Modeling energy-efficient building loads using machine-learning algorithms for the design phase

Sapnken, Flavian Emmanuel; Hamed, Mohammad M.; Soldo, Božidar; Tamba, Jean Gaston

doi:10.1016/j.enbuild.2023.112807

Cited by 25 publications

(9 citation statements)

References 93 publications

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“…Better results were obtained for decision trees in the study by Sapnken et al [56], in which energy consumption was estimated for many buildings using nine ML approaches. Among them, decision trees showed the highest computational efficiency and the best learning speed.…”

Section: Decision Trees and Random Forestmentioning

confidence: 94%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

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Section: Decision Trees and Random Forestmentioning

confidence: 94%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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“…To address computational complexity and resource requirements [102][103][104][105], the framework will explore strategies for model compression [106][107][108][109], distributed training, and efficient deployment on both cloud and edge computing platforms, enabling seamless integration into architectural design workflows [110,111].…”

Section: Addressing the Gap And Proposed Approachmentioning

confidence: 99%

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Karimi,

Adibhesami,

Hoseinzadeh

et al. 2024

Buildings

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This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

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“…In energy consumption prediction in building projects, Sapnken et al [23] conducted a study using data from 7559 buildings and employing nine ML models. Their investigation focused on the efficiency of a Deep Neural Network (DNN) model, demonstrating impressive results and proposing it as an innovative tool for optimizing and predicting energy consumption during the construction design phase of energyefficient buildings.…”

Section: B Literature Reviewmentioning

confidence: 99%

Investigating Cooling Load Estimation via Hybrid Models Based on the Radial Basis Function

Zhang,

Zheng

2024

IJACSA

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To advance energy conservation in cooling systems within buildings, a pivotal technology known as cooling load prediction is essential. Traditional industry computational models typically employ forward or inverse modeling techniques, but these methods often demand extensive computational resources and involve lengthy procedures. However, artificial intelligence (AI) surpasses these approaches, with its models exhibiting the capability to autonomously discern intricate patterns, adapt dynamically, and enhance their performance as data volumes increase. AI models excel in forecasting cooling loads, accounting for various factors like weather conditions, building materials, and occupancy. This results in agile and responsive predictions, ultimately leading to heightened energy efficiency. The dataset of this study, which comprised 768 samples, was derived from previous studies. The primary objective of this study is to introduce a novel framework for the prediction of Cooling Load via integrating the Radial Basis Function (RBF) with 2 innovative optimization algorithms, specifically the Dynamic Arithmetic Optimization Algorithm (DAO) and the Golden Eagle Optimization Algorithm (GEO). The predictive outcomes indicate that the RBDA prediction model outperforms RBF in cooling load predictions, with RMSE=0.792, approximately half as much as those of RBF. Furthermore, the RBDA model's performance, especially in the training phase, confirmed the optimal value of R 2 =0.993.

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Modeling energy-efficient building loads using machine-learning algorithms for the design phase

Cited by 25 publications

References 93 publications

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Investigating Cooling Load Estimation via Hybrid Models Based on the Radial Basis Function

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