Predicting energy use in construction using Extreme Gradient Boosting

Introduction

The annual rise in global energy use is one of the direct consequences of economic and population expansion. Energy use in the construction sector, which accounts for an average of 20.1% of the world’s total energy use, is a vital aspect of the global energy consumption picture (Conti et al., 2016). In many nations, this proportion is substantially greater; for example, in China and the United States, it contributes up to 21.7% and 38.9% of total energy use, respectively (China Association of Building Energy Efficiency, 2021; Becerik-Gerber et al., 2014). This rising energy use worsens global warming and causes natural resource depletion. Therefore, increasing the efficient use of energy in the construction sector is essential because it lessens the risk of global warming and enhances sustainable growth. To effectively manage diverse technical activities, e.g., demand response during construction (Dawood, 2019), organization of urban power systems (Moghadam et al., 2017), and defect detection (Kim et al., 2019), prediction of energy demand is crucial in preventing ineffective energy use in many sectors (Min et al., 2023; Li et al., 2022), especially construction (Liu et al., 2023; Zhang, Li & Yang, 2021). Besides, a good estimation of energy demand helps to create energy-saving plans for heating, ventilation, and air conditioning (HVAC) systems (Du et al., 2021; Min, Chen & Yang, 2019). To address this issue, several computation frameworks were proposed to predict energy use. Zhao & Magoulès (2012) performed a comprehensive review of numerous computational frameworks developed to estimate construction energy use. According to their investigation, all approaches for predicting energy use may be generally categorized into three groups: statistics-based, engineering-based, and machine learning-based techniques.

Statistics-based approaches were employed to model the correlation between energy use and involved attributes with a parametrically defined mathematical formula. Ma et al. (2010) developed models using multiple linear regression and self-regression techniques and essential energy consumption features characterized by particular population activities and weather conditions. The least squares method was used to estimate parameters as well as approximate monthly energy consumption for broad-scale public housing facilities (Ma et al., 2010). Conducting the principal component analysis, Lam et al. (2010) discovered three significant climatic variables, comprising dry-bulb temperature, wet-bulb temperature, and global solar radiation, to create a novel climatic index. After that, regression models were built to establish an association focusing on the daily period between the climatic index and the simulated cooling demand. Despite fast and simple calculations, these approaches often lack flexibility and have poor prediction ability due to limitations in handling stochastic occupant behaviors and complicated factor interactions between factors (Ahmad et al., 2014).

Applying physics’ concepts and thermodynamic calculations, engineering-based approaches determine the energy use of each building component (Zhao & Magoulès, 2012). Although the defined relationships between input and output variables improve the model’s explainability, these approaches need curated sources of building and environmental data whose accessibility is very limited (Zhao & Magoulès, 2012). Some researchers have attempted to reduce the complexity of engineering models in order to improve their ability to accurately predict energy use. Yao & Steemers (2005) developed a straightforward approach for generating a load profile (SMLP) to predict the breakdown of everyday energy consumed by electrical household appliances. This technique estimated seasonal energy demand since the average daily energy (consumed by these appliances) seasonally fluctuated. Based on frequency response characteristic analysis, Wang & Xu (2006) obtained simple physical properties to determine the model parameters. In addition, they used monitored operating data to identify parameters for a thermal network of lumped thermal mass to describe a building’s interior mass (Wang & Xu, 2006). The prediction accuracy, however, is one of the most concerning issues because the simplified model may be somewhat underfitting (Wei et al., 2018).

Machine learning-based approaches utilize either traditional machine learning (e.g., random forest, support vector machines) or deep learning (e.g., artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN)) for modeling by learning from historical data (Nora & El-Gohary, 2018; Tian et al., 2019). These machine learning-based approaches usually exhibit better performance compared to other methods, especially in event detection (Wang et al., 2023; Sun et al., 2023c; Ren et al., 2022) and other applications using time-series data (Sun et al., 2023a, 2023b; Long et al., 2023). To predict perennial energy use, Azadeh, Ghaderi & Sohrabkhani (2008) suggested using ANN in combination with an analysis of variance. The approach was demonstrated to outperform the standard regression model. Hou & Lian (2009) developed a computational framework using support vector machines to estimate the cooling demand of an HVAC system, and their findings showed that it was dominant over autoregressive integrated moving average (ARIMA) models. Tso & Yau (2007) used decision tree (DT), stepwise regression, and ANN to estimate Hong Kong’s power consumption. The results suggested that the DT and ANN techniques performed somewhat better during the summer and winter seasons, respectively. However, many traditional machine learning approaches use shallow frameworks for modeling, reducing prediction efficiency. As black box models, deep neural networks do not well explain physical behaviors, but they can effectively learn abstract features from raw inputs to build superior models (Ozcan et al., 2021). Cai, Pipattanasomporn & Rahman (2019) constructed prediction frameworks using RNN and CNN to measure time-series building-level load in recursive and direct multi-step approaches, respectively. Compared to the seasonal ARIMA model with exogenous inputs, the gated 24-h CNN model had its prediction accuracy improved by 22.6% in the experiments. To calculate electric load consumption, Ozcan, Catal & Kasif (2021) suggested dual-stage attention-based recurrent neural networks consisting of encoders and decoders. Experimental results revealed that their proposed method vanquished other methods (Ozcan, Catal & Kasif, 2021). A hybrid sub-category of deep learning called deep reinforcement learning (DRL) blends reinforcement learning decision-making with neural networks (Mnih et al., 2015; Levine et al., 2016; Sallab et al., 2017). DRL approaches are often used in the construction industry to investigate the ideal HVAC control (Wei, Wang & Zhu, 2017; Huang et al., 2020). DRL approaches obtained promising outcomes for energy use prediction (Wei, Wang & Zhu, 2017; Huang et al., 2020). Liu et al. (2020) examined the efficacy of DRL approaches for estimating energy consumption, and their results suggested that the deep deterministic policy gradient (DDPG) method had the most predictive power for single-step-ahead prediction. Although the deployment of DRL approaches yields initial success, further research is required to determine its ability to solve this issue.

In this study, we proposed a more effective method to predict energy use in the construction sector using four tree-based machine learning algorithms, including random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGB), and gradient boosting (GB). Four feature sets are created to combine with four learning algorithms to form 16 models. The four feature sets correspond to four development and testing scenarios: (1) using 1–3 historical hours, (2) using 1–5 historical hours, (3) using 1–7 historical hours, and (4) using 1–9 historical hours before prediction time as input features. The best-performing models are then selected as the final models for evaluation.

Materials and Methods

Overview of method

Figure 1 provides a flowchart of the modeling strategies of our work with two stages: validation and testing. The validation stage aims to select the best model, while the testing stage focuses on evaluating the performance of the selected one. The modeling steps of both stages are similarly designed. As the samples are time-series, training, validation, and test data are partitioned in time-sequential order. The validation and test sets contain 1,440 and 2,160 samples, which correspond to 60 and 90 days, respectively. Initially, the model is developed with the training data and then evaluated on a timely ordered list of seven sub-validation sets before being retrained. Each sub-validation set contains 336 validation samples, which are equivalent to 14 days. After 7 days, the model is retrained with an updated training set, which is a combination of previous training samples and 7-day validation samples. This process is repeated until no validation samples are left. After completing the validation stage, we compare the performance of these models to select the best one. The best model is then retrained with the new training set, which contains all training and validation samples. The obtained model is finally evaluated on the test data in the same manner as in the validation stages.

Data processing and featurization

Figure 2 describes the main steps in data sampling, processing, and featurization. After obtaining the training, validation, and test sets, we processed the data to generate their feature sets. The original data frame has only two columns: ‘recorded time’ and ‘energy used’. We created two types of features, including ‘historical feature’ (Fig. 2B) and ‘date feature’ (Fig. 2C). The historical features are energy values that were recorded $1,2,...,n$ h before the prediction time with $n=48$ (2 days). The label ${}_i$ (value of energy used) of sample ${}_i$ is filled to the column ordered ${48}^{th}$ of sample ${}_{i+1}$. The columns ordered ${48}^{th},{47}^{th},{46}^{th},...,1^{st}$ present the recorded values of energy used before $1,2,3,...,48$ h, respectively. The first sample was recorded on ‘January 1, 2015 at 00 h’ with undetermined historical features. The next 47 samples have their historical features determined with $1,2,3,...,47$ recorded values. Since the first 48 samples have historical features containing undetermined values, we removed them from the dataset. The samples recorded after ‘January 3, 2015 at 00 h’ have the historical features of all determined values. The date features of a sample give information on ‘day of year’, ‘whether it is a holiday’, and ‘when it is recorded’. Gaining information on whether a day is a holiday because the energy use in holiday time will be significantly different from daily energy use. Besides international holidays, Eastern countries like China celebrate some holidays based on the lunar calendar (e.g., Chinese New Year). The historical and date features are expressed as 48-dimensional and three-dimensional numeric vectors, respectively. To select the best feature set, we examined three scenarios, including using only historical features, using only date features, and using combinatory features (Table 2).

Table 2:

Scenarios for model development and evaluation.

Scenario	No. of features	Annotation
1	48	Using 1, 2,…, 48 h before prediction time as historical features
2	3	Using day of year, holiday information, and recorded hour as date features
3	51	Using both historical features and date features as combinatory features

DOI: 10.7717/peerj-cs.1500/table-2

Results and discussion

The model’s performance was evaluated using two metrics: root mean squared error (RMSE) and mean absolute percentage error (MAPE).

(1) $$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{\sum _{i=1}^N\frac{{({\hat{y}}_i-y_i)}^2}{n}},$$

(2) $$\mathrm{M}\mathrm{A}\mathrm{P}\mathrm{E}=\frac{1}{n}\sum _{i=1}^N|\frac{A_i-F_i}{A_i}|,$$where $n$ is the number of samples, $\hat{y}$ is the predicted value, and $y$ is the true value.

Model evaluation

Figure 3 gives information on the variation of prediction error over multiple time points of test data. The results indicate that the prediction errors show downward trends over the testing period. The errors tend to increase when the time point’s distance to the retraining point is large and then immediately drop once we retrain the model with updated training data. Figures 3A and 3B visualize the changes in RMSE and MAPE over 76 time points, where each time point represents the 14-day period with between-point distances of 1 day. Besides, Figures 3C and 3D illustrate the performance of the best and worst predicted time points over 336 consecutive hours. To quantify the variation of prediction error, we computed the mean, standard deviation, and 95% confidence interval (95% CI). The RMSE achieves a mean of 109.00 and a standard deviation of 37.07, while the MAPE obtains a mean of 0.24 and a standard deviation of 0.08. The 95% CI values of RMSE and MAPE are [100.47–117.52] and [0.22–0.26], respectively (Table 5).

Table 5:

The statistical values of metrics on the test set.

Metric	Statistics
	Mean	Standard deviation	95% Confidence Interval
RMSE	109.00	37.07	[100.47–117.52]
MAPE	0.24	0.08	[0.22–0.26]

DOI: 10.7717/peerj-cs.1500/table-5

Conclusions

The experimental results confirm the robustness and effectiveness of machine learning, especially Extreme Gradient Boosting (XGB), in developing computational models predicting energy use. The achieved results indicate that the XGB models work more effectively than other tree-based models to address this issue. Also, the selection of a suitable historical period is essential to improving model performance.

Supplemental Information