Problems of achieving energy efficiency in residential low-rise housing construction within the framework of the resource-saving technologies use

Секисов, Александр

doi:10.1051/e3sconf/202128106004

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The energy performance of buildings (EPB) is fundamental to architectural and civil engineering research since it directly impacts the environment and reduces energy waste. Energy efficiency has been defined in terms of building design and construction as "less energy utilisation of buildings including commercial, manufacturing, and residential for cooling, heating, and running electricity to derive the same outcome [1,2]. Recent statistics show that the energy consumption of buildings has increased steadily over the last few decades, suggesting benefits to the development of smart optimisation for managing heating, ventilation, and air conditioning (HVAC).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Environmental legislation is constantly changing in connection with real transformations in industry, agriculture and other spheres of activity [8][9]. In the information space, natural resource users, environmentalists, industrialists, lawyers, economists, the public and other citizens widely discuss current trends in this area and propose systemically sound solutions [10][11].…”

Section: Introductionmentioning

confidence: 99%

Natural environment assessment of negative impact of anthropogenic factors

2023

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Sustainable development of each region of the country and improvement in living standards and public health can only be achieved by preserving natural capital and maintaining proper environmental quality. Environmental quality in the Krasnodar Region is determined by levels of anthropogenic and technogenic load on the natural environment. Sources of pressure are objects of industry, power engineering, transport, capital construction, agroindustrial complex and municipal services, which lead to pollution of atmospheric air, water and land resources, to pollution of surface and ground water and degradation of some water bodies, to disruption of ecosystems of flora and fauna. The article is devoted to the review of the condition of atmospheric air and water bodies in Krasnodar Krai, as well as to the study of pollution indicators of discharged wastewater in the territory of the region. Information on indicators of polluted wastewater discharge into water bodies is presented, a grouped table is determined on the basis of these pollution indicators, which indicates the main reasons for the continued pollution of surface water bodies, conclusions are drawn as to what measures to solve the global environmental problem should be taken to improve the quality of wastewater treatment.

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Thermal Load Prediction in Residential Buildings Using Interpretable Classification

Abdel-Jaber,

Dirks

2024

Buildings

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Energy efficiency is a critical aspect of engineering due to the associated monetary and environmental benefits it can bring. One aspect in particular, namely, the prediction of heating and cooling loads, plays a significant role in reducing energy use costs and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML), have provided cost-effective and high-quality solutions for the prediction of heating and cooling loads. However, few studies have focused on interpretable classifiers that can generate not only reliable predictive systems but are also easy to understand for the stakeholders. This research investigates the applicability of ML techniques (classification) in the prediction of the heating and cooling loads of residential buildings using a dataset consisting of various variables such as roof area, building height, orientation, surface area, wall area, and glassing area distribution. Specifically, we sought to determine whether models that derive rules are competitive in terms of performance when compared with other classification techniques for assessing the energy efficiency of buildings, in particular the associated heating and cooling loads. To achieve this aim, several ML techniques including k-nearest neighbor (kNN), Decision Tree (DT)-C4.5, naive Bayes (NB), Neural Network (Nnet), Support Vector Machine (SVM), and Rule Induction (RI)- Repeated Incremental Pruning to Produce Error (RIPPER) were modelled and then evaluated based on residential data using a range of model evaluation parameters such as recall, precision, and accuracy. The results show that most classification techniques generate models with good predictive power with respect to the heating or cooling loads, with better results achieved with interpretable classifiers such as Rule Induction (RI), and Decision Trees (DT).

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Problems of achieving energy efficiency in residential low-rise housing construction within the framework of the resource-saving technologies use

Cited by 3 publications

References 3 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

Natural environment assessment of negative impact of anthropogenic factors

Thermal Load Prediction in Residential Buildings Using Interpretable Classification

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