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Estimating temperature effects on the Italian electricity market

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  • Bigerna, Simona

Abstract

This paper provides empirical evidence of the effects that weather conditions exert on the electricity market, offering a new contribution to the understanding of hourly regional price formation in the day ahead market in Italy. The empirical estimation uses a new data set of hourly data on both market variables and temperature variables.

Suggested Citation

  • Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    • Handle: RePEc:eee:enepol:v:118:y:2018:i:c:p:257-269
    DOI: 10.1016/j.enpol.2018.03.068
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    as
    1. Mohammadi, Hassan & Ram, Rati, 2017. "Convergence in energy consumption per capita across the US states, 1970–2013: An exploration through selected parametric and non-parametric methods," Energy Economics, Elsevier, vol. 62(C), pages 404-410.
    2. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    3. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    4. Simona Bigerna, Carlo Andrea Bollino and Paolo Polinori, 2016. "Market Power and Transmission Congestion in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    5. Smyth, Russell & Narayan, Paresh Kumar, 2015. "Applied econometrics and implications for energy economics research," Energy Economics, Elsevier, vol. 50(C), pages 351-358.
    6. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    7. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    8. Joshua Graff Zivin and Kevin Novan, 2016. "Upgrading Efficiency and Behavior: Electricity Savings from Residential Weatherization Programs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    9. Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.
    10. Miller, Reid & Golab, Lukasz & Rosenberg, Catherine, 2017. "Modelling weather effects for impact analysis of residential time-of-use electricity pricing," Energy Policy, Elsevier, vol. 105(C), pages 534-546.
    11. Figueiredo, Nuno Carvalho & Silva, Patrícia Pereira da & Bunn, Derek, 2016. "Weather and market specificities in the regional transmission of renewable energy price effects," Energy, Elsevier, vol. 114(C), pages 188-200.
    12. Simona Bigerna, Carlo Andrea Bollino and Paolo Polinori, 2016. "Renewable Energy and Market Power in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    13. Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    14. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    15. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    16. Janice Boucher Breuer & Robert McNown & Myles S. Wallace, 2001. "Misleading Inferences from Panel Unit‐Root Tests with an Illustration from Purchasing Power Parity," Review of International Economics, Wiley Blackwell, vol. 9(3), pages 482-493, August.
    17. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    18. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    19. Maria Jesus Herrerias and Eric Girardin, 2013. "Seasonal Patterns of Energy in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    20. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    21. Papakostas, K. & Kyriakis, N., 2005. "Heating and cooling degree-hours for Athens and Thessaloniki, Greece," Renewable Energy, Elsevier, vol. 30(12), pages 1873-1880.
    22. Kevin F. Forbes and Ernest M. Zampelli, 2014. "Do Day-Ahead Electricity Prices Reflect Economic Fundamentals? Evidence from the California ISO," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    23. Véliz, Karina D. & Kaufmann, Robert K. & Cleveland, Cutler J. & Stoner, Anne M.K., 2017. "The effect of climate change on electricity expenditures in Massachusetts," Energy Policy, Elsevier, vol. 106(C), pages 1-11.
    24. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
    25. Breuer, Janice Boucher & McNown, Robert & Wallace, Myles S, 2001. "Misleading Inferences from Panel Unit-Root Tests with an Illustration from Purchasing Power Parity," Review of International Economics, Wiley Blackwell, vol. 9(3), pages 482-493, August.
    26. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
    27. Borovkova, Svetlana & Schmeck, Maren Diane, 2017. "Electricity price modeling with stochastic time change," Energy Economics, Elsevier, vol. 63(C), pages 51-65.
    28. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    29. Hsu, Yi-Chung & Lee, Chien-Chiang & Lee, Chi-Chuan, 2008. "Revisited: Are shocks to energy consumption permanent or temporary? New evidence from a panel SURADF approach," Energy Economics, Elsevier, vol. 30(5), pages 2314-2330, September.
    30. Maria Mansanet-Bataller & Angel Pardo & Enric Valor, 2007. "CO2 Prices, Energy and Weather," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 73-92.
    31. Peirson, John & Henley, Andrew, 1994. "Electricity load and temperature : Issues in dynamic specification," Energy Economics, Elsevier, vol. 16(4), pages 235-243, October.
    32. Jovanović, Saša & Savić, Slobodan & Bojić, Milorad & Djordjević, Zorica & Nikolić, Danijela, 2015. "The impact of the mean daily air temperature change on electricity consumption," Energy, Elsevier, vol. 88(C), pages 604-609.
    33. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    34. Huurman, Christian & Ravazzolo, Francesco & Zhou, Chen, 2012. "The power of weather," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3793-3807.
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    Cited by:

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    3. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).

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    More about this item

    Keywords

    Hourly electricity market; Temperature effects; Hourly temperature data; Vector autoregression; Non-parametric regression;
    All these keywords.

    JEL classification:

    • 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
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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