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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39223231
70519
10.1038/s41598-024-70519-y
Article
Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability
Mahmood Shahid shahidnajam786@live.com

1
Sun Huaping 12
El-kenawy El-Sayed M. 36
Iqbal Asifa 4
Alharbi Amal H. 5
Khafaga Doaa Sami 5
1 https://ror.org/03jc41j30 grid.440785.a 0000 0001 0743 511X School of Finance and Economics, Jiangsu University, Zhenjiang, China
2 https://ror.org/02egmk993 grid.69775.3a 0000 0004 0369 0705 School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083 China
3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111 Egypt
4 https://ror.org/04ypx8c21 grid.207374.5 0000 0001 2189 3846 School of International Studies, Zhengzhou University, Zhengzhou, China
5 https://ror.org/05b0cyh02 grid.449346.8 0000 0004 0501 7602 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia
6 https://ror.org/059bgad73 grid.449114.d 0000 0004 0457 5303 MEU Research Unit, Middle East University, Amman, Jordan
2 9 2024
2 9 2024
2024
14 2033119 2 2024
19 8 2024
© The Author(s) 2024
2024
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A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Keywords

Green building
Environmental sustainability
Artificial intelligence
Machine learning
Deep learning
Subject terms

Energy and society
Sustainability
http://dx.doi.org/10.13039/501100004242 Princess Nourah Bint Abdulrahman University PNURSP2024R120 El-kenawy El-Sayed M. issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

The building and construction industry is recognized for using excessive amounts of natural resources, which has a detrimental impact on the environment1. Buildings and construction consume the most energy (36%) and emit the most CO2 (37%) globally2–4. The Architecture, Engineering, and Construction (AEC) industry has a significant issue in the form of sustainability, which encompasses resource efficiency. Researchers, specialists, and practitioners in the building and construction sector have endeavored to identify substitute methods for implementing energy conservation throughout the building life cycle. The green building (GB) idea is being implemented as one of the initiatives5,6.

GB is being endorsed worldwide as an approach to enhancing the building industry’s sustainability7. The Green Building Concept pertains to using environmentally friendly and sustainable concepts throughout the building life cycle, starting with the first stages of project development and continuing through to the decommissioning phase. It is frequently considered a method to reduce energy consumption in the building and construction industry8–10. At every project step, the contractor, the architects, the engineers, and the client must all support this. The concerns about the economy, well-being, utility, and durability associated with traditional design are lessened by green building practices. More and more people are embracing the notion of green building since it is advantageous from a financial, health, and, most importantly, environmental standpoint11.

The GB paradigm reduces the adverse environmental consequences that buildings and human activity within them have by introducing concepts and technology into buildings at every stage of their existence12. The environment may be greatly impacted by decisions made at the first stages of building design13. However, the design of GB is typically more difficult than conventional structures because of the different design components and building performances that need to be adjusted to accomplish sustainability ideally14,15. As a result, the GB design process may take longer than expected since it requires a multidisciplinary team project in which team members must elaborate on every GB component of the design16.

Technological advancements in construction have enabled digitization, automation, and integration, improving decision making and productivity in (GB) initiatives17–19. This has led to global interest and numerous empirical studies on GB’s benefits20,21. AI, which encompasses intelligent systems capable of learning and problem-solving, further enhances communication and productivity in GB design17,18. AI includes machine learning (ML) as a subset. It’s a method that gives a system the capacity to grow and learn from its own experiences without programming22. It has been thoroughly studied and used throughout the construction process23. This method was created during the building design phase to maximize the GB design’s building performance. The growth of digital technology adoption in the building design process has been greatly aided by earlier research on (ML) use in the process carried out in the last few years. The whole design process has been changed by it24. For example, in research25, an ML model to forecast dependable energy performance in office buildings was developed using the artificial neural network (ANN) approach. This model required 50 times less computing time than the industry standard building performance simulation tools.

However, it has been demonstrated that the Statistical Neural Network and Gaussian Regression techniques used by Rahman and Smith26 to create an ML model to forecast fuel usage in a commercial building were more accurate. In addition, Geyer and Singaravel27 created a component-based machine-learning model to forecast the thermal energy performance of office buildings by employing the ANN approach. With an error of less than 3.9%, the forecast may be generated with a significantly smaller computing time. This earlier research shows that the suggested predictive models utilizing machine learning techniques might drastically reduce the time needed for calculation during the design phase, improving the efficiency of engineers and architects creating GB. Though there have been several advancements, further proof is still required to support the use of (ML) in creating a prediction model for GB design. This study attempts to create a design prediction model for GB using the ML and DL classifiers method as one of the techniques to address this gap. This paper is expected to provide references and give insights to building practitioners regarding the utilization of the ML and DL approach to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process, which can significantly contribute to accelerating technology-based development in the building and construction sector.

Research contribution

The following are this paper’s primary contributions:We propose a technique for green building design by applying machine and deep learning techniques that can maximize resource use, minimize energy consumption, and reduce the built environment impact to enhance environmental sustainability.

We apply the suggested models to the ASHARE-884 dataset. We perform data preprocessing by applying Exploratory Data Analysis (EDA), which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. Also, the Z-Score normalization technique was applied to normalize the data.

The experiment’s findings show that the suggested GNN and LSTM design performs better in terms of accuracy and efficiency in terms of environmental sustainability in green buildings when compared to traditional DL methodologies.

Research organization

This paper is structured as follows: section “Literature review” presents the background and relevant works. Section “Proposed methodology” introduces the proposed approach to machine learning and the deep learning method for green building design to enhance environmental sustainability. Section “Results” assesses the performance of our technique and contrasts it with the baseline methods. Section “Conclusion” concludes the paper and provides future direction.

Literature review

This section examines previous studies on environmental sustainability and artificial intelligence (AI) techniques for green buildings to pinpoint research gaps and support the necessity of the suggested strategy.

Environmental sustainability for green buildings

Sustainability is growing in the building and construction sector as a significant driver of social, economic, and environmental benefits with fewer adverse environmental effects. Green and sustainable practices must be established to increase energy efficiency in the building and construction sector. This is especially true when applying the most recent green technologies. The study28 aims to find the most applicable techniques for employment in green building, assess the advantages of implementing green building, and examine the best practices of green building attributes. The results of this study demonstrated that green buildings can be created with less energy consumption and a lower long-term operating and maintenance cost by utilizing sustainable practices and energy-efficient systems. According to the study’s author29, achieving the objectives of resource protection, pollution reduction, and ecological environment improvement requires careful consideration of the environmental benefit analysis of green buildings. This includes efficient energy and resource utilization. The findings demonstrate that the incremental environmental advantages in terms of land saving, energy saving, water saving, material saving, indoor environmental quality, and operation management are increased by 23.15%, 10.37%, 19.30%, 18.25%, and 22.53%, respectively. In contrast, the measured results of the incremental environmental costs of the office building in Taiyuan City are decreased by 13.56%, 11.02%, 25.17%, 14.43%, and 15.25%, respectively. According to those, BIM technology used in the full life cycle cost assessment of green buildings can evaluate, quantify, and direct every stage of building design, construction, and upkeep while also largely satisfying resource and energy conservation requirements.

Green buildings are seen as a vital aspect of attaining sustainability since they incorporate green and natural components that reduce pollution and usage of resources. The goal of30 research is to define the terms “sustainability” and “green building” as they relate to residential building design since it’s important to comprehend how sustainable design principles can help mitigate negative effects on the environment and society. The case-study methodology is employed. The study focuses on the innovative and sustainable design elements utilized in three case studies of green buildings—one each in China, Indonesia, and Dubai. The results showed that these nations seek to encourage the construction of environmentally friendly structures, especially homes, to achieve maximum social, economic, and environmental sustainability. In31, a study was conducted to determine the stages of action and challenges involved in implementing sustainable building practices, as well as to assess the extent of integrating these techniques into professional practice. The study’s suggested purpose was to ensure the best possible outcomes and qualitative and quantitative methodologies were used to examine. The study set a descriptive analysis based on survey analysis for the quantitative approach. The results showed that stakeholders have a respectable degree of awareness and knowledge. The results of this study may close a significant knowledge gap about the benefits of green building practices that exist in the absence of empirical research in developing nations.

The author of article12 suggested a practical mapping tool that assesses how much a (GB) contributes to the Sustainable Development Goals (SDGs) by applying green building rating tools (GBRTs). It then used the analytic hierarchy technique to examine this contribution quantitatively. The findings demonstrated that GBRTs greatly assist SDGs 3, 7, 11, and 12, with SDG 12 benefiting the most. SDG Target 7.3, on the other hand, is the most notable since it offers the most significant avenue for GBRT to contribute to the SDGs. The study32 investigates how the information modules suggested by EN 15978 and the three elements of sustainabilityenvironmental, social, and economic are covered by the indicators in GBRS as it moves through the life cycle phases of building development. The 387 sustainability indicators that were part of the eight chosen GBRS were examined and grouped based on three distinct classification criteria: the sustainability dimension, information modules, and stage of the construction process life cycle. Four rounds and meetings of an iterative process of indicator analysis and clustering were conducted by a panel of diverse academic and professional experts in the subject of study, leading to a consensus on the results. According to the analysis’s findings, the environmental dimension is the one that is most valued among the instruments, and to strike a fair balance, more focus needs to be paid to both the social and economic dimensions.

Recent advances in green building technologies (GBTs) have increased significantly due to environmental, economic, and societal benefits. The primary goal of GBTs is to use resources like water, energy, and other materials sparingly and in a balanced manner. As a result, the environment will be better. GBs improve productivity and health, reduce maintenance and operating costs, and reduce energy use and emissions. The goal of33 study is to identify important concerns in the field of green building research that pertain to sustainable building practices that are low-impact on the environment, economical, and long-term in nature while also taking future developments into account. To ensure a sustainable future, this article analyzes the current status of green building construction and recommends more research and development. This study also suggested a few potential paths for sustainable development research to stimulate more investigation. The author of34 study creates a set of valid and reliable social sustainability metrics for evaluating green buildings in China. Indicators of green buildings are required to support practitioners in comprehending social sustainability indicators and to validate different studies. Therefore, the fuzzy Delphi approach is applied to examine these indicators. The findings indicate that the most crucial elements of green building social sustainability are durability and safety. Health, comfort, accessibility, and convenience are further important factors. To attain social sustainability, this set of indicators helps practitioners make decisions and offers appropriate, useful guidance for many stakeholders.

Artificial intelligence techniques for green buildings

In particular, for sustainable projects (i.e., green buildings), a precise expense forecast is essential. In the construction sector, where stakeholders require more knowledge in contract cost estimating, green building construction contracts are still relatively new. Green buildings, in contrast to conventional construction, are made to make use of innovative technology to lessen the negative effects that their operations have on society and the environment. To anticipate the costs of green buildings, the author of the35 paper proposes machine learning-based techniques such as random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBOOST). The impact of both hard and soft cost-related attributes is taken into account in the construction of the suggested models. The accuracy of the created algorithms is assessed and compared using evaluation measures. When compared to the DNN’s 0.91 accuracy, XGBOOST’s 0.96 accuracy was the greatest; RF’s 0.87 accuracy came in second.

The Artificial Intelligence-based Energy Management Method (AI-EMM) for green buildings is recommended in36 article. It can respond intelligently to improve user comfort, safety, and energy efficiency in response to human choices. The AI-EMM model includes subsystems for smart user identification and monitoring of the interior and external surroundings, as well as a universal infrared communication system. Energy usage is improved using Long Short-Term Memory (LSTM) models. The recommended methodology is applied to analyzing energy usage data from green buildings. The proposed method for investigating the interaction between Heating, Ventilation, and Air Conditioning (HVAC) systems should emphasize airside design optimization for the improved interior climate. The results show that environmentally friendly buildings and financial rewards are compatible together. The AI-EMM’s experimental results showed a 94.3% high-performance ratio, a 15.7% lower energy consumption ratio, a 97.4% accuracy ratio, a 95.7% energy management level, and a 97.1% prediction ratio. In research37, the author has examined how AI and DL have recently advanced and been applied to promote sustainability in a number of areas, such as attaining the (SDGs), energy efficiency, healthy environments, and energy management for smart buildings. AI has the potential to support 134 of the 169 SDGs targets, making it a valuable instrument for encouraging sustainable practices. However, considering the rapid pace at which these technologies are developing, extensive regulatory control is required to guarantee ethical standards, safety, and transparency. AI and DL have been successfully applied in the renewable energy sector to optimize power grid stability, fault detection, and energy management.

The objective of the38 study is to create a mathematical model that will investigate the supply and demand balance for external green construction support and the related spending adjustment procedures in a deflationary environment. To determine the key parameters influencing the green building cost prediction process, the most recent datasets from 3578 green projects in Northern America were gathered, pre-processed, analyzed, post-processed, and evaluated using state-of-the-art (ML) techniques. The results indicate that green building costs are expected to decline due to governmental and private expenditures in green development. Moreover, public and private investment has a greater effect on reducing the cost of green construction during deflation than during inflation. Consequently, the proposed approach can be employed by decision-makers to oversee and assess the annual ideal external investment in the development of green buildings.

Proposed methodology

This section explains the whole procedure of the suggested approach. The proposed method comprises multiple steps, such as obtaining datasets, preparing data, and making model predictions. Figure 1 provided a graphic representation of the suggested architecture. The ASHRAE dataset is explored first in the design, and then data preprocessing, such as label encoding and Z-score normalization, is applied. After that, ML and DL classifiers are trained. The ensemble model forecasts energy use, and models are combined for increased accuracy. Metrics are used in evaluation to measure performance, and the most effective model is ultimately selected before the process is accomplished.Fig. 1 Proposed architecture.

Experimental dataset

The dataset was acquired through field investigations of 160 distinct building sites worldwide. It is provided in support of the ASHRAE initiative to create a model of preferred thermal convenience. ASHRAE monitors an accumulation of data from several investigations by various investigators as a component of the RP-884 accessible repository. Numerous climate zones dispersed throughout various regions provide data files39.

The dataset was selected because individual turnover depends on a pleasant temperature. Residents of the building might experience discomfort due to an absence of temperature regulation. There are 56 features and 12,595 records in the ASHRAE RP-884 dataset used for this research. The objective of this dataset is to create an adaptive classifier. It comprises over 20,000 consumer convenience scores from 52 polls conducted across 10 climatic regions. The dataset’s primary identifiers are the reading day, age, year, subject, building code, and time of day. A thermal survey that residents submit, including the ASHRAE, the warmth scale (ash), clothing insulation, comfort level, metabolic rate, and high air temperature. Indices that are calculated from existing data, average air temperature, average radiation temperature, operational temperature, an average of three heights airspeed, an average of three heights airspeed, an average of three heights turbulence, air pressure, relative humidity, new standard practical temperature index set, two-node disc index, predicted mean vote, predicted percentage dissatisfied, etc. Perceived control over the thermal environment (PCC), PCED from 1 to 7, and other aspects of private environmental oversight combined with outdoor climate information, the outdoor average of the min/max air temperature, and outdoor maximum humidity percentage.

Dataset preprocessing

Data preprocessing is important since it enhances the model’s effectiveness and produces more precise attributes. In this step, data preprocessing is carried out using Exploratory Data Analysis (EDA), which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. The present study utilized a Z-score normalization for cleaning data.

Exploratory data analysis

An essential component of any research endeavor is exploratory data analysis (EDA). Finding patterns and abnormalities in the data that can be utilized to focus the hypothesis testing is the primary objective of exploratory analysis. It also provides resources for data visualization and evaluation, usually through graphical representation, to help in hypothesis generation. After data collection, EDA is completed. Without making any assumptions, the data is effectively evaluated, plotted, and updated to assess the quality of the data and build models40. The ASHRAE dataset was subjected to the EDA approach. The dataset consists of one categorical and 55 numeric features, with a normal distribution for most. UW (Uncomfortable Warm), N (Neutral), and UC (Uncomfortable Cold) are the three classes that make up the categorical target attributes. 12,595 records of these classes are found in the dataset; the UW class has 1029, the N class has 10,061, and the UC has 1505 data entries. Figure 2 illustrates the unbalanced quality of the dataset.Fig. 2 Graphical visualization of dataset classes neutral (N), uncomfortable cold (UC), uncomfortable warm (UW).

A measure of imbalance or divergence from normative patterns in an ensemble of data is called skewness. To determine the direction of the outlier, this study measures the data skewness. After determining the kurtosis of the information set and verifying its skewness, if the skewness value is positive, the distribution is asymmetrical. It has an extended tail on the right side. Kurtosis is the total weight of the distribution’s tails expressed proportionately to the distribution’s center. In this work, the kurtosis of the normal distribution is analyzed before the log transformation of skewed data is carried out. The log transformation can be used to approximate normalcy for skewed data. The outcome shows that the normal distribution’s kurtosis is 0.

Encoding categorical variables

Categorical variables challenge certain ML techniques. The classification variable must be transformed into numerical data, which is crucial for the designed algorithms to function as intended. The categorical variables’ coding determines the way various algorithms function. One or more labels in word or numerical form can be present in a feature’s dataset. This makes it simpler for people to evaluate the data, but it is incomprehensible to machines41. We utilize an encoding that renders these labels interpretable by machines. There are other encoding techniques, such as one-hot and hash encoding. The label encoding approach is used in this work to encode categorical information.

Label encoder

Numerical label input is made possible in an ML model using label encoding. Label Encoder uses numbers to assign a value to each label, replacing the values of each label in the dataset. When they have divergent priorities, the labels can be employed. This step is crucial in the data preparation process for supervised learning methods41. Usually, this technique replaces each value in a category column with a number between 0 and N − 1. In this study, a label encoder assigns a value of 0 to 1 or 2 to each categorical variable.

Data cleaning

The Z-score normalization technique is used in this study to identify and eliminate anomalies while cleaning the data. This study cleans the data after converting the category variable to a numerical value. The data distribution was 0.5 and 0.98 before the cleanup. Figure 3 represents the distribution of features before data cleaning.Fig. 3 Distribution of features before data cleaning.

Z-Score: The degree to which a value resembles the average of a group of values is measured by a Z-score. Standard deviations are used in the average calculation of the Z-score. A data point’s Z-score of zero indicates that it has the same value as the average represented in Eq. (1). The data distribution is 0.5, 0.98 after the Z-score is applied and the outliers are eliminated. Figure 4 represents the distribution of features after data cleaning.1 Z-score=x-μσ

Fig. 4 Distribution of features after data cleaning.

The prediction portion counts the label data as class 0, which has 6919 values; class 1, which has 1014 values; and class 2, which has 651, following the completion of EDA, label encoding, and data cleaning. After preprocessing the data, the modeling phase is carried out. The data is divided into training and testing data, keeping a ratio of 70% training data and 30% testing data to increase accuracy and efficacy for this phase. The machine and deep learning models are trained after splitting.

Model selection

Model selection involves choosing the appropriate hyperparameters, optimizing strategies, and neural network architecture. Configure hyperparameters such as batch size, learning rate, the number of layers, activation parameters, dropout rate, etc. Hyperparameter tuning can significantly impact the model’s performance. Each model architecture is trained on the training set with a distinct set of hyperparameters using the appropriate evaluation criteria. This study utilized the multiple ML and DL models: RF, DT, XGB classifier, Stacking, GNN, LSTM, and RNN for green building sustainable environment.

Random forest

An ensemble learning technique called Random Forest combines several decision trees to produce a model that is more reliable and accurate. It is a popular model for regression and classification applications and is a member of the tree-based model class. Using random feature choice, Random Forest models build several decision trees independently, each trained on bootstrapped dataset samples. The model is less likely to overfit than individual trees because it aggregates predictions from individual trees by majority voting or averaging, which lowers variance and improves generalization performance.

Decision tree

Decision trees are supervised learning algorithms that create regions in the feature area by making decisions based on the supplied characteristic values. At each node, the tree finds which attributes best separate the data by optimizing a chosen criterion, such as data acquisition or Gini impurity. This process iterates backward and forwards until an end condition is met, such as reaching a maximum depth or a minimum number of samples per leaf. Decision trees are widely used in many different sectors because of their ease of understanding and ability to handle numerical and categorical data. However, in large, complex datasets, they may overfit and have difficulty generalizing to new data if the proper regularization procedures are not used.

Extreme gradient boosting

A potent ensemble learning technique built on gradient boosting is called XGBoost (Extreme Gradient Boosting). Gradient descent is used to maximize the weak learners, which are usually decision trees that are constructed one after the other. Rapid processing on structured/tabular data, efficiency, and scalability are well-known attributes of XGBoost. It has built-in functionality for handling missing values and uses regularization techniques to avoid overfitting. Furthermore, XGBoost provides sophisticated functionalities such as cross-validation and early stopping to optimize model performance.

Stacking

Stacking, also called stacked expansion, is an ensemble learning method that uses a meta-model (logistic regression) to aggregate predictions from several base models (RF, DT). After training fundamental models on the dataset, a meta-model is trained using the predictions of the base models as input characteristics. By determining which to mix the outputs of several models best, stacking attempts to enhance overall forecasting accuracy by utilizing the strengths of each model. It is a well-liked option in machine learning contests and ensemble learning contexts since it frequently performs better than individual models and conventional ensemble techniques.

Graph neural network

Graph Neural Networks (GNNs) were developed to arrange and visualize data in topologies or networks. Graphs are made up of vertices, or nodes, joined by vertices and, in this case, hyperlinks. To acquire information and insights, GNNs are used in data mining42. A GNN’s function is to process organized data. In a graph, each point is connected to a feature vector. Node i’s beginning position is represented by gi0.2 gi(m+1)=h∑nεK(i)Agg(gnm,cinm)

m represents the GNN layer and K(i) indicated the node i neighborhood. An aggregation method called Agg collects data from nearby nodes. The edge attribute is cinm among node i and node n in layer m, and the non-linear activation function is denoted as h. To generate a graph-level participation, data from each node is combined after multiple layers.3 fj=Readout(fib|∀iεG)

b is the final layer, and an aggregation function called readout is used to determine the illustration at the graph scale. A loss function, which is usually a measurement of the discrepancy between the true and anticipated categories, is minimized to train the GNN.4 L=Loss(Prediction(fj),ActualLabels)

The GNN model output is denoted by Prediction(fj). Training uses optimization algorithms and backpropagation to change the model variables θ.5 θ←θ-η·∇θL

where η represents the learning rate.

Long short-term memory (LSTM)

The enhanced form of the recurrent neural network (RNN) is the long-short-term memory. It is proposed that memory blocks, not normal RNN units, but long-term memory, can address the increasing slope and vanishing issue. The main distinction between LSTM and RNN is that LSTM incorporates the cell state to preserve the long-term states. An LSTM network can retrieve and link previous data to present information43. Three distinct gates are used in the architecture of long short-term memory: the input, forget, and output gates. The cell’s new and prior states are designated by nt and nt−1, respectively, while the current and previous outputs are indicated by ot and ot−1, respectively. The current input is represented by ct. The following equations provide the rules for the LSTM’s input gate.

In Eq. (6), the input gate is represented by gi, and the preceding outputs, vt−1 and pt, are passed through the sigmoid layer for deciding which portion of the information needs to be added.6 Lt=σDi.vt-1,pt+gi

After transferring the old information, pt−1, and the current information, ct, by tanh layer using the input gate ai, the Eq. (7) is utilized to obtain the updated information Ut. Equation (8) integrates the information of long-term memory St−1 into St and the present state of information Ct. The sigmoid output is denoted by Di, while St is denoted by the tanh output. Di represents the weight metrics, and ai represents the LSTM’s input gate. Using the dot product of the input information Jt and the current state of information st and sigmoid layer, the forget gate of the LSTM then enables the particular transmission of the data.7 Ut=tanh(Di·[pt-1,ct]+ai)

8 Ct=ht(St-1)+jtst

Equation (9) is utilized to determine the specific probability of deleting the linked data by the final cell. The weight matrix is represented by Df, the offset is af, and the sigmoid function is σ.9 bt=σDf·pt-1,ut+af

The inputs in Eqs. (10) and (11) establish that the states required for the continuation through the previous and current outputs, ot−1 and pt, respectively, are described by the output gate Tt of the LSTM. The decision vector of the condition that transmits new information Nt by the tanh layer is multiplied and acquired by the final output Ft.10 Tt=σDo·ot-1,pt+ao

11 Ft=Tttanh(Nt)

where ao denotes the bias of the LSTM of the output gate and Do is its weighted matrix44.

The input, forget, and output gates are the three gates in the model. The first gate, called a forget gate (ht), uses a sigmoid function of σ to take the previous output, ot−1, and the current input, ct, from the prior state, st−1. The input gate uses the sigmoid function σ and the tanh layer to take in input information Jt after adding the prior data. The information is obtained from the input gate and fed into the output gate, which utilizes the sigmoid function σ to compute all the information and deliver the current state where the output is kept.

Recurrent neural network

An artificial neural network that processes sequential data by preserving a hidden state that records details about earlier inputs is called a recurrent neural network (RNN). Because RNNs feature connections that allow information to remain over time, they are excellent for tasks requiring sequence or time series, in contrast to feedforward neural networks, which analyze each input sequentially.

Recurrent connections, which enable information to move from a single phase to the next, distinguish an RNN. The RNN gets an input i_t at each time step t, processes it to generate an output o_t, and changes its hidden state h_t h, which stores data from earlier time steps. The hidden state at time t is calculated mathematically as an expression of the input it and the hidden state that came before it, h_t. An RNN can learn to analyze sequences of varied lengths since an identical set of parameters (weights and biases) are utilized at each time step. Parameter sharing simplifies This design, enabling the RNN to generalize across multiple time steps.

Results

This section examines the suggested model’s effectiveness. Employing the ASHARE-884 dataset, the suggested model applies various DL classifiers. The parameters used to assess the model are f1-score, recall, accuracy, and precision. By comparing the current methods, these standards assess the proposed model’s performance.

Evaluation metrics

This study assesses the framework’s efficacy using extensive evaluation criteria, each offering valuable perspectives on the model’s operation. The first metric, accuracy, is typically used as the standard to evaluate performance. It is computed as the part of accurately recognized samples based on the total sample amount. The procedure is made simpler by Eq. (12), which emphasizes the measure’s simplicity despite its substantial influence.

Accuracy

The accuracy is the ratio of all positive forecasts the model produces to effectively precise projections; it is a crucial assessment metric utilized in performance evaluation. Equation (12) proportionally illustrates this value, making the metric notional equation easier to understand.12 Accuracy=TP+TNTP+TN+FP+FN

Precision

A model or system’s precision indicates how it forecasts the positive class. It represents the accuracy of the model and the degree of confidence in its ability to produce good predictions. This value is shown proportionately in Eq. (13), facilitating comprehension of the metric basic equation.13 Precision=TPTP+FP

Recall

Recall, also called sensitivity, is an evaluation metric that centers on the ratio of every positive instance to the percentage of precise positive predictions. This balanced viewpoint offers a special benefit while estimating, as the computation of Eq. (14) demonstrates.14 Recall=TPTN+FN

F1-Score

The appropriately identified F1 score functions as an equilibrium of memory and precision because it can effectively communicate the essence of a balanced performance. Combining these two metrics yields the F1-score, a popular estimate of model performance that is especially useful for evaluation. This basic estimating procedure is well described by Eq. (15), which looks complicated but provides much information.15 F1-score=2×Precision+RecallPrecision+Recall

One significant and unique indicator used in the evaluation process is the Confusion Matrix (CM), which is carefully designed to provide precise data regarding the efficacy of the classification model. This essential tool illustrates the model’s efficacy by comparing the anticipated and actual data. True positive (TP), false negative (FN), false positive (FP), and true negative (TN) are the four values displayed by the CM, a unique kind of display. The matrix sections’ labels, which show the actual class designations, correspond to the columns. The correctly recognized samples are arranged along the diagonal, while the incorrectly categorized cases are situated on the diagonal portions. The CM values are an essential tool for assessment that can highlight the advantages and disadvantages of the model. They also provide insightful data that improves the model and produces favorable outcomes.

ML models result analysis

The outcomes of ML models (RF, DT, XGB and Stacking) on the ASHARE-884 dataset are displayed in Table 1. The models with the best accuracy are the XGB and RF models (0.84), the Stacking model (0.84), and the DT model (0.76). On the other hand, the XGB model has the highest precision (0.83), while the RF model has the lowest (0.82). The RF and XGB versions have the highest recall (0.84), while the DT model has the poorest recall (0.76). The F1-score considers both the precision and recall of a model since it is a harmonic mean of both metrics. The models with the greatest F1-score (0.80) are the XGB and RF models, trailed by the DT (0.77) and the stacking (0.80) models. Table 1 Machine learning models results on ASHARE-884 dataset.

Models	Accuracy	Precision	Recall	F1-score	
RF	0.84	0.82	0.84	0.80	
DT	0.76	0.77	0.76	0.77	
XGB	0.84	0.83	0.84	0.81	
Stacking	0.84	0.81	0.84	0.80	

Ml model confusion matrix graphical representation is displayed in Fig. 5. Figure 5a, the confusion matrix of the RF is shown graphically. The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1997 instances were projected for class 0, 92 instances for class 1, and 24 cases are accurately anticipated. Figure 5b, the confusion matrix of the DT is shown graphically. It shows the number of occurrences of correctly identified diagonal cells, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1744 instances were projected for class 0, 124 instances for class 1, and 59 cases are accurately anticipated.Fig. 5 Performance visualization of ML model results.

Figure 5c shows the confusion matrix of the XGB. The diagonal cells of the matrix show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1992 instances were projected for class 0, 100 instances for class 1, and 34 cases are accurately anticipated. Figure 5d shows the confusion matrix of the Stacking. It shows the number of occurrences of correctly identified diagonal cells, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1997 instances were projected for class 0, 92 instances for class 1, and 18 cases are accurately anticipated.

DL models result analysis on training and testing data

The outcomes of deep learning models (GNN, LSTM, RNN) on the ASHARE-884 dataset are displayed in Table 2. The models employ various building energy consumption parameters from the dataset to forecast the energy utilization of a building. DL is compared according to their evaluation, which indicates if they were trained on the test or training dataset. The outcomes demonstrate that the training dataset yielded outstanding results from all three models compared to the test dataset. Overall, the GNN model performed exceptionally well, with an accuracy of 0.83 on the test dataset and 0.85 on the training dataset. Accuracy for the LSTM and RNN models was approximately 0.81 on the training dataset and 0.79 on the test dataset. Compared to the LSTM and RNN models, the GNN model performs better on the test and training datasets regarding accuracy. This implies that capturing the relationships between the various features in the data constitutes a task the GNN model does better. Of the three models, the accuracy is higher than the precision and recall. This shows that false positive predictions are more common in the models than false negative ones. Comparing the test dataset to the training dataset, the F1-score of the three models is declining. Table 2 Deep learning models results on ASHARE-884 dataset.

Models	Assessment	Accuracy	Precision	Recall	F1-score	
GNN	Train dataset	0.85	0.84	0.85	0.81	
GNN	Test dataset	0.83	0.80	0.83	0.79	
LSTM	Train dataset	0.85	0.84	0.85	0.81	
LSTM	Test dataset	0.83	0.80	0.83	0.79	
RNN	Train data	0.81	0.75	0.81	0.75	
RNN	Test data	0.82	0.75	0.82	0.75	

Figure 6 shows the performance visualization of the GNN model in terms of accuracy, loss, and roc curve. Figure 6a demonstrates the training and testing accuracy of the GNN model. The x-axis displays the number of epochs or the number of instances in which the model has evaluated the training data. The percentage of accurate predictions the model generates is displayed on the y-axis, representing accuracy. The training accuracy, or the model’s accuracy using the training data set, is represented by the blue line. The model’s accuracy, represented by the green line, represents the test accuracy. The training accuracy in the figure starts at about 0.80% and rises to about 0.84%. The test accuracy rises to approximately 0.82% from a starting point of about 0.80%. There is a 0.02% applicability gap.Fig. 6 Performance visualization of GNN model results.

Figure 6b shows the training and testing loss of the model. The training loss starts at about 0.625 and goes down to about 0.450. The test loss begins around 0.600 and gradually drops to about 0.500. Although there is no substantial distinction between the two lines, the training loss is always less than the test loss. This implies that the model is effectively expanding with new data. Over time, there has been a decrease in both training and test loss. From this, the model continues to evolve. The test loss is not that different from the training loss. This implies that there is not a significant overfitting of the model. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig. 6c, the model performs better; the train ROC curve has an AUC of 0.82, and the test ROC curve has an AUC of 0.71.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig. 7. It provides an overview of the operation of a classification algorithm. Because the suggested strategy produces fewer false positive and negative data and more constant, better true positive and negative values, it performs better. The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix in Fig. 7a show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7,020 instances were projected for class 0, 497 instances for class 1, and 114 cases are accurately anticipated on training data.Fig. 7 Graphical Visualization of GNN Model Results.

The diagonal cells of the matrix in Fig. 7b show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 19 cases are successfully predicted, whereas 1982 occurrences for class 0 and 83 instances for class 1 were forecasted.

Figure 8 shows the performance visualization of the LSTM model in terms of accuracy, loss, and roc curve. Figure 8a demonstrates the training and testing accuracy Curve of the LSTM model. The training accuracy in the figure starts at about 0.81% and rises to about 0.82%. The test accuracy rises to approximately 0.82% from a starting point of about 0.78%.Fig. 8 Performance visualization of LSTM model results.

Figure 8b shows the training and testing loss of the LSTM model. The training loss starts at about 0.645 and goes down to about 0.44. The test loss begins around 0.62 and gradually drops to about 0.57. Although there is no substantial distinction between the two lines, the training loss is always less than the test loss. This implies that the model is effectively expanding with new data. Over time, there has been a decrease in both training and test loss. From this, the model continues to evolve. The test loss is not that different from the training loss. This implies that there is not a significant overfitting of the model. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig. 8c, the model performs better; the train ROC curve has an AUC of 0.68, and the test ROC curve has an AUC of 0.65.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig. 9. The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix in Fig. 9a show the number of occurrences that were correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7997 instances were projected for class 0, 212 instances for class 1, and 15 cases are accurately anticipated on training data.Fig. 9 Graphical visualization of LSTM model results.

The diagonal cells of the matrix in Fig. 9b show the number of occurrences that were correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 2025 cases are successfully predicted for class 0, whereas 30 occurrences for class 1 and 2 instances for class 1 were forecasted.

Figure 10 shows the performance visualization of the GNN model in terms of accuracy, loss, and roc curve. Figure 10a demonstrates the training and testing accuracy of the RNN model. The training accuracy in the figure starts at about 0.79% and rises to about 0.81%. The test accuracy rises to approximately 0.806% from a starting point of about 0.82%. Figure 10b shows the training and testing loss of the RNN model. The training loss starts at about 0.65 and goes down to about 0.54. The test loss begins around 0.62 and gradually drops to about 0.57. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig. 10c, the model performs better; the train ROC curve has an AUC of 0.77, and the test ROC curve has an AUC of 0.66.Fig. 10 Graphical visualization of RNN model results.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig. 11. The diagonal cells of the matrix in Fig. 11a show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7985 instances were projected for class 0, 212 instances for class 1, and 0 cases are accurately anticipated on training data. The diagonal cells of the matrix in Fig. 11b show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 2027 cases are successfully predicted for class 0, whereas 38 occurrences for class 1 and 0 instances for class 1 were forecasted.Fig. 11 Graphical visualization of RNN model results.

Findings and discussion

This study utilized the ASHARE-884 dataset for green building energy consumption to predict the temperature and environmental sustainability of a smart building. In particular, for green buildings that significantly depend on local climate circumstances, the resolution of the climate statistics was not precise enough to capture localized temperature fluctuations, which are critical for accurately projecting energy usage. The dataset might not encompass enough time to include upward trends or variations in climatic patterns, which are crucial for comprehending energy usage patterns and producing precise forecasts. When combined with climatic data, historical energy consumption data makes it possible for researchers to validate and calibrate forecasting models successfully. Validated models can help make evidence-based choices for green building and operations and increase trust in the reliability of energy consumption projections.

There are advantages and disadvantages to using real-world datasets for DL and ML in the design of green buildings. The complexity and volume of the data, which need a large amount of processing power, as well as problems with data quality and consistency, such as inconsistent or incomplete data, are challenges. Complicating matters further are worries about data security and privacy as well as regulatory compliance. Furthermore, integration and benchmarking are challenging due to the dynamic and heterogeneous nature of the data and the absence of uniformity across many sources. Nonetheless, there are numerous opportunities in the areas of better occupant comfort and health through optimized indoor environments, enhanced predictive modeling for energy efficiency and sustainability, and better design and operation through data-driven insights. Optimization of operations and resource allocation can lead to cost savings, and regulatory adherence can be streamlined with automated compliance and sustainability reporting. Moreover, real-world data may encourage inventiveness, early adopters of ML and DL technology a competitive advantage. Applying machine and deep learning techniques to green buildings utilizing factors such as maximizing resource use, enhancing occupant comfort, and reducing the built environment impact to enhance environmental sustainability.

DL and ML Building energy modeling technologies can benefit from artificial intelligence approaches by increasing forecasting accuracy and calibrating models using actual data. Deep learning algorithms can discover links between building parameters and energy usage, making more realistic simulations that consider intricate interconnections and uncertainties possible. The effectiveness of the suggested model is evaluated using optimal indicators necessary for statistical analysis. Statistical analysis assesses the effectiveness, standardization potential, and usefulness of DL models. The degree to which a deep learning model can identify patterns and correlations in data, as well as the intricacy and refinement of its structure, indicate its complexity. Multiple architectural features determine the DL model’s complexity. A model becomes more complex as its number of parameters increases. Even though sophisticated models can capture complicated relationships in the data, they are increasingly prone to overfitting when improperly regularized. The quantity and kind of features a model uses can affect its complexity. Even while adding more characteristics can make the model more complex, not all characteristics can have a significant impact on the model’s performance. By adding penalty components and regularizing the loss function, various ways lower the complexity of the model. A reduction in the likelihood of overfitting occurs when extremely complex metrics are avoided. The study uses the GNN, LSTM, and RNN models based on DL to tackle green building environmental sustainability. The experiment results indicate that the proposed GNN and LSTM architecture functions more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Conclusion

Developers often use machine learning and other forms of advanced artificial intelligence, such as deep learning approaches, to help them complete their tasks more quickly and accurately. The goal of this research is to create a predictive model for GB design using ML and DL approaches to maximize resource usage, enhance occupant comfort, and decrease the environmental impact of the built environment throughout the GB design process. The proposed models are applied to a dataset, ASHARE-884. An exploratory data analysis (EDA) and data preprocessing techniques are applied, including cleaning, sorting, converting categorical data into numerical data, and normalizing the data. ML and DL techniques for green building design to enhance environmental sustainability. DL models such as GNN and LSTM perform more accurately and efficiently than all models and outperform conventional DL techniques for environmental sustainability in green buildings. However, since this research is limited regarding the dataset, this study can be extended by adding more feature datasets in further studies.

This study encourages future studies to develop a more robust ML and DL model with improved accuracy performance. Furthermore, other data preprocessing techniques will enhance the performance of models in the future. The study’s future directions will concentrate on addressing the limits of climate data resolution and period to increase the accuracy of energy consumption estimates. Longer periods and higher temporal and spatial resolution of climate information added to the ASHARE-884 dataset will improve its ability to capture long-term climatic trends as well as localized temperature changes. Moreover, integrating meteorological data with large historical energy usage data improves forecasting model calibration and validation considerably. Future research endeavors can leverage the current foundation to enhance the precision, dependability, and adaptability of models for energy consumption prediction and comprehension of the interaction between climate and energy use by exploring these study avenues.

Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R120), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author contributions

Shahid Mahmood: Writing—original draft, Project administration, Conceptualization. Huaping Sun: Supervision, Investigation. El-Sayed M. El-kenawy: Visualization, Software, Project administration. Asifa Iqbal: Writing—review and editing. Amal H. Alharbi: Software, Data curation. Doaa Sami Khafaga: Visualization, Formal analysis.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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