An adaptive longterm electricity price forecasting modelling using Monte Carlo simulation

Andreas Poullikkas


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 to
build a precise forecasting model. If the electricity market price can be predicted properly, the generation companies and the
load service entities as the main market participating entities can reduce their risks and further maximize their outcomes. In
this work, adaptive longterm electricity price forecasting modelling using Monte Carlo simulation is proposed. The applicability
of the prediction performance of the method is demonstrated for the case of electricity and oil price prediction, for vaious
forecasting periods. Oil price prediction is an external factor for electricity price forecasting and is becoming very important in
power systems running on oil derivatives. The proposed method could be useful for long term studies, evaluating the risk for
financing since good electricity price forecast feeds into developing cost effective risk management plans for the participating
companies in the electricity market and thus will help attract appropriate financing.


electricity markets; electricity price forecasting; oil price forecasting

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