This page has only limited features, please log in for full access.
This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.
Yunsun Kim; Sahm Kim. Forecasting Charging Demand of Electric Vehicles Using Time-Series Models. Energies 2021, 14, 1487 .
AMA StyleYunsun Kim, Sahm Kim. Forecasting Charging Demand of Electric Vehicles Using Time-Series Models. Energies. 2021; 14 (5):1487.
Chicago/Turabian StyleYunsun Kim; Sahm Kim. 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models." Energies 14, no. 5: 1487.
This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).
Heung-Gu Son; Yunsun Kim; Sahm Kim. Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid. Energies 2020, 13, 2377 .
AMA StyleHeung-Gu Son, Yunsun Kim, Sahm Kim. Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid. Energies. 2020; 13 (9):2377.
Chicago/Turabian StyleHeung-Gu Son; Yunsun Kim; Sahm Kim. 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid." Energies 13, no. 9: 2377.
Peak load demand forecasting is important in building unit sectors, as climate change, technological development, and energy policies are causing an increase in peak demand. Thus, accurate peak load forecasting is a critical role in preventing a blackout or loss of energy. This paper presents a study forecasting peak load demand for an institutional building in Seoul. The dataset were collected from campus area consisting of 23 buildings. ARIMA models, ARIMA-GARCH models, multiple seasonal exponential smoothing, and ANN models are used. We find an optimal model with moving window simulations and step-ahead forecasts. Also, including weather and holiday variables is crucial to predict peak load demand. The ANN model with external variables (NARX) worked best for 1-h to 1-d ahead forecasting.
Yunsun Kim; Heung-Gu Son; Sahm Kim. Short term electricity load forecasting for institutional buildings. Energy Reports 2019, 5, 1270 -1280.
AMA StyleYunsun Kim, Heung-Gu Son, Sahm Kim. Short term electricity load forecasting for institutional buildings. Energy Reports. 2019; 5 ():1270-1280.
Chicago/Turabian StyleYunsun Kim; Heung-Gu Son; Sahm Kim. 2019. "Short term electricity load forecasting for institutional buildings." Energy Reports 5, no. : 1270-1280.
Xueyan Zheng; Sahm Kim. A Study on the Comparison of Electricity Forecasting Models: Korea and China. Communications for Statistical Applications and Methods 2015, 22, 675 -683.
AMA StyleXueyan Zheng, Sahm Kim. A Study on the Comparison of Electricity Forecasting Models: Korea and China. Communications for Statistical Applications and Methods. 2015; 22 (6):675-683.
Chicago/Turabian StyleXueyan Zheng; Sahm Kim. 2015. "A Study on the Comparison of Electricity Forecasting Models: Korea and China." Communications for Statistical Applications and Methods 22, no. 6: 675-683.