Short-term forecasting of electricity prices in the Colombian electricity market

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Short-term forecasting of electricity prices in the Colombian electricity market

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Published

The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi–Sugeno–Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.

Inspec keywords: pricing; power generation economics; power markets; autoregressive moving average processes; power generation scheduling; forecasting theory

Other keywords: optimal bidding strategy; Takagi-Sugeno-Kang models; short-term electricity price forecasting; Colombian electricity market; next-day-prices forecasting; scheduling generation; ARMAX models; electricity-generating industry

Subjects: Generating stations and plants; Power system management, operation and economics; Other topics in statistics

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