Electricity demand for Sri Lanka : A time series analysis

By Himanshu A. Amarawickrama, L. Hunt. 2008

257 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/44890fdfe42be3a0d7859fb377c56e1d2596e7a0

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka (2010, background): “Amarawickrama and Hunt (2008) also estimate electricity demand functions for Sri Lanka using six econometric techniques.”
Medium-term electric load forecasting using singular value decomposition (2011, background): “Results showed that there is a variation of around 452 MW in the base forecast peak demand [18].”
Short- and long-run electricity demand elasticities at the subsectoral level: A cointegration analysis for German manufacturing industries (2015, background): “As stated by Amarawickrama & Hunt (2008), this standard log-linear specification, apart from the obvious advantages, i.e. its simplicity, its straightforward interpretation and the limited data requirements, according to Pesaran et al. (1998) outperforms more complex models.”
Evaluation of time series techniques to characterise domestic electricity demand (2013, unknown): “Multiple Regression/ Probabilistic Yes [28e31] Yes [1,32e38] Yes [1,28,29,32e37] Yes [30,31,38] Widely used for generating standard load profiles (as shown in Fig.”
Carbon emissions and oil consumption in Saudi Arabia (2015, methodology): “Dilaver and Hunt [7,8] used the STSM approach to predict the industrial electricity demand, aggregate electricity demand, and residential electricity demand in Turkey.”
Forecast of electricity consumption and economic growth in Taiwan by state space modeling (2009, methodology): “On the other hand, the multivariate models of neural network techniques [29,30], regression and econometric approach [31–33] have been also applied in predicting EC.”
How significant is energy efficiency to mitigate CO2 emissions? Evidence from OECD countries (2018, methodology): “The order of integration of the variables is not very crucial for STSM and allows the trend component 𝜇𝑡 in the model to vary stochastically over time (Amarawickrama and Hunt, 2008).”