Electricity consumption, Peak load and GDP in Saudi Arabia: A time series analysis
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Energy is one of the most important resources of the national economy, which plays an important role in economic production and life more generally. Given its significance, this paper formulates prediction models for electricity consumption (EC), peak load (PL) and gross domestic product (GDP) in Saudi Arabia by employing the Autoregressive Integrated Moving Average (ARIMA) model; using time series data from 1990–2015. It also examines the relationships between EC, PL and GDP through a vector auto-regression (VAR) analysis, which includes Granger causality (GC) testing, impulse response, and forecast error variance decompositions (FEVD). The results show that ARIMA (1, 1, 1), ARIMA (0, 1, 0) and ARIMA (0, 1, 0) were the most appropriate univariate models of EC, PL and GDP, respectively, based on the Akaike information criterion. The results also revealed significant unidirectional granger causality from PL to EC and PL to GDP. The variance decomposition reveals that in the case of EC, the major changes arise from its own innovation and the contribution from GDP at the 1%.
22nd International Congress on Modelling and Simulation: Managing cumulative risks through model-based processes (MODSIM2017)
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Environmental Impact Assessment
Numerical and Computational Mathematics not elsewhere classified