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Forecasting Hourly Prices in Indian Spot Electricity Market

Author

Listed:
  • Mukherjee, Paramita
  • Coondoo, Dipankor
  • Lahiri, Poulomi

Abstract

In this paper, an attempt has been made to forecast the hourly electricity spot prices in India as this is very important for the bidders in the energy exchange for participating in the day-ahead market. Forecasting high frequency data is a challenging task. In forecasting, different variants of ARMA, ARMA-GARCH models are applied in different contexts, but no unequivocal dominance of a particular model exists. In this paper, based on hourly data for several years for all the regions in India, several variants of ARMAX models are estimated, by combining static and dynamic forecasts. Along with ARMA, intra-day, inter-day and hourly variations in prices as well as seasonalities on weekdays, holidays and festive days are incorporated. ARMAX models in this context performed quite well for forecasting horizons of hourly prices of upto 5 days. Interestingly, the ARMAX models provide reasonably good forecasts for day-ahead-market and the simple structure can be quite easily implemented. Such forecasts are not only essential for the players in the spot market, but also provides insights for policymakers as it reveals several aspects of Indian electricity market including the different dimensions of seasonality in demand.

Suggested Citation

  • Mukherjee, Paramita & Coondoo, Dipankor & Lahiri, Poulomi, 2019. "Forecasting Hourly Prices in Indian Spot Electricity Market," MPRA Paper 103161, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103161
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    References listed on IDEAS

    as
    1. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    2. Sajal Ghosh & Kakali Kanjilal, 2014. "Modelling and forecasting of day-ahead electricity price in Indian energy exchange - evidence from MSARIMA-EGARCH model," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 8(3), pages 413-423.
    3. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    6. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    7. Girish Godekere Panchakshara Murthy & Vijayalakshmi Sedidi, 2014. "Forecasting Electricity Prices in Deregulated Wholesale Spot Electricity Market: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 4(1), pages 32-42.
    8. repec:bla:jfinan:v:59:y:2004:i:4:p:1877-1900 is not listed on IDEAS
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    More about this item

    Keywords

    Forecasting; electricity; hourly data; energy; spot price; ARMAX model; day-ahead market;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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