Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms

Dinmohammadi, Fateme; Han, Yuxuan; Shafiee, Mahmood

doi:10.3390/en16093748

Cited by 22 publications

(10 citation statements)

References 44 publications

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“…Further research should additionally include model sensitivity analysis, for example using SHapley Additive exPlanations (SHAP) analysis [53]. This approach has the advantage of assessing the local influence of a specific feature on a specific prediction, which will be particularly relevant for heat exchangers with variable slope effects on its effectiveness [32].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

Starzec,

Kordana-Obuch,

Piotrowska

2024

Sustainability

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The use of greywater heat exchangers (GHEs) is an effective way to reduce energy consumption for heating domestic water. However, the available characteristics of this type of device are often insufficient and consider only a few selected parameters of water and greywater, which results in the need to look for tools enabling the determination of the effectiveness of GHEs in various operating conditions with incomplete input data. The aim of this paper was to determine the usefulness of artificial neural networks (ANNs). For this purpose, comprehensive experimental tests were carried out on the effectiveness of the horizontal heat exchanger, taking into account a wide range of water and greywater flow rates and temperatures of these media, as well as the linear bottom slope of the unit, which allowed for the creation of a database of 32,175 results. Then, the feasibility of implementing the full research plan was assessed using ANNs. The analysis showed that the impact of the media temperatures on the heat exchanger effectiveness values obtained using ANNs is limited, which makes it possible to significantly reduce the number of necessary experiments. Adopting only three temperature values of at least one medium allowed the generation of ANN models with coefficient values R2 = 0.748–0.999 and RMSE = 0.077–1.872. In the case of the tested GHE, the slope and the flow rate of the mixed water are of key importance. However, even in the case of parameters of significant importance, it is possible to reduce the research plan without compromising the final results. Assuming five different values for each of the four input parameters (a total of 625 combinations) made it possible to generate an ANN model (R2 = 0.993 and RMSE = 0.311) with high generalization ability on the full research plan covering 32,175 cases. Therefore, the conducted analysis confirmed the usefulness of ANNs in assessing the effectiveness of GHEs in various operating conditions. The approach described in this paper is important for both environmental and economic reasons, as it allows for reducing the consumption of water and energy, which are necessary to carry out such scientific research.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

Starzec,

Kordana-Obuch,

Piotrowska

2024

Sustainability

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“…However, this AutoML approach does not provide any explanation in support of the predictive output. Dinmohammadi et al [37] predicted heating energy consumption of residential buildings using advanced machine learning algorithms. They identified the most important features contributing to residential energy consumption by employing a Particle Swarm Optimization (PSO)-optimized Random Forest classification algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…This stacking method included Extreme Gradient Boosting (XGBoost), Random Forest, and Light Gradient-Boosting Machine (Light-GBM), which showed superior performance to other traditional methods. Dinmohammadi et al [37] also proposed a causal inference graph, in addition to SHAP, to explain the factors influencing energy consumption. However, none of the three machine learning models of their stacking method incorporates domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

Kabir,

Hossain,

Andersson

2024

Energies

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The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

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“…Reinforcement learning (RL) has also gained traction in architectural design, particularly for building energy optimization and control [71][72][73][74][75][76][77][78]. In this context, an RL agent iteratively explores different design parameters or control strategies, receiving rewards or penalties based on the resulting energy consumption and other performance metrics [79][80][81][82][83].…”

Section: Reinforcement Learning In Architectural Designmentioning

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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Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms

Cited by 22 publications

References 44 publications

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

Evaluation of the Suitability of Using Artificial Neural Networks in Assessing the Effectiveness of Greywater Heat Exchangers

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

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

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