1. Introduction
Buildings use a substantial amount of energy to provide a pleasant environment for occupants and residents. Statistics show that buildings consume about 35% of the world’s final energy and cause 75% of the world’s greenhouse gas emissions. Most of the energy consumed in buildings is used for air conditioning; lighting; water supply; and transportation during the heating, ventilation, and air conditioning (HVAC) system’s life cycle, with air conditioning accounting for 60 percent [
1,
2,
3]. Therefore, the proper operation of various facilities is important to reduce energy consumption in buildings. The concept of a Building Energy Management System (BEMS) is being implemented to support energy and management efficiency at the operational level. A BEMS is an integrated system of measurement, control, management, and operation that provides optimized building energy management measures by monitoring the energy usage not only for efficient energy management but also to maintain a pleasant indoor environment.
The ability to predict energy consumption and demand is essential for optimizing energy performance from the design stage to the operational stage. The system’s design and the selection of appropriate facility capacities are determined during the design stage, and an optimal control plan and proper operations plan are established in the operational stage to improve the energy performance. Researchers have employed many techniques to predict energy consumption and demand, including machine learning, which has been used to predict power demand since the 1990s. One major type of machine learning model is the artificial neural network (ANN), which researchers have used extensively to investigate building energy predictions in various ways.
For example, Peng et al. proposed a combined model of two ANN models (Box and Jenkins) to predict cooling loads, with a less than 2.1% mean absolute percentage error [
4]. Cheng et al. used an ANN model with input variables such as building envelope performance, parameters, and the heating degree day and cooling degree day to predict building energy consumption quickly and effectively, resulting in a forecast that was 96% more successful than the existing method [
5]. Roldán et al. proposed a method for predicting short-term building energy consumption using an ANN-based time–temperature curve prediction model [
6]. The input variables included temperature, type of day, etc., which can affect energy consumption. Testing in real buildings for over a year resulted in a high predictive accuracy [
6]. Turhan et al. used an ANN model to predict the thermal load of an existing building based on the width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio of the building, etc., and compared the results with those obtained from a building energy simulation tool [
7]. They observed a good correlation between the ANN model results and the building energy simulation tool results, with an average absolute percentage of 5.06% and a success forecast of 0.977 [
7]. Ferrito et al. developed an ANN model that uses monthly building electrical energy consumption data [
8]. The predicted accuracy of the developed model indicated that a root mean square percentage error of 15.7% to 17.97% could be obtained [
8]. Ahmed et al. used weather data to compare and analyze the predicted power load performance of ANN and random forest (RF) models for a single building [
9]. To improve the predictions, the ANN models achieved an average coefficient of variance of the root mean square error (CV(RMSE)) of 4.91% by extracting and removing normalized techniques and input variables, whereas the RF models produced an average CV(RMSE) of 6.10% by changing the tree depth [
9]. Li et al. proposed ANN-based forecasting methods that could quicken the prediction of the energy consumption of complex types of buildings in the initial design phase [
10]. They proposed a method to simplify a complex type of building into several blocks. As a result, the relative deviation of heating and cooling energy consumption was within ±10%, and the relative deviation of total energy consumption was within 10 percent [
10]. Ding et al. used ANN models and a support vector machine (SVM) in a study of prediction accuracy by combining eight input variables [
11]. When K-means and hypersarchical clustering methods were used to obtain a combination of optimized variables, the accuracy was better than otherwise, and the historical cooling capacity data had the greatest impact on the predicted accuracy [
11].
Koschwitz et al. predicted data-driven thermal loading using two nonlinear autoregressive exogenous recurrent neural networks (NARX RNN) of different depths and ε-SVM region models [
12]. They used historical data from non-residential regions in Germany for training and testing to predict the monthly load. The evaluation results show that the NARX RNN was more accurate than the ε-SVM region model [
12].
In short, various studies of machine learning methods, including ANN models, have been conducted in the field of building energy. Predictions of the energy consumption and cooling loads of buildings show a high prediction accuracy.
In a study on predicting air handling unit (AHU) energy consumption, Niu et al. conducted thermal energy consumption forecasts for AHU using AutoRegressive with eXternal inputs (ARX), State Space (SS), Subspace state space (N4S), and Bayesian Network (BN). All four models satisfied the criteria in the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) guideline 14, and Bayesian Network’s forecast results were the most accurate [
13]. Le Cam et al. used a closed-loop nonlinear ANN model to predict the supply fan power demand of an AHU. During the test period, the Root Mean Squared Error (RMSE) was predicted at 5.5% and the CV (RMSE) at 17.6%. Fan electricity demand was predicted to be 1.4 kW RMSE, CV(RMSE) 30%, over 6 h. A sensitivity analysis shows that reducing the size of the training data set from 23 days to 4 or 8 days does not adversely affect the RMSE values [
14].
In addition, a study of chiller plants with an absorption chiller showed that Adnan et al. used ANN to model the baseline electrical energy use of chiller system, providing a higher predictive performance when ANN is over 93% R, and showing small error values for mean square error (MSE) and mean absolute percentage error (MAPE) for the selected ANN structure [
15]. Lazrak et al. used ANN to forecast energy consumption under the weather conditions of the solar combisystem combined with an absorption chiller, with annual energy forecast errors mostly showing less than 5%. As such, it can be seen that ANN techniques are valid for predicting energy consumption for AHUs and chillers. However, there were not many studies of individual components of HVAC systems for energy consumption forecasts in buildings [
16].
This research team is developing a centralized air conditioning system and energy management technique for BEMS applications and has researched energy consumption and load predictions based on ANN (Artificial Neural Networks). Using ANN models, the team conducted a prediction study of chiller energy consumption and achieved results that satisfy the American Society of Heating, Refrigerating, and Air-Conditioning Engineers
(ASHRAE) criteria, with an average CV(RMSE) of 19.49% in the training period and an average CV(RMSE) of 22.83% in the testing period [
17]. In addition, the team conducted studies to optimize the cooling load prediction model based on MATLAB’s NARX (with eXigenous) Feedforward Neural Networks model to confirm that a forecast accuracy of less than 7% CV(RMSE) can be obtained depending on the conditions [
18]. The accuracy of the ANN prediction model was verified through prior research, whose results were based on the data generated by simulation programs. In this study, the prediction accuracy of the ANN prediction model was investigated using both the air handling unit (AHU) and absorption chiller operations data from an actual building.
5. Conclusions
In this study, the energy consumption of the AHU and absorption chiller in an actual building was forecast using prediction models that are based on ANNs. The performance of the ANN prediction models that are based on training size was evaluated by collecting a month’s worth of driving data during the cooling period.
The performance of both the AHU model and the absorption chiller model was verified in this study. The results for the AHU prediction model show that the CV(RMSE) ranged from 13.27% to 15.25% for the training period and 19.42% to 19.53% for the testing period. The MBE ranged from 4.03% to 4.97% for the training period and 3.48% to 4.39% for the testing period. The results for the absorption chiller prediction model show that the CV(RMSE) ranged from 24.64% to 25.58% for the training period and 27.12% to 29.39% for the testing period. The MBE ranged from 2.59% to 3.40% for the training period and 1.35% to 2.87% for the testing period. These results satisfy ASHRAE guidelines.
As a result of predicting energy consumption using actual data, the AHU model showed an error rate of more than 10% and a relatively high standard deviation in the testing period by time step. However, when the training period and testing period results were combined, the error rate ranged from 0.22 to 1.11% in the training period and 0.17~2.44% in the testing period. The absorption chiller model showed a high error rate range of 19.73% to 28.54% per time step. The total energy consumption of the training period showed an error rate ranging from 0.22 to 2.12%, but the error rate was somewhat higher at 11.67% to 15.18% in the estimation of the total consumption of the testing period.
When the energy consumptions of both the AHU and absorption chiller were predicted together, the predicted results by time step showed a high error rate, whereas the predicted results for the training and testing periods combined showed a low error rate. The prediction models used in this study were more effective in forecasting over a certain time interval than for smaller sections. The predicted performance indicators, CV(RMSE) and MBE, satisfied the performance criteria of ASHRAE and showed a modest error rate in predicting energy usage over the entire period, indicating that ANN-based prediction models had adequate predictive performance.
However, it seems that errors occurred because the models used in this study had high variance in the form of high variance-low bias, which was somewhat higher than the CV(RMSE) at the test period and the error rate at maximum of 15.18% at the test period when predicting the absorption chiller. In addition, the data used in the study were collected shortly after the BEMS was installed in the target building, and the accuracy was insufficient. Therefore, a high error is thought to have occurred due to the inaccuracy of the data itself.
However, despite the poor quality of the data, some predictive accuracy has been confirmed, and the performance of the ANN-based predictive model has been verified. In order to achieve better results, it is deemed necessary to improve the predictive model and verify the data set to avoid overfitting and underfitting. To improve the predictive model, studies will be conducted such as optimizing the parameters of the NARX feedforward neural network model for improved accuracy when there is not enough data, and comparing them with other machine learning models. To improve the quality of the data set, more HVAC operation data will be collected and the correlation with the energy consumption is analyzed to verify the data set through various methods. such as the analysis of the results of the correlation of the input value and cross validation methods.