Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to predict electricity prices in the future. Over the last 30 years electricity price forecasts have become a fundamental input to energy companies’ decision-making mechanisms at the corporate level.[1]
Since the early 1990s, the process of deregulation and the introduction of competitive electricity markets have been reshaping the landscape of the traditionally monopolistic and government-controlled power sectors. Throughout Europe, North America, Australia and Asia, electricity is now traded under market rules using spot and derivative contracts.[2] However, electricity is a very special commodity: it is economically non-storable and power system stability requires a constant balance between production and consumption. At the same time, electricity demand depends on weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs. off-peak hours, weekdays vs. weekends, holidays, etc.). These unique characteristics lead to price dynamics not observed in any other market, exhibiting daily, weekly and often annual seasonality and abrupt, short-lived and generally unanticipated price spikes.[3]
Extreme price volatility, which can be up to two orders of magnitude higher than that of any other commodity or financial asset, has forced market participants to hedge not only volume but also price risk. Price forecasts from a few hours to a few months ahead have become of particular interest to power portfolio managers. A power market company able to forecast the volatile wholesale prices with a reasonable level of accuracy can adjust its bidding strategy and its own production or consumption schedule in order to reduce the risk or maximize the profits in day-ahead trading.[4] A ballpark estimate of savings from a 1% reduction in the mean absolute percentage error (MAPE) of short-term price forecasts is $300,000 per year for a utility with 1GW peak load. With the additional price forecasts, the savings double.[5]
^Maciejowska, Katarzyna; Uniejewski, Bartosz; Weron, Rafal (2023-07-19), "Forecasting Electricity Prices", Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, arXiv:2204.11735, doi:10.1093/acrefore/9780190625979.013.667, ISBN 978-0-19-062597-9, retrieved 2024-04-12
^Mayer, Klaus; Trück, Stefan (March 2018). "Electricity markets around the world". Journal of Commodity Markets. 9: 77–100. doi:10.1016/j.jcomm.2018.02.001.
^Weron, Rafał (2014). "Electricity price forecasting: A review of the state-of-the-art with a look into the future". International Journal of Forecasting. 30 (4). [Open Access]: 1030–1081. doi:10.1016/j.ijforecast.2014.08.008.
^Weron, Rafał (2006). Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. Wiley. ISBN 978-0-470-05753-7.
^Hong, Tao (2015). "Crystal Ball Lessons in Predictive Analytics". EnergyBiz Magazine. Spring: 35–37. Archived from the original on 2015-09-10. Retrieved 2015-11-29.
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