4% in winter and 8% during the transitional season phase.

show abstract

Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Cited by 4 publications

References 56 publications

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Preparation of an Indoor Air Quality Baseline Model for School Retrofitting Using Automated and Semi-Automated Calibrations: The Case Study in South Korea

MODI: A Structured Development Process of Mode-Based Control Algorithms in the Early Design Stage of Building Energy Systems

Contact Info

Product

Resources

About