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Electricity demand analysis and forecasting: The tradition is questioned

Author

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  • N. Vijayamohanan Pillai

    (Centre for Development Studies)

Abstract

The present paper seeks to cast scepticism on the validity and value of the results of all earlier studies in India on energy demand analysis and forecasting based on time series regression, on three grounds. (i) As these studies did not care for model adequacy diagnostic checking, indispensably required to verify the empirical validity of the residual whiteness assumptions underlying the very model, their results might be misleading. This criticism in fact applies to all regression analysis in general. (ii) As the time series regression approach of these studies did not account for possible non-stationarity (i.e., unit root integratedness) in the series, their significant results might be just the misleading result of spurious regression. They also failed to benefit from an analytical framework for a meaningful long-run equilibrium and short-run `causality' in a cointegrating space of error correction. (iii) These studies, by adopting a methodology suitable to a developed power system in advanced economies, sought to correlate the less correlatables in the context of an underdeveloped power system in a less developed economy. All explanations of association of electricity consumption in a hopeless situation of chronic shortage and unreliability with its generally accepted `causatives' (as in the developed systems) of population, per capita income, average revenue, etc., all in their aggregate time series, might not hold much water here. Our empirical results prove our secepticism at least in the context of Kerala power system. We find that the cost of dispensing with model adequacy diagnosis before accepting and interpreting the seemingly significant results is very high. We find that all the variables generally recognised for electricity demand analysis are non-stationary, I(1). We find that all the possible combinations of these I(1) variables fail to be explained in a cointegrating space and even their stationary growth rates remain unrelated in the Granger-`causality' sense.

Suggested Citation

  • N. Vijayamohanan Pillai, 2001. "Electricity demand analysis and forecasting: The tradition is questioned,"Centre for Development Studies, Trivendrum Working Papers 312, Centre for Development Studies, Trivendrum, India.
    • Handle:RePEc:ind:cdswpp:312
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    Cited by:

    1. K M, Siby, 2020. "An economic analysis of electricity consumption in Kerala with special reference to Kalamassery Municipality,"MPRA Paper 100532, University Library of Munich, Germany.
    2. Pillai N., Vijayamohanan, 2008. "Forecasting Demand for Electricity: Some Methodological Issues and an Analysis,"MPRA Paper 8899, University Library of Munich, Germany.
    3. N. Vijayamohanan Pillai, 2003. "A contribution to peak load pricing theory and application,"Centre for Development Studies, Trivendrum Working Papers 346, Centre for Development Studies, Trivendrum, India.
    4. Lin, Boqiang & Zhang, Li & Wu, Ya, 2012."Evaluation of electricity saving potential in China's chemical industry based on cointegration,"Energy Policy, Elsevier, vol. 44(C), pages 320-330.
    5. Pillai N., Vijayamohanan, 2008. "Strengthening Infrastructure: Power Sector Reforms- Some Viable Proposals For Kerala,"MPRA Paper 8869, University Library of Munich, Germany.
    6. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010."Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka,"Energy Economics,Elsevier, vol. 32(Supplemen), pages 34-40, September.
    7. Sreekanth, K.J., 2016."Review on integrated strategies for energy policy planning and evaluation of GHG mitigation alternatives,"Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 837-850.
    8. Siddiqui, Md Zakaria & de Maere d'Aertrycke, Gauthier & Smeers, Yves, 2012."Demand response in Indian electricity market,"Energy Policy, Elsevier, vol. 50(C), pages 207-216.
    9. Wasantha Athukorala & Clevo Wilson, 2010. "Demand for electricity: evidence of cointegration and causality from Sri Lanka,"School of Economics and Finance Discussion Papers and Working Papers Series 258, School of Economics and Finance, Queensland University of Technology.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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