Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Estimating And Forecasting Demand For Broad Money In Iran Through Cointegration Analysis And Stochastic Simulation

A comparative analysis of power demand forecasting with artificial intelligence and traditional approach

Power demand forecasting is a significant factor in the planning and economic and secure operation of modern power system. This research work has compared different forecasting techniques and opted to find out better technique in context of power generation, which varies rapidly from time to time. The dataset has been generated from yearly demand of electricity of Bangladesh for last five years...

estimating and forecasting demand for broad money in iran through cointegration analysis and stochastic simulation

Electric Power Demand Forecasting Based on Cointegration Analysis and a Support Vector Machine

In the process of cointegration analysis, electricity consumption is chosen as the explained variable, and GDP per capita, heavy industry share, and efficiency improvement are chosen as the explanatory variables; then a cointegration model is put forward, which shows that there is a cointegration relationship between the explained variable and explanatory variables. The explained and explanator...

Modeling for Energy Demand Forecasting

• Traditional approaches, including Box–Jenkins autoregressive integrated moving average (ARIMA) model, autoregressive and moving average with exogenous variables (ARMAX) model, seasonal autoregressive integrated moving average (SARIMA) model, exponential smoothing models [including Holt–Winters model (HW) and seasonal Holt and Winters’ linear exponential smoothing (SHW)], state space/Kalman fi...