Building Energy Prediction Models and Related Uncertainties: A Review

Yu, Jiaqi; Chang, Wen‐Shao; Dong, Yu

doi:10.3390/buildings12081284

Cited by 36 publications

(14 citation statements)

References 217 publications

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“…The potential of smart systems was also discovered in related studies with overarching experiments using machine learning (ML) and real-time deep learning models [3]. In a comprehensive review article, the models were classified as white box, grey box, and black box models, such as machine learning models, artificial neural networks, and recurrent neural networks, which are deployed for predicting building energy usage [48]. In that research, the building envelope parameters, HVAC systems, and weather factors were surveyed accordingly [48].…”

Section: Related Workmentioning

confidence: 99%

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Erişen

2023

Buildings

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Energy-efficient automated systems for thermal comfort control in buildings is an emerging research area that has the potential to be considered through a combination of smart solutions. This research aims to explore and optimize energy-efficient automated systems with regard to thermal comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor spaces. In this research, a systematic approach is deployed, and building information modeling (BIM) software and energy optimization algorithms are applied at first to thermal comfort parameters, such as natural ventilation, to derive the contextual information and compute the building performance of an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset from the experiment environment is also applied in training and testing unique black box models, which are examined through the users’ voting data acquired via the personal comfort systems (PCS), thus revealing the significance of Fanger’s approach and the relationship between people and their surroundings in developing the learning models. The contextual information obtained via BIM simulations, the IoT-based data, and the building performance evaluations indicated the critical levels of energy use and the capacities of the thermal comfort control systems. Machine learning models were found to be significant in optimizing the operation of the automated systems, and deep learning models were momentous in understanding and predicting user activities and thermal comfort levels for well-being; this can optimize energy use in smart buildings.

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Section: Related Workmentioning

confidence: 99%

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Erişen

2023

Buildings

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“…In addition, increasing urbanization and the development of cities is one of the issues that aggravates this concern in many countries [5]. One of the available solutions to control this problem is to predict the energy consumption of the building before designing and building the structure.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast

Khodadadi,

Riazi,

Yazdani

2024

IJE

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The issue of energy limitation has gained attention as a crisis faced by societies. Buildings play a major role, in energy consumption making it crucial to accurately predict their energy usage. This prediction problem has led researchers to explore machine learning techniques in the field of energy efficiency. In this study we investigated the performance of used machine learning methods like Random Forest (RF) Multi Layer Perceptron (MLP) Linear Regression (LR) and deep learning methods for predicting building energy consumption. The findings revealed that deep learning outperformed methods in solving this problem. To address this we proposed a voting based solution that combines three CNN models with structures and a Deep Neural Network (DNN) method. We applied our proposed method to the WiDS Datathon dataset and achieved promising results. Each of the deep learning methods used in the proposed method provide suitable results and finally, the voting them is done by the averaging. Due to the fact that the proposed method obtains the final result from voting regression models with high accuracy, it is considered a robust model that will be able to provide a suitable prediction against new data.

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“…White-box approach models the building dynamics and controlled units using physical equations. It typically produces accurate and detailed models, but at the expense of a high computational cost that may be prohibitive for the implementation (Yu et al, 2022).On the contrary, the blackbox model relies exclusively on models that are driven by data, neglecting physical equations (Broholt et al, 2022). While this approach is computationally efficient, it might be lacking for explanatory power.…”

Section: Introductionmentioning

confidence: 99%

Comparison between program synthesis with large language models and model predictive control for buildings optimal operation

Zabala,

Liventsev,

Febres

et al. 2023

Preprint

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Advanced control techniques, particularly Model Predictive Control (MPC), have proven effective in enhancing energy efficiency within buildings. While MPC remains a prominent choice, alternative strategies like program synthesis, employing large language models, warrant exploration. Despite its success in diverse applications and the inherent advantage of explainability, the performance of program synthesis in the building domain remains largely unexplored. Implemented through the Synthesize, Execute, Debug, and Rank framework with a pretrained GPT model, program synthesis is compared with MPC in an energy management problem using the BOPTEST building optimization testing framework. The test scenario involves a residential dwelling with a modulating heat pump. MPC, incorporating a graybox model recalibrated using real-time data, excels in improving thermal comfort in typical conditions, achieving a notable 35.5\% reduction in energy costs. In extreme peak conditions, MPC maintains thermal comfort improvements at a slightly higher energy cost. Program synthesis, while enhancing thermal comfort, lags behind MPC, revealing some trade-off challenges in certain scenarios. Notably, a program synthesis designed for a specific scenario demonstrates adaptability, achieving desired objectives across diverse scenarios. These findings emphasize the potential of program synthesis in optimizing energy efficiency and thermal comfort but underscore the importance of nuanced parameterization and scenario-specific optimization for its successful implementation in building control strategies.

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Building Energy Prediction Models and Related Uncertainties: A Review

Cited by 36 publications

References 217 publications

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast

Comparison between program synthesis with large language models and model predictive control for buildings optimal operation

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