Energy price forecasting

AleaSoft developed an energy forecasting methodology that is unique, guaranteeing the highest degree of efficiency and accuracy. AleaSoft provides short, medium and long term energy price forecasts in a variety of markets of the energy industry. It has supplied energy price forecasting for the major European electricity markets, providing services for traders, retailers, large clients and electricity companies, such as MIBEL (Spain and Portugal), EPEX SPOT (France, Germany and AustriaBelgiumThe Netherlands and Switzerland), IPEX (Italy), Nord Pool Spot (Norway, Sweden, Finland, Denmark, Estonia, Lithuania, and Latvia), SEM (Ireland), POLPX (Poland), ROPEX (Romania) and N2EX (United Kingdom). AleaSoft complements energy price forecasting with forecasts related to other price-related variables that are also of interest to market players, such as forecasts of commodity prices (oil, gas, coal, CO2 emissions), production by technology (wind, hydro and solar) and meteorological variables, also influencing the energy industry. AleaSoft has been a strong player in the European market and has ambitions to cater energy price forecasting globally. AleaSoft´s solution for electricity price forecasting can be delivered as a product or as a service.

AleaSoft offers the following energy price forecasting products and services:

AleaPriceShort

AleaSoft offers European market price forecasting at the short term. Price forecasts have a 10‑day horizon and hourly granularity.

Short term price forecasting is essential for any day-ahead market player: generators, direct consumers, retailers, energy traders, etc.

The main variables that are used in price forecasting are:

  • Demand, that uses explanatory variables such as temperatures, calendar data and socio-economic variables.
  • Wind energy production.
  • Solar energy production.
  • Hydroelectric production.
  • Nuclear energy production.
  • International interconnections.

Price forecasting takes into account forecasted prices from interconnected countries, and optimize the price forecasts using the available capacity of the international interconnections.

AleaPriceMid

 

AleaSoft offers mid-term price forecasting for European markets. The price forecasts have hourly granularity, a 3-year horizon and include probability distributions (stochastic forecasts) for each period (month, quarter and year) within the forecast horizon.

Stochastic price forecasting allows analyzing the impact of the variability of the explanatory variables in the mid-term price forecasts, and it is a basic tool for risk management and the determination of Values-at-Risk.

Stochastic price forecasting is generated using the data of the distributions of the explanatory variables and their associated probabilities. The main variables stochastically generated are the following:

  • Temperature.
  • Demand (obtained from stochastic temperature forecasts).
  • Wind energy production.
  • Solar energy production.
  • Hydroelectric production.
  • Coal price.
  • Gas price.
  • CO2 emission rights price.

For each of them, its intrinsic variability is estimated based on its historical values.

A sufficiently high number of random forecasts is calculated for each of the explanatory variables (coherent between them). With these simulations, the market price simulations are calculated, and from them the percentiles of the price distribution are obtained.

Stochastic forecasting will be generated using all registered data available at that time.

The delivery will include the probability distributions for each month, quarter and calendar product that is currently being traded in the futures markets and within the forecast horizon. For each period, the distribution will include a reference to the last settlement prices in the futures markets.

AleaPriceLong

 

AleaSoft offers price forecasting services at the long term for European markets. Price forecasts have an hourly granularity and 20 years of horizon. The main variables that are taken into account to generate the price forecasts are:

  • Demand, that uses explanatory variables such as temperatures, calendar data and socio-economic variables.
  • Wind energy production.
  • Solar energy production.
  • Hydroelectric
  • Nuclear energy production.
  • International interconnections.
  • CO2 emission rights
  • Fossil fuel prices (Brent, coal and gas).
  • Foreign exchange rates.

In addition, plans to increase the interconnection capacity between the main European electricity markets are taken into account and thus the convergence of market prices is taken into account.

Forecasts are generated using all registered data available at that time.

The forecasts take into account the scenarios for the dissemination of new and existing technologies (electric vehicles, batteriesself-consumption, heat pumps, etc.) and their impact on the demand volume and the hourly profile of the price curve.

The forecasts include the maximum and minimum confidence bands with annual granularity. The calculation of the bands is made using a sufficiently large number of price simulations generated from random simulations of the explanatory variables, taking into account the probability associated to each simulation of the variables. The variability and the probability of occurrence of each simulation of the variables is be determined by their past behavior and by the realistic future scenarios of the variable.

In a PPA (Power Purchase Agreement), having reliable price forecasts at the long-term is essential for all parties involved: investors, manufacturers, installers, managers, producers and consumers.