Electricity price forecasting
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.
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.
[10] Curtailment could potentially significantly impact solar power's economic and environmental benefits at greater PV penetration levels.
[12] The effect of the recent introduction of smart grids and integrating distributed renewable generation has been increased uncertainty of future supply, demand and prices.
California's duck curve shows the difference between electricity demand and the amount of solar energy available throughout the day.
In 2015, SAIDI and SAIFI more than doubled from the previous year in Zambia due to low water reserves in their hydroelectric dams caused by insufficient rainfall.
Most countries characterized as having low energy access have electric power utilities that do not recover any of their capital and operating costs, due to high subsidy levels.
Fundamental (structural) methods try to capture the basic physical and economic relationships which are present in the production and trading of electricity.
Reduced-form (quantitative, stochastic) models characterize the statistical properties of electricity prices over time, with the ultimate objective of derivatives valuation and risk management.
Depending on the type of market under consideration, reduced-form models can be classified as: Statistical (such as econometric) methods forecast the current price by using a mathematical combination of the previous prices and/or previous or current values of exogenous factors, typically consumption and production figures, or weather variables.
Statistical models are attractive because some physical interpretation may be attached to their components, thus allowing engineers and system operators to understand their behavior.
Statistical models constitute a very rich class which includes: Computational intelligence (artificial intelligence-based, machine learning, non-parametric, non-linear statistical) techniques combine elements of learning, evolution and fuzziness to create approaches that are capable of adapting to complex dynamic systems, and may be regarded as "intelligent" in this sense.
The ability to adapt to nonlinear, spiky behavior does not necessarily lead to better point or probabilistic predictions, and a lot of effort is required to find the right hyper-parameters.
[55] Many of the modeling and price forecasting approaches considered in the literature are hybrid solutions, combining techniques from two or more of the groups listed above.
In short-term forecasting, the annual or long-term seasonality is usually ignored, but the daily and weekly patterns (including a separate treatment of holidays) are of prime importance.
Its misspecification can introduce bias, which may lead to a bad estimate of the mean reversion level or of the price spike intensity and severity, and consequently, to underestimating the risk.
Finally, in the long term, when the time horizon is measured in years, the daily, weekly and even annual seasonality may be ignored, and long-term trends dominate.
[3][88][89] Apart from historical electricity prices, the current spot price is dependent on a large set of fundamental drivers, including system loads, weather variables, fuel costs, the reserve margin (i.e., available generation minus/over predicted demand) and information about scheduled maintenance and forced outages.
[90] Very rarely has an automated selection or shrinkage procedure been carried out in EPF, especially for a large set of initial explanatory variables.
[91] However, the machine learning literature provides viable tools that can be broadly classified into two categories:[92] Some of these techniques have been utilized in the context of EPF: but their use is not common.
When predicting spike occurrences or spot price volatility, one of the most influential fundamental variables is the reserve margin, also called surplus generation.
Given that more and more system operators (see e.g. http://www.elexon.co.uk) are disclosing such information nowadays, reserve margin data should be playing a significant role in EPF in the near future.
[103][104] Combining probabilistic (i.e., interval and density) forecasts is much less popular, even in econometrics in general, mainly because of the increased complexity of the problem.
[105][106] With the increase of computational power, the real-time calibration of these complex models will become feasible and we may expect to see more EPF applications of the multivariate framework in the coming years.
This calls for a comprehensive, thorough study involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model's outperformance of another.
A selection of the better-performing measures (weighted-MAE, seasonal MASE or RMSSE) should be used either exclusively or in conjunction with the more popular ones (MAPE, RMSE).
