Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods

Abdou, N.; Mghouchi, Y. El; Jraida, K.; Hamdaoui, Said; Hajou, A.; Mouqallid, Mhamed

doi:10.1016/j.jobe.2022.105332

Cited by 16 publications

(10 citation statements)

References 54 publications

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“…There are several types of ANN including feedforward, long shortterm memory, and recurrent. Some studies have applied SVM and ANN algorithms, or compared them with other ML algorithms, in classification problems related to the energy efficiency of buildings [27,40,41]. A hybrid method, called group support vector regression (GSVR), consisting of SVM algorithms (used for model verification) and a number of ML methods based on ANN and regression (used for early model building), has been proposed [40] to predict the thermal loads of residential buildings based on [8]'s dataset.…”

Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Abdel-Jaber,

Dirks

2024

Buildings

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Energy efficiency is currently a hot topic in engineering due to the monetary and environmental benefits it brings. One aspect of energy efficiency in particular, the prediction of thermal loads (specifically heating and cooling), plays a significant role in reducing the costs associated with energy use and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML) techniques, have provided cost-effective and high-quality solutions for solving energy efficiency problems. This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads. The review includes many ML techniques, including ensemble learning, support vector machines (SVM), artificial neural networks (ANN), statistical models, and probabilistic models. Existing studies are analysed and compared in terms of new criteria, including the datasets used, the associated platforms, and, more importantly, the interpretability of the models generated. The results show that, despite the problem under investigation being studied using a range of ML techniques, few have focused on developing interpretable classifiers that can be exploited by stakeholders to support the design of energy-efficient residential buildings for climate impact minimisation. Further research in this area is required.

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Section: Support Vector Machine and Artificial Neural Network Learnersmentioning

confidence: 99%

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Abdel-Jaber,

Dirks

2024

Buildings

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“…Several studies propose various construction modifications, bioclimatic architecture principles, and the use of bio-based materials to enhance energy efficiency [29][30][31][32]. Some authors emphasize the need to expand the current RTCM to existing buildings and improve zoning methodology [33][34][35]. Sobhy et al [26] suggest that the evaluated house demonstrates superior performance compared to a configuration adhering to the RTCM requirements across both investigated climate zones.…”

Section: Energy Efficiency Improvements In the Rtcmmentioning

confidence: 99%

Moroccan Public Buildings and the RTCM: Insights into Compliance, Energy Performance, and Regulation Improvement

El Asri,

Abdou,

Mharzi

et al. 2023

Energies

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This study investigates the compliance of Moroccan public buildings with the thermal regulation (RTCM). It analysis public building envelope typology, the conformity and the impact of RTCM conformity on energy performance across the six climatic zones defined by the RTCM. The outcomes of this research may serve as a decision-support instrument by identifying areas where the thermal regulation is already validated and the potential impact that a public building could undergo by implementing these provisions. Additionally, this study can be viewed as a crucial analysis contributing to the enhancement of the existing regulation. The study emphasizes various extraneous stipulations present within the prevailing regulation. In this work, we have attempted to categorize these stipulations into two distinct groups: recommendations for refinement to be integrated into the regulatory framework, and essential measures to ensure successful implementation of the regulation in the realm of building energy efficiency.

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“…Subsequent research has extensively applied machine learning-based data-driven approaches to building load prediction [19]. Common machine learning prediction methods include Support Vector Machines (SVMs) [20][21][22], Artificial Neural Networks (ANNs) [23,24], and Deep Learning (DL) methods [25][26][27]. In the field of load prediction, various data-driven methods each play a unique role, synergistically enhancing the accuracy and efficiency of predictions.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Zhao,

Fan

2024

Sustainability

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Accurately predicting the cold load of industrial buildings is a crucial step in establishing an energy consumption management system for industrial constructions, which plays a significant role in advancing sustainable development. However, due to diverse influencing factors and the complex nonlinear patterns exhibited by cold load data in industrial buildings, predicting these loads poses significant challenges. This study proposes a hybrid prediction approach combining the Improved Snake Optimization Algorithm (ISOA), Variational Mode Decomposition (VMD), random forest (RF), and BiLSTM-attention. Initially, the ISOA optimizes the parameters of the VMD method, obtaining the best decomposition results for cold load data. Subsequently, RF is employed to predict components with higher frequencies, while BiLSTM-attention is utilized for components with lower frequencies. The final cold load prediction results are obtained by combining these predictions. The proposed method is validated using actual cold load data from an industrial building, and experimental results demonstrate its excellent predictive performance, making it more suitable for cold load prediction in industrial constructions compared to traditional methods. By enhancing the accuracy of cold load predictions. This approach not only improves the energy efficiency of industrial buildings but also promotes the reduction in energy consumption and carbon emissions, thus contributing to the sustainable development of the industrial sector.

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Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods

Cited by 16 publications

References 54 publications

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

A Review of Cooling and Heating Loads Predictions of Residential Buildings Using Data-Driven Techniques

Moroccan Public Buildings and the RTCM: Insights into Compliance, Energy Performance, and Regulation Improvement

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

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