*2.1. Short-Term Load Forecasting*

Typical approaches for STLF have been applied to statistical and machine learning methods for diverse external information such as time factors, weather conditions, and so on. Table 1 summarizes STLF-related studies using statistical techniques and machine learning. Veghefi et al. [22] proposed an STLF model based on the Cochrane–Orcutt estimation technique that combines the multiple linear regression (MLR) and seasonal auto-regressive integrated moving average (SARIMA) models to predict cooling and electric energy consumption e ffectively. Bagnasco et al. [23] constructed an artificial neural network (ANN)-based STLF model considering holiday indicators and weather conditions as input variables for forecasting electric energy consumption of Cellini Medical Clinic. Powell et al. [24] constructed an STLF model based on a nonlinear autoregressive model with exogenous inputs (NARX) for heating, cooling, and electric energy consumption of a district energy system. This study was unique because it covered a large-scale district energy system that simultaneously produced combined heat and power (CHP), chilled water thermal energy storage (TES), gas turbines, steam turbines, heat recovery steam generators (HRSGs), and auxiliary boilers for a large campus. Jurado et al. [25] constructed several prediction models using RF, ANN, and fuzzy inductive reasoning (FIR). They then compared the prediction models with an ARIMA model by predicting electric energy consumptions in three di fferent buildings at Catalonia Technical University, Catalonia, Spain. They confirmed that FIR showed the best prediction performance. Sandels et al. [26] presented a data analysis framework for identifying and generating models that can predict energy consumption on load level in North European o ffice building floors. The models were based on a simplified statistical approach that did not require detailed knowledge about the o ffice building floor. Grolinger et al. [27] constructed two STLF models based on support vector regression (SVR) and ANN. They considered time data, historical electric load data, and event information and compared their prediction performances with other methods for a large entertainment building in Canada. With daily data, the ANN model achieved better accuracy than the SVR. Gerossier et al. [28] presented a forecasting model for hourly household electric load based on quantile smoothing spline regression using the previous day's hourly load, last week's mid-load, and temperature. They computed the mean of the predicted quantile distribution and used it as a single-point forecast. These statistical approaches exhibited excellent performance for simple demand patterns but inaccurate prediction performance for intricate demand patterns. Chen et al. [29] developed a combination of a hybrid SVR model and multiresolution wavelet decomposition (MWD) to predict the hourly electric energy consumption of a hotel and mall. Dong et al. [30] proposed a seasonal SVR with a chaotic cuckoo search (CCS) named SSVRCCS to predict electric energy consumptions in the National Electricity Market and New York Independent System Operator. By using the CCS model, their proposed model can enlarge the population in cuckoo search (CS) to prevent the local optimal problem and increased the search space. By using the seasonal SVR model, it can deal with the seasonal cyclic nature of electric load for accurate and better prediction. However, the computational time is increased due to a large number of iterations. Fan et al. [31] proposed a novel electricity load forecasting model by hybridizing the phase space reconstruction (PSR) algorithm with the bi-square kernel (BSK) regression model, namely the PSR-BSK

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model. The authors investigated the performance of the model using an hourly dataset of NYISO, USA, and New South Wales market. Hong et al. [32] proposed an electric load prediction model, namely the H-EMD-SVR-PSO model, which combines the empirical mode decomposition (EMD) method, particle swarm optimization (PSO) algorithm, and SVR, to improve predictive accuracy. Based on electrical load data from the Australian electricity market, experimental results showed that the proposed H-EMD-SVR-PSO model received more satisfactory prediction performance than other comparable models.

These studies suggested the construction of non-generic forecasting models by considering the characteristics of buildings and microgrids. On the other hand, CCHP can be installed and used in various places with possibly different features. Moreover, different types of schedules may be required for CCHP depending on electric energy consumption patterns.


**Table 1.** Summary of several approaches for short-term load forecasting.
