**1. Introduction**

Recently, as the amount of resources consumed by one person has increased, there are growing concerns about environmental problems caused by carbon dioxide emitted during energy generation and energy shortage problems [1]. Smart grid technologies have been gaining much attention because they help to solve these problems by enabling more e fficient use of energy [2]. A smart grid is an intelligent power grid that combines information and communication technology with the existing power grid and integrates the work of all users in the power network by using computer-based remote control and automation [3]. It allows monitoring, analyzing, controlling, and communication within the supply chain to improve e fficiency, reduce energy consumption and costs, and maximize the transparency and reliability of the energy supply chain [4]. In addition, by intelligentizing the power grid, it is possible to construct a bi-directional supply system such as a microgrid and distributed power supply system where suppliers and consumers can exchange information that they need [5].

Based on this information, energy prosumers can be more active in the trade of electricity. For instance, prosumers on the demand side can choose the supplier that can supply electricity at a lower price, and prosumers on the supply side can create opportunities to sell electricity more expensive.

Typical smart grids are closely related to various energy systems such as the energy storage system (ESS), renewable energy system (RES), combined cooling, heating and power (CCHP), and so on [6]. In particular, CCHP is a cogeneration technology that integrates an absorption chiller to produce cooling. Thermal energy produced during the power generation process is collected to meet cooling and heating demands via the absorption chiller and heating unit [7]. Besides, natural gas-based CCHP has the advantage of lower fuel prices and lower carbon dioxide emissions compared to existing fossil fuel-based power generations [8]. For the efficient operation of CCHP, accurate short-term load forecasting (STLF) is required [9]. STLF is the basis of the design and implementation of the control strategy of the CCHP system, and the results of the STLF affect the overall energy efficiency of the system directly [10]. CCHP uses the primary energy to drive the generator to generate electricity and then recycle waste heat using waste heat equipment. Therefore, running CCHP without accurate predictions can increase the unnecessary operation cost of the power generation facility [11].

Electric energy consumption can be affected by diverse factors, which include architectural structures, thermal properties of physical materials, lighting, time zones, climatic conditions, and electric rates [12]. In addition, there are complicated electric load correlations between current and previous times [13]. They should be considered appropriately for accurate electric energy consumption forecasting. For instance, many STLF models have proposed a single machine learning algorithm to consider them [14]. However, such models do not always provide good prediction performance because electric energy consumption patterns are intricate, and uncertain external factors can cause a shift in the demand curve [15]. Besides, the domains that they show good performance could be different. Thus, it is not effective to use a single STLF model for prediction in diverse domains. This limitation can be alleviated by combining multiple models of this type [16].

To address these issues, many previous studies have suggested a two-stage STLF model that uses linear regression in the second stage for improving the accuracy of electric load forecasting [17]. These models performed better than previous studies that use a single algorithm by combining the predicted values obtained in the first stage [18]. However, there still are many deficits in the linearly combined model. For instance, the fixed weights of the linear combination can ignore the importance of potential nonlinear terms, which leads to a reduction in prediction accuracy. Additionally, the linear combination can give poor forecasting results when there is a strong nonlinear relationship between individual predictors and outcomes [19]. South Korea is one of the highest energy consumption countries and is interested in using smart grids to improve energy efficiency [20]. However, although studies on the electric load forecasting model have been sufficiently conducted, there are not many cases of configuring a power system in conjunction with CCHP. We focus on the features of the Korean power system and develop an application for scheduling CCHP operations to provide a bi-directional benefit to power suppliers and users.

In this paper, we propose a novel two-stage STLF scheme based on nonlinear combination of forecasting methods to solve this problem. In the first stage, we build two STLF models based on extreme gradient boosting (XGBoost) and random forest (RF), which are known to be popular tree-based ensemble models in time series prediction. In the second stage, we build a deep neural network (DNN)-based STLF model to combine the predicted values of XGBoost and RF. Further, we propose an economic analysis-based operation scheduling scheme for CCHP to effectively utilize the results of the STLF. For instance, in Korea, electric rates and contract demand, especially for industrial services should be considered in the electric rate system. Contract demand indicates the instantaneous peak load contracted with the power supply company. Based on the contract demands, a power supply company can make a stable power plan. Basically, the lower the contract demand is, the lower the basic electricity bill is. Hence, to derive more accurate contract demands, the following policy is used: If the consumer sets the contract demand too low, a progressive tax penalty will be added to

the excess power, which results in higher electricity charges. On the contrary, if the contract demand is set too high, consumers have to pay unnecessarily high electricity bills. The economic analysis shows the electric rate and contract demand that should be made to achieve the lowest electric charges. In order to intuitively display the outcome of the economic analysis, a graphical representation of CCHP scheduling is shown with the amount of economic benefits gained from the schedule. Figure 1 shows the overall architecture of our scheme.

The main contributions of this paper are as follows:


**Figure 1.** Overall system architecture.

The rest of this paper is organized as follows: In Section 2, related studies on STLF are reviewed. In Section 3, we explain the input variables for constructing the STLF model. In Section 4, we describe the structure of our forecasting model, and then, explain how to make CCHP operational scheduling in Section 5. In Section 6, we describe some of the experiments for performance evaluation of the proposed model and CCHP operation scheduling. Finally, in Section 7 we summarize our study.
