Selection of forecast model for consumption (four sectors) and transmission (two Piplines) of natural gas in Punjab (Pakistan) based on ARIMA model

Abstract

The main purpose of this study is to select an appropriate forecast model for Natural Gas Consumption and Transmission System. For ARIMA model, Box-Jenkins Approach (1976) has been adopted i.e. Stationarity of the series has been checked for each data set, correlogram has been estimated for identification of order of ARIMA model and a class of models has been estimated. Then, most adequate and appropriate model is selected by analyzing diagnostics checks. Later on, by comparing values of Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Standard Error (S.E.) of Regression, Root Mean Square Error (RMSC) and Theil Inequality Coefficient (TIC) for each model, forecast model has been finalized. In the end, forecasts have been made using models and compared these forecast values with the actual values for 2010 in order to check the accuracy of the model.

Cluster Analysis and Model Comparison Using Smart Meter Data

“…As discussed in the paper [ 14 ], we are going to implement the ARIMA model in SAS with different P, D, and Q values, where…”

“…In paper [ 14 ], the author identified the best ARIMA model for load forecasting. Two different data sets were used: one for consumption and the other for transmission.…”

“…This is due to the additional failure of the ARIMA method to calculate the peak size. The author in [ 14 ] used ARIMA for load prediction and found it to be accurate in forecasting the consumption of load and its transmission in the province of Punjab, Pakistan.…”

Modified dam deformation monitoring model considering periodic component contained in residual sequence

“…In addition, considering that the residual sequence contains certain noise and periodic components, a modified modeling method by extracting periodic component from the residual sequence of conventional statistical model is proposed in the study. Inspired by the application of singular spectrum analysis (SSA) in data processing [27][28][29][30] and the application of autoregressive integrated moving average (ARIMA) model in time series analysis, [31][32][33][34] the residual sequence obtained by conventional statistical model is processed and forecasted by SSA and ARIMA. Firstly, the conventional statistical model is established with stepwise regression model, and the residual sequence is reconstructed using the trend and periodic components extracted by SSA.…”

Combination forecast model for concrete dam displacement considering residual correction

“…Combination forecast model is applied in many research fields because it combines advantages of multiple models and its information mining function is powerful. 21 Considering that the complex nonlinearity of residual sequence and the relationship between structure displacement evolution and environment impacts must be investigated urgently, inspired by the application of wavelet analysis in signal processing 22,23 and the application of back propagation (BP) neural network and autoregressive integrated moving average (ARIMA) model in complex nonlinear analysis in hydrology, medicine, economics, and many other fields; 24–28 the combination forecast model for concrete dam displacement considering residual correction is established in this study. First, regression model is established on the basis of dam prototype observation data, and the fitting residual is decomposed and reconstructed by multi-scale wavelet analysis.…”