# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 1 Introduction  
The energy conservation of public buildings is the focus of energy conservation [1, 2]. Scholars have found that the energy consumption intensity of hotel buildings is the highest, and the energy consumption of hotel air conditioning systems accounts for a large proportion of the total energy consumption. Therefore, energy-saving optimization of air conditioning systems is an important measure to reduce hotels' operational energy consumption [3, 4]. Establishing an accurate energy consumption model of central air conditioning systems is very important for system analysis and control optimization. At present, the commonly used energy consumption models of central air conditioning systems are the physics-based white box model, data-driven black box model, and gray box model generated by the mixture of both [5, 6, 7]. The white box model is mainly used in the design phase, and the performance of the central air conditioning system is predicted and analyzed through simulation. [PERSON] et al. [8] proposed a simplified calculation method of energy performance based on energy conservation, which is suitable for buildings with insufficient information on the central air conditioning system. The black box model measures the input and output variables of the central air conditioning system and uses a mathematical algorithm to determine the relationship between the input and output variables. [PERSON] et al. [9] took the operating parameters of inverter air conditioners as samples and established the air conditioning performance prediction model by using a neural network algorithm to realize the operation control optimization of inverter air conditioners. In fact, some heat- and mass-transfer processes of central air conditioning systems are difficult to be defineddirectly by equations due to a lack of information or insufficient training data. In this case, the gray box model is very effective. The basic structure of the model is formed by the physics-based white box method, and the internal parameters of the model are determined by the parameter-estimation algorithm based on the measured data of the central air conditioning system. [PERSON] (2016) proposed an improved artificial fish swarm algorithm, which takes the minimum power consumption of the water chiller and cooling tower as the objective function to solve the optimal load problem of the water chiller. [PERSON] et al. (2016) fitted the multivariate polynomial regression model of the water chiller and chilled water pump based on the actual operating parameters and optimized their operating parameters based on the simulation platform. [PERSON] et al. (2017) proposed a short-term hybrid forecasting model based on the Mean Impact Value-Improved Gray Wolf Optimizer-Support Vector Regression (MIV-IGWO-SVR) and applied this model to ice storage air conditioning cooling load forecasting. [PERSON] et al. (2017) proposed a prediction method based on dest simulation and support vector machine regression algorithm (SVR) so that the air conditioning system can respond to the indoor load change of near-zero energy consumption buildings in time. [PERSON] et al. (2017) investigated a forecasting method for univariate time series with a nonlinear, random, large fluctuation and presented two hybrid machine learning modeling methods--Chaos-support vector regression (Chaos-SVR) and wavelet decomposition (WD)-SVR. Research on the control optimization of air conditioning systems shows that the control of air conditioning systems based on feedback has a time delay and coupling, and accurate load forecasting is the premise to realize the optimal operation of air conditioning systems ([PERSON] et al., 2017; [PERSON] et al., 2017), while most of the above studies directly optimize the control of a device alone, which does not fully exploit the energy-saving potential of the system.