3.1. Typical Hotel Energy Model Establishment
After conducting a statistical analysis of the drawings of hotel buildings in Guangzhou, the input parameters of typical hotel building models can be classified into four categories:
Architectural geographic information;
Building energy equipment information;
Internal heat source information;
Building geometry information.
The first three types of information are determined according to the “Energy Efficiency Design Standards for Public Buildings” (GB 50189-2015) in China [
22], and the specific types and value ranges are shown in
Table 1. The building geometric information is then summarized, based on the statistical and research results, and a typical building model is established.
Due to the large number of building feature factors and their interrelationships in this study, regression analysis is used to reduce the amount of input variables. If the R2 of 1 variable surpasses 0.8, we can use the regression equation to replace this input variable. With the help of the box plot method and regression analysis statistics, it can be inferred that the typical high-rise hotel building mode comprises 14 floors of guest rooms and 4 floors of public areas, giving 18 floors in total. The guest room floors stand at 3.4 m in height, while the public area floors measure 4.8 m in height, resulting in a total height of 66.8 m.
According to our research on the drawings, there are two types of layout methods for the guest room floors in high-rise hotel buildings: strip layouts and square layouts. These layout methods can exhibit contrasting characteristics in terms of geometric space, thermal performance, and other aspects. In both layout methods, guest rooms can be standardized to the same unit, based on their weighted average area. Therefore, only the average area of this unit should be considered. For public areas within hotels, the function type and its corresponding area are counted. The various parameter values of both guest room and public area floors are presented in
Table 2.
For the strip floor plan, the area of the non-temperature-controlled zone can be obtained through regression analysis:
where
is the area of the non-temperature-controlled zone in the strip floor plan,
is a single room’s area, and
is the number of rooms on one side in the strip floor plan.
For the square floor plan, the area of the non-temperature-controlled zone can be calculated by:
where
is the area of the non-temperature-controlled zone in the square floor plan,
is the length of the square floor plan,
is a single room’s length,
is the corridor width, and
is the width of the square floor plan.
Out of the 78 drawings analyzed in this study, 59 drawings contain public area information, and 49 drawings provide detailed introductions to the public areas. By dividing the frequency of each function type of public area into 25% units, we can classify them into four levels. The first three levels are set in descending order of frequency, while those falling below 25% are considered special values and are excluded from the calculation. The statistical results are presented in
Figure 1. In the various designs, the areas of the public spaces range from 400 to 6000 m
2, making it challenging for a single value to completely cover the different levels of public areas in hotel buildings. Therefore, the area of a public space is counted separately, according to the corresponding function. The statistical results are then classified according to their levels, as shown in
Table 3.
Other research on typical building models has shown that the spatial location of public areas has no significant impact on E
H when the area is fixed [
23,
24]. Therefore, after determining the area, the high-rise hotel podium model can be simplified into simple geometric shapes. According to statistics, the width-to-length ratios of strip and square layout podiums are 30% and 80%, respectively.
In summary, a typical building model can be established as having a north-south orientation, a total of 18 floors, and a total height of 66.8 m. There are 308 guest rooms, each with an average area of 40 square meters and an aspect ratio of 2:1. There are 20 guest rooms on each floor, and the corridor width is 2.2 m. Within each guest room, the bathroom area of 8 m
2 accounts for 20% of the total area. For the strip layout model, there are 10 guest rooms on the north and south sides, divided by the service area in the middle. For the square layout model, there are 2 guest rooms on the short side and 8 guest rooms on the long side, with a service area in the middle as well. The ratio of the window-to-wall area (RWR) for the guest room floor is set at 34%, while for public areas, the average RWR value is set at 67%, based on the statistical data. The typical geometric models of two types of high-rise hotels are shown in
Figure 2.
3.2. Sensitivity Analysis
On the basis of the typical hotel building model, EH can be calculated via EnergyPlus. After that, we can use EH as a metric for conducting sensitivity analyses of the various parameters, including geometric feature parameters, internal heat source parameters, and thermal parameters, to verify their influence. Initially, we assume that the relationship between this parameter and building energy consumption conforms to a linear model, and then use the standardized regression coefficient (SRC) to determine the fitting effect. The sensitivity of parameters to energy consumption can be determined by the absolute value of the SRC, and the larger the absolute value, the more important the parameter is. A positive value indicates a positive correlation between the parameter and the model output, while a negative value indicates the opposite.
As the calculation results are extensive, the R programming language was used to compile and calculate the standard regression coefficients for each parameter. The specific code segments are provided in
Appendix A and
Appendix B, and the calculation result is shown in
Figure 3.
Parameters with an SRC greater than 0.5 are considered to have a significant impact on the total energy consumption of the building, as follows:
Geometric feature parameters: Total number of floors (FN), number of rooms on one side (RNP, LRNQ, SRNQ), area of a single room (RA), temperature-controlled area of public space (CAH), area ratio of the window to the wall (GWR), and the height of public areas (PH);
Internal heat source parameters: Energy efficiency ratio of chillers (COPn), hot water consumption (DHW), boiler efficiency (BTE), and guest floor setting for temperature in summer (STG);
Thermal parameters: North external window heat transfer coefficient (Un), east external window heat gain coefficient (SHGCe), heat transfer coefficient of the roof (KR), and heat transfer coefficient of an external wall (KE).
From this, it can be concluded that focusing on the parameters listed above during the design and management stages of high-rise hotel buildings in Guangzhou and the surrounding areas can effectively improve the efficiency of energy usage.
3.3. Parametric Prediction Model
The process of establishing an energy consumption prediction model based on machine learning methods can be briefly described according to the steps shown in
Figure 4 [
25].
The establishment of a machine learning model requires a large amount of input data. In this study, a method employing the computer batch generation of models was used to construct an input database. While generating hotel building models in bulk, based on the sensitivity analysis results given above, those parameters having a significant impact on the result were designated as variables, while other input parameters with little impact were replaced by constants. The geometric parameters were derived from the typical hotel building model, while the thermal and internal heat source parameters were calculated using benchmark values, and the meteorological data were taken from the typical annual meteorological parameters of Guangzhou. The quantitative values and ranges are shown in
Table 4. Given the numerous input data categories and diverse dimensions, variations between variables could potentially impact the outcomes of certain learning model calculations. To eliminate these effects and ensure comparability, we utilized the StandardScaler tool from the Sklearn package to preprocess the data.
According to
Table 4, there are 15 types of variables and 20 types of fixed-value data constructing the database. To guarantee the convergence of the calculation outcomes, the sampling frequency was set to 2500 times for each of the strip and square layout models. In total, 5000 energy consumption prediction models generated by random combination sampling were inserted into EnergyPlus for energy consumption simulation. After collecting the energy consumption (E
H) data via the R programming language, the results indicate a normal distribution with no evident outlier data. The specific outcomes are as follows:
For the strip layout hotel model, the E
H ranges from 93.14 to 153.60 kWhm
−2, mainly distributed between 113.82 and 127.10 kWhm
−2, with a mean of 120.67 kWhm
−2, a median of 120.24 kWhm
−2, and a standard deviation of 9.68, as illustrated in
Figure 5a. For the square layout hotel model, the E
H ranges from 111.94 to 152.23 kWhm
−2, mainly distributed between 123.94 and 133.38 kWhm
−2, with a mean of 128.74 kWhm
−2, a median of 128.38 kWhm
−2, and a standard deviation of 6.74, as displayed in
Figure 5b.
At this point, the preliminary work of data collection and organization, data processing, and variable screening has been completed. Moving forward, appropriate modeling methods will be determined to refine and train the prediction model, followed by the relevant evaluation and validation. We selected 14 common machine learning models and used 5000 sets of generated models as the input dataset. The k-fold cross-validation method (k = 10) was used to calculate and predict the E
H. The average and variance from ten training sessions were calculated to compare the generalization and stability of different models, enabling us to identify the optimal model.
Table 5 presents the results. After comparison, it can be inferred that for both datasets, the predictive performance of each model exhibits a similar trend. Among them, the R
2 of quadratic polynomial regression is above 0.95, and the variance is less than 0.01. Considering its accuracy and stability, this model was selected. The relationship between the calculated and predicted values is shown in
Figure 6.
To verify the accuracy of the prediction model, we designated the prediction values from the typical hotel building model as the benchmark for E
0 and compared them with the E
H from the prediction model. A comparison of the results is presented in
Table 6. From the table, it is evident that the bias falls within the range of 2.38% to 8.11%, which is considered acceptable.
To further validate the accuracy and practicality of the prediction model, we compared the EH from actual hotel projects (both strip and square layouts) with the prediction model. Prior to using EnergyPlus to calculate the energy consumption of the actual hotel project, we used the OpenStudio plugin in SketchUp to establish the geometric model according to the architectural drawings. We then inputted the internal heat source parameters, thermal parameters, and energy equipment parameters according to the drawings and used the meteorological parameter file in Guangzhou for simulation. As a result, it was calculated that the EH from an actual strip layout hotel building project was 152.87 kWhm−2, and the prediction value was 135.67 kWhm−2, resulting in a bias of 11.25%. The EH from an actual square layout hotel project was 157.48 kWhm−2, and the predicted value was 148.87 kWhm−2, resulting in a bias of 5.59%.
From the comparison results, it can be inferred that despite a minor bias between the prediction model and the actual project, considering such factors as special equipment and actual personnel activities, the bias remains within an acceptable range. This suggests that the prediction model demonstrates good predictive capabilities and can accurately facilitate the numerical analysis of hotel energy consumption.