Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Abstract

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).

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

“…Econometric models, which are based on economic principles and statistical methods, build mathematical models to make predictions. Common econometric models include time series models (e.g., autoregressive moving average (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), and autoregressive integrated moving average (ARIMA)) (Ma and Wang, 2019;Son et al, 2020;Zhang et al, 2021;Sun et al, 2023) and regression models (e.g., multiple linear regression (MLR) and vector autoregression (VAR)) (Youssef et al, 2021;Egbueri J and Agbasi J., 2022;Pannakkong et al, 2022). Although these models can capture trends, seasonality, and periodicity in price series, they are less capable of addressing nonlinear problems and large-scale datasets.…”