*2.2. Our Previous Works*

In this section, we briefly describe several STLF models that we proposed in our previous studies and their di fferences from the proposed model.

In [33], we built two STLF models using the ANN and SVR for four building clusters of a private university in South Korea. For the prediction, we considered not only weather information and time data but also university events, o ffice hours, and class hours. Subsequently, we evaluated the prediction performance of each model by using 5-fold cross-validation. The comparison showed that the ANN-based forecasting model had better performance than the SVR-based model. In [34], we proposed another STLF model based on an auto-encoder (AE) and RF. The AE was used to extract weather information features and time factors e ffectively. We constructed an RF-based forecasting model using feature extraction values and historical electric loads for day-ahead electric load forecasting. The model was evaluated using the electric energy consumption data of university campuses and the results showed that it gave a better performance than the proposed model in [33]. In [35], we proposed a recurrent inception convolution neural network (RICNN) that combines recurrent neural networks (RNN) and 1-dimensional convolutional neural networks (CNN) to forecast multiple short-term electric loads (48 time steps with an interval of 30 min). A 1-D convolution inception module was used to calibrate the prediction time and hidden state vector values calculated from nearby time steps. By doing so, the inception module could generate an optimized network via the prediction time generated in the RNN and nearby hidden state vectors. The proposed RICNN model was verified using the electric energy consumption data of three large distribution complexes in South Korea. In [36], we constructed diverse ANN models using di fferent numbers of hidden layers and diverse activation functions and compared their performances in a 30 min STLF resolution. To compare the prediction performance, we considered electric load data collected for two years from five di fferent types of buildings (including the dataset used in this study). The comparison showed that a scaled exponential linear unit (SELU)-based ANN model with five hidden layers had a better average performance than other ANN-based STLF models. In [37], we proposed a two-stage electric load forecasting model that combined XGBoost and RF using MLR for the e fficient operation of CCHP. To construct this model, an hourly load forecasting was performed using XGBoost and RF. The forecasting results were then combined using a sliding-window based MLR to reflect the energy consumption pattern. The model had a better prediction performance compared with several popular single algorithm-based forecasting models.

The di fference between the papers mentioned above and our paper is as follows.

The models in [33] were tailored for university campuses; they were challenging to apply for other types of buildings. The model in [34] used AE to extract features. However, since the performance of AE heavily depends on the size of the training set, it is challenging to show excellent performance if there is not enough quantity of data. In [35], we proposed the RICNN model. However, the RICNN, which purposed a probabilistic approach, is a di fferent purpose because we focus on day-ahead point load forecasting. In [36], the SELU-based ANN model with five hidden layers showed that the dataset we used in this study exhibited insu fficient prediction accuracy compared to the other building types because its electric loads are close to zero. In [37], we proposed a two-stage electric load forecasting model to combine XGBoost and RF using MLR. However, to use the forecasted values from one-stage more e fficiently, we have to consider existing input variables. Eventually, we further develop our research and propose integrated applications with CCHP operation scheduling and electric rate recommendations, not just ending with forecasts.
