1. Introduction
According to China Building Energy Consumption Research Report 2020, by 2018, the country’s total floor area reached 67.1 billion m
2, of which public floor area was about 12.9 billion m
2, accounting for 19% [
1]. However, the energy consumption of public buildings is generally higher, and the energy consumption of public buildings accounts for a more significant proportion of the total building energy consumption. In China, the total energy consumption per square meter of public buildings is more than twice that of residential buildings [
2]. As a type of public building, the library also has excellent energy-saving potential [
3]. In order to realize the goal of energy conservation and emission reduction in a library, it is of great significance to explore the library design method oriented by comprehensive performance optimization [
4].
The architects have made many attempts to optimize the performance of the library building, such as setting large windows, using light-colored skin, and setting sun shading components [
5,
6]. However, the effect of optimization can only be investigated after the completion of building construction in most cases, so it is essential to study the optimization and prediction of building performance in the early stage of the design [
7]. Because the comprehensive performance evaluation process is complex and time-consuming and the parameter setting is highly specialized, professionals usually conduct systematic and objective analysis and summary of the comprehensive performance of the scheme after the design is completed [
8]. Making a reasonable decision in the initial stage of architectural design can affect the comprehensive performance of the building at a lower cost [
9]. During the compact design process, the architects wanted more timely feedback during design. Therefore, the generative design method based on building performance simulation has emerged in recent years [
10].
With the deepening of research and practice, generative design and multi-objective optimization combined with better design methods have been widely used in architectural design [
11]. Manav et al. developed an approach that combines “modeling with building information” and “Machine learning” to rapidly provide building performance information [
12]. Yan et al. proposed a performance-driven early design optimization workflow, taking office buildings as an example, and introduced a genetic algorithm and XGBoost algorithm into the early design [
13]. Based on the process of “modeling-computation-optimization”, Zhang et al. proposed a form optimization method for large-space buildings, which applied a multi-objective genetic algorithm to perform iterative optimization of architectural modeling. Pareto Frontier, formed from the optimization results, provided sufficient alternative plans for designers [
14].
However, the design process based on building performance simulation and algorithm optimization are still to be improved. First, this design method has a high time cost and an incredible amount of work, which requires architects to set parameters for various schemes. At the same time, simulation and iterative optimization need much time. Secondly, this design method requires architects to be highly skilled in using various software [
15]. Currently, widely recognized building performance simulation software, such as Energy Plus, Equest, DesignBuilder, and OpenStudio, requires users to have a thorough understanding of building physics and set the parameters involved in the simulation in detail [
16]. However, in this process, it is difficult for architects to choose the appropriate parameters and obtain the optimal scheme of all aspects based on their experience [
17]. This kind of workflow of building comprehensive performance simulation and iterative algorithm optimization are combined to obtain a better scheme. Generally speaking, the process is complex, the workload is heavy, and the feedback cannot be timely. Moreover, the architect lacks subjectivity in adjusting various parameters in the design and selecting the final scheme [
18].
To solve these problems, construction industry researchers have begun exploring the combination of artificial intelligence and building design. With the updating and development of algorithms in recent years, architecture has introduced the fast prediction model based on machine learning model to solve problems [
19,
20,
21]. Santos et al. developed a prediction model based on artificial neural network (ANN) to obtain the final prediction model by training massive hourly data. In the study of existing public buildings, this model can obtain the prediction results of energy consumption and thermal performance with acceptable accuracy and no effort in modeling [
22]. Xie et al., based on a commonly used multi-layer neural network (MLNN), optimized the building layout by Genetic Algorithm (GA) and Back Propagation (BP) algorithm to predict the distribution of the Mean Radiation Temperature (MRT) around the building [
23]. Palladino et al. used ANN to establish a simplified algorithm to evaluate summer PMV using only three input variables (indoor air temperature, relative humidity, and clothing insulation) [
24]. Using data from 550 buildings, Xu et al. proposed a data-driven approach to summarize the effects of retroactive projects and predict future savings potential [
25]. Pittarello et al. found that ANN is very useful for assessing the energy consumption of buildings, supporting rapid comparative analysis of different schemes and facilitating subsequent optimization [
26].
Although machine learning has been widely applied in architecture in recent years, most of the current applications of machine learning algorithms in architecture solve regression problems, and few studies apply multi-classification prediction to architectural design. Previous research mainly focuses on predicting indoor or outdoor building performance and building energy consumption. Few researchers combine the simulation of comprehensive building performance, algorithm optimization, and scheme classification according to the advantages and disadvantages of building performance to guide architectural design. Second, most of the current performance prediction research tends to take specific buildings as the research object, and excessive pursuit of the accuracy of prediction results in poor generalization ability, which makes it challenging to combine the established prediction framework with the actual architectural design.
Because of the current research status, this paper constructs a library architectural design method based on the LightGBM algorithm, which is used to predict and classify the performance of different schemes at the initial stage of library design, providing ideas for architects’ subsequent design. In the Methods part, this paper selects a university library in Xinjiang, China, as the research object. The library fully considers the use of sustainable energy in the design and reduces building energy consumption while improving indoor comfort. Taking the library as the research object is very beneficial for exploring the energy-saving design of the library. The building scheme is generated by setting various building parameters, and much data are obtained by building performance simulation and multi-objective optimization (MOO). Then, by selecting the algorithm model, dividing the training set and test set, hyperparameter tuning, and cross-validation, an efficient and reasonable multi-classification algorithm model is built, which can quickly and accurately classify different schemes according to their performance and guide the subsequent design of architects. In the result section, the prediction results of the multi-classification prediction model established in this paper are summarized and analyzed, and the model’s prediction accuracy is investigated. In the Discussions section, the established LightGBM algorithm model is compared with several other mainstream algorithms, and the following research directions are analyzed and summarized. In the Conclusions part, the research results of this paper are summarized.
In this study, the innovations mainly include:
This paper proposes a design method that combines the generative architectural design process based on building performance with the LightGBM algorithm, which can guide architects to better carry out the subsequent design;
This paper uses the LightGBM algorithm to evaluate, predict, and classify library performance, and the possibility of applying LightGBM algorithm in architectural design is verified;
This paper compares the prediction performance of the LightGBM algorithm with several other commonly used multi-classification algorithms in detail, and the superiority of applying LightGBM algorithm in architectural design is verified.
4. Discussion
This paper proposes a library design process combining MOO and LightGBM algorithm. The design process has advantages in efficiency and practicability, which is convenient for architects to get feedback quickly and make a more reasonable design strategy in the initial design. Compared with previous studies that only carried out a numerical prediction for specific schemes, the proposed label classification of schemes according to their comprehensive performance is more intuitive.
In order to further verify the advantages of the LightGBM algorithm, this study uses Decision Tree, KNN, and Random Forest for comparative analysis. The classification prediction performance of the four algorithms is shown in
Table 8. As can be seen, the LightGBM algorithm has the highest F
1-Score of 0.851, which is an ideal classification prediction algorithm. The worst prediction algorithm was the Random Forest algorithm, with an F
1-Score of 0.816.
In addition, the efficiency of the LightGBM algorithm is noteworthy. Compared with the traditional process of “modeling-setting parameters-building performance simulation”, the trained LightGBM algorithm can accurately predict the building performance of the design scheme in a short time, significantly saving labor and time costs.
Nevertheless, objectively speaking, the classification prediction model proposed in this study needs to be further optimized. Through the confusion matrix of the prediction model, we can see that there are still some errors in the prediction results. Consider that the feature variables set in this paper are only 5, and the dataset is only 5000 groups. Due to the small data sets, the accuracy and generalization ability of the model can be further optimized. Follow-up research can consider adding more characteristic variables and expanding the number of data sets to try to build a more accurate and effective algorithm model.
Finally, it is worth noting that UDI is used as the primary reference variable in this study to predict and classify design schemes and reflect the indoor lighting situation of the library. In this paper, the lighting performance of the building is mainly investigated, but the thermal environment, acoustic environment, and other performance indexes are not thoroughly investigated. Due to the large area of the research object in this paper, it takes a long time to simulate the thermal environment of many different schemes, so it is not involved. Other performance indicators, such as APMV, DA, and building energy consumption, can be used as the standard of design scheme classification in subsequent studies. For example, APMV has a clear classification standard for thermal comforts, such as “0” corresponds to “comfortable” and “+3” corresponds to “hot”, which is very suitable for building a multi-classification algorithm model.
5. Conclusions
In order to quickly predict and effectively optimize the comprehensive performance of the library, this study analyzes the feasibility of the LightGBM algorithm applied to architectural design and constructs the corresponding research framework. Taking a university library in Urumqi as the research object, this study generates 5000 groups of design schemes by adjusting five feature variables and classifies the labels according to the comprehensive performance to train the LightGBM multi-classification prediction model. Second, the GridSearchCV method was used to adjust the hyperparameters of the prediction model, and the F1-score of the optimized LightGBM model reached 0.851. Finally, the supervised learning method constructed several multi-classification prediction models to compare the performance with the LightGBM model. The conclusion of this paper shows that the LightGBM algorithm applied to the early design of libraries can help architects to design the design scheme with excellent comprehensive performance quickly and effectively and has better performance than other prediction models.
Most of the previous studies focused on the numerical prediction of specific performance indexes of specific buildings, which can be studied on a single performance index but cannot quickly and intuitively reflect the comprehensive performance of buildings. The multi-classification prediction model proposed in this paper can classify different schemes according to the comprehensive performance of the building so that the architect can choose the better scheme in the early stage of the design.
However, this study also has many areas that deserve improvement. First, this study only focuses on the architectural form and facade design at the initial stage of library design and fails to consider other design variables more comprehensively. Second, this paper only focuses on studying the building’s light environmental performance, which can be further carried into the study of other performance objectives. Finally, the research framework proposed in this study applies to specific studies and can be used as a reference for similar studies, but its universality is limited.
Subsequent studies must consider more complex design variables and comprehensive performance indicators to carry out multi-objective optimization and fast and accurate prediction classification. Subsequent research should continue to explore the application of other algorithmic models in the field of architectural design to assist architects in solving complex problems in the design process and provide reasonable and practical solutions. We can also try to link it to BIM to make it more automated.