An adaptive longterm electricity price forecasting modelling using Monte Carlo simulation

  • Andreas Poullikkas

Abstract

Accurate electricity price forecasting is of great importance for risk-analysis and decision-making in the electricity market.However, due to the characteristics of randomness and non-linearity associated with the electricity price series, it is difficult tobuild a precise forecasting model. If the electricity market price can be predicted properly, the generation companies and theload service entities as the main market participating entities can reduce their risks and further maximize their outcomes. Inthis work, adaptive longterm electricity price forecasting modelling using Monte Carlo simulation is proposed. The applicabilityof the prediction performance of the method is demonstrated for the case of electricity and oil price prediction, for vaiousforecasting periods. Oil price prediction is an external factor for electricity price forecasting and is becoming very important inpower systems running on oil derivatives. The proposed method could be useful for long term studies, evaluating the risk forfinancing since good electricity price forecast feeds into developing cost effective risk management plans for the participatingcompanies in the electricity market and thus will help attract appropriate financing.

Author Biography

Andreas Poullikkas
Prof.

References

[1] P. L. Joskow, R. Schmalensee, Markets for Power: An Analysis of Electrical
Utility Deregulation, MIT Press, 1983.
[2] M. Ventosa, Á. . Baíllo, A. Ramos, M. Rivier, Electricity market
modeling trends, Energy Policy 33 (7) (2005) 897–913.
doi:10.1016/j.enpol.2003.10.013.
[3] R. Weron, Modeling and forecasting electricity loads and prices: A
statistical approach, John Wiley & Sons, Chichester, England, 2006.
arXiv:arXiv:1011.1669v3, doi:10.1002/9781118673362.
[4] A. Poullikkas, G. Kourtis, I. Hadjipaschalis, An overview of load demand
and price forecasting methodologies, International Journal of
Energy and Environment 2 (1) (2011) 123–150.
[5] R. Pino, J. Parreno, A. Gomez, P. Priore, Forecasting next-day price
of electricity in the Spanish energy market using artificial neural networks,
Engineering Applications of Artificial Intelligence 21 (1) (2008)
53–62. doi:10.1016/j.engappai.2007.02.001.
[6] V. Vahidinasab, S. Jadid, A. Kazemi, Day-ahead price forecasting
in restructured power systems using artificial neural networks,
Electric Power Systems Research 78 (8) (2008) 1332–1342.
doi:10.1016/j.epsr.2007.12.001.
[7] A. I. Arciniegas, I. E. Arciniegas Rueda, Forecasting short-term
power prices in the Ontario Electricity Market (OEM) with a fuzzy
logic based inference system, Utilities Policy 16 (1) (2008) 39–48.
doi:10.1016/j.jup.2007.10.002.
[8] Z. Liu, J. Yan, Y. Shi, K. Zhu, G. Pu, Multi-agent based experimental
analysis on bidding mechanism in electricity auction markets, International
Journal of Electrical Power and Energy Systems 43 (1) (2012)
696–702. doi:10.1016/j.ijepes.2012.05.056.
[9] R. Weron, Electricity price forecasting: A review of the state-of-the-art
with a look into the future, International Journal of Forecasting 30 (4)
(2014) 1030–1081. doi:10.1016/j.ijforecast.2014.08.008.
[10] M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, Z. Yan,
A review on the forecasting of wind speed and generated
power (2009). arXiv:/dx.doi.org/10.1038/nclimate2833,
doi:10.1016/j.rser.2008.02.002.
[11] F. L. Chu, Forecasting tourism demand with ARMA-based
methods, Tourism Management 30 (5) (2009) 740–751.
doi:10.1016/j.tourman.2008.10.016.
[12] P. Ramos, N. Santos, R. Rebelo, Performance of state space and
ARIMA models for consumer retail sales forecasting, Robotics
and Computer-Integrated Manufacturing 34 (2015) 151–163.
doi:10.1016/j.rcim.2014.12.015.
[13] H. Nyberg, P. Saikkonen, Forecasting with a noncausal VAR model,
Computational Statistics and Data Analysis 76 (2014) 536–555.
doi:10.1016/j.csda.2013.10.014.
[14] S. J. Byun, H. Cho, Forecasting carbon futures volatility using GARCH
models with energy volatilities, Energy Economics 40 (2013) 207–221.
doi:10.1016/j.eneco.2013.06.017.
[15] H. Takeda, Y. Tamura, S. Sato, Using the ensemble Kalman filter for
electricity load forecasting and analysis, Energy 104 (2016) 184–198.
doi:10.1016/j.energy.2016.03.070.
[16] I. P. Panapakidis, A. S. Dagoumas, Day-ahead electricity
price forecasting via the application of artificial neural network
based models, Applied Energy 172 (2016) 132–151.
doi:10.1016/j.apenergy.2016.03.089.
[17] F. V. Gutierrez-Corea, M. A. Manso-Callejo, M. P. Moreno-Regidor,
M. T. Manrique-Sancho, Forecasting short-term solar irradiance
based on artificial neural networks and data from neighboring
meteorological stations, Solar Energy 134 (2016) 119–131.
doi:10.1016/j.solener.2016.04.020.
[18] S. Hassan, A. Khosravi, J. Jaafar, M. A. Khanesar, A systematic
design of interval type-2 fuzzy logic system using extreme learning
machine for electricity load demand forecasting, International
Journal of Electrical Power & Energy Systems 82 (2016) 1–10.
doi:10.1016/j.ijepes.2016.03.001.
URL http://www.sciencedirect.com/science/
article/pii/S0142061516303271
[19] J. Zhou, J. Shi, G. Li, Fine tuning support vector machines for shortterm
wind speed forecasting, Energy Conversion and Management
52 (4) (2011) 1990–1998. doi:10.1016/j.enconman.2010.11.007.
[20] T.-T. Chen, S.-J. Lee, A weighted LS-SVM based learning system
for time series forecasting, Information Sciences 299 (2015) 99–116.
doi:10.1016/j.ins.2014.12.031.
URL http://linkinghub.elsevier.com/retrieve/pii/
S0020025514011736
[21] J. Z. Wang, Y. Wang, P. Jiang, The study and application
of a novel hybrid forecasting model - A case study of wind
speed forecasting in China, Applied Energy 143 (2015) 472–488.
doi:10.1016/j.apenergy.2015.01.038.
[22] A. Meng, J. Ge, H. Yin, S. Chen, Wind speed forecasting based on
wavelet packet decomposition and artificial neural networks trained
by crisscross optimization algorithm, Energy Conversion and Management
114 (2016) 75–88. doi:10.1016/j.enconman.2016.02.013.
[23] D. Liu, D. Niu, H. Wang, L. Fan, Short-term wind speed forecasting
using wavelet transform and support vector machines optimized
by genetic algorithm, Renewable Energy 62 (2014) 592–597.
doi:10.1016/j.renene.2013.08.011.
[24] T. Xiong, Y. Bao, Z. Hu, Interval forecasting of electricity demand: A
novel bivariate EMD-based support vector regression modeling framework,
International Journal of Electrical Power and Energy Systems 63
(2014) 353–362. doi:10.1016/j.ijepes.2014.06.010.
[25] D. Wang, H. Luo, O. Grunder, Y. Lin, H. Guo, Multi-step
ahead electricity price forecasting using a hybrid model based on
two-layer decomposition technique and BP neural network optimized
by firefly algorithm, Applied Energy 190 (2017) 390–407. doi:10.1016/j.apenergy.2016.12.134.
[26] The Mathworks Inc., MATLAB - MathWorks (2016). doi:2016-11-26.
URL http://www.mathworks.com/products/matlab/
[27] U.S. Energy Information Administration, www.eia.gov (2017).
URL https://www.eia.gov/electricity/wholesale/
Published
2018-11-08
How to Cite
POULLIKKAS, Andreas. An adaptive longterm electricity price forecasting modelling using Monte Carlo simulation. Journal of Power Technologies, [S.l.], v. 98, n. 3, p. 267–273, nov. 2018. ISSN 2083-4195. Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1241>. Date accessed: 28 sep. 2021.
Section
Policy, Economy and Society

Keywords

electricity markets; electricity price forecasting; oil price forecasting

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.