At AleaSoft, we offer price forecasts for electricity markets to global level, covering all time horizons: short, medium and long term. Depending on the market, these forecasts have a granularity of 15 minutes, half an hour or an hour.
This wide range of applications makes our forecasts essential tools for practically all market players: renewable energy developers, utilities, traders, large consumers, marketers, IPPs, investment funds and banks.
Our forecasts are based on the Alea methodology, which combines Artificial Intelligence, analysis of time series and models statistics. The quality of our results is guaranteed by the trust that our clients have placed in us for 27 years.
In addition, we complement our energy price forecasts with other market variables, such as commodity prices (gas, CO2 emissions, oil, coal), demand, production by technology (wind, hydroelectric and solar), meteorological variables, exchange rate, economic indices and any factor that influences the price of energy.
AleaSoft Energy Forecasting, March 30, 2026. Interview by Ramón Roca, director of El Periódico de la…
Read more >>
AleaSoft Energy Forecasting, June 8, 2026. In the first week of June, weekly average prices fell…
Read more >>
AleaSoft Energy Forecasting, June 5, 2026. The European Commission’s approval of the capacity market in Spain…
Read more >>AleaSoft has developed a methodology for energy forecasting that is unique, guaranteeing the highest degree of efficiency and accuracy. This methodology has been in use for more than 27 years in some of the most important companies in the energy sector in Europe. The more than 400 models obtained with this methodology are in service in different European markets.
AleaSoft provides electricity and gas market price forecasts in the short, medium and long term in most European markets, providing services to traders, marketers, large consumers, utilities, renewable developers, banks and investment funds. The main European electricity markets are MIBEL (Spain and Portugal), EPEX SPOT (France, Germany and Austria, Belgium, Netherlands, Switzerland, Luxembourg and Kingdom United), IPEX (Italy), Nord Pool Spot (Norway, Sweden, Finland, Denmark, Estonia, Lithuania and Latvia), SEM (Ireland), POLPX (Poland), OPCOM (Romania), N2EX (United Kingdom), CROPEX (Croatia), HEnEx (Greece), OTE (Czech Republic), HUPX (Hungary), OKTE (Slovakia), SEEPEX (Serbia), EPEX SPOT SEE (Bosnia and Herzegovina), IBEX (Bulgaria), CSE (Cyprus), BSP Southpool (Slovenia), Landsnet (Iceland), MEMO (North Macedonia), MEX (Malta) and MEPX (Montenegro). AleaSoft also provides forecast services in some American markets such as SEN (Chile).
AleaSoft complements the forecasts of energy prices with the forecasts of other related variables that are also of interest to those interested in the market, such as forecasts of commodity prices (oil, gas, coal, emissions of CO2), demand, production by technology (wind, hydro and solar), variables meteorological conditions, exchange rates, economic indices, and in general all the variables that influence the price of energy.
For long-term price forecasts, the use of renewable production penetration scenarios, especially wind and photovoltaic, is of special interest.
§The price forecast reports have as outputs the expected price values and confidence bands with associated probabilities.
AleaSoft offers the following products and services for energy price forecasts:
AleaSoft makes price forecasts for European markets in the medium term. The price forecasts have an hourly granularity, a 3-year horizon, and include probability distributions (forecasts with stochasticity) for each period (month, quarter and year) within the forecast horizon.
Forecasts with price stochasticity allow us to analyse the impact of the stochasticity of the explanatory variables on the medium-term price forecasts, and are a basic tool for risk management and the calculation of Values-at-Risk.
Price forecasts are calculated using data from the distributions of the explanatory variables and their associated probabilities. The main variables26 that are obtained stochastically are the following:
For each of them, their intrinsic variability is estimated based on their values historical.
A sufficiently high number of random forecasts is calculated for each of the explanatory variables consistent with each other. With these simulations of the variables, the corresponding simulations of the market price are calculated, and from these the percentiles of the price distribution are calculated.
Stochasticity will be generated using all the recorded data available at that time.
The shipment will include the probability distributions for each monthly, quarterly and annual product being currently trading in the futures markets within the forecast horizon. For each period, the distribution will include a reference to the latest prices traded in the stock markets. futures.
Short-term forecasts are essential for any actor participating in the daily market: producers, direct consumers, marketers, traders, etc.
Forecasts are offered in product format and in format service.
The product format consists of the installation of an application that works automatically, both for updating the data and for obtaining forecasts.
The service format consists of the daily sending of updated price forecasts and the main explanatory variables.
The main explanatory variables used in the price forecasts of short-term energy markets are demand, which in turn uses temperature and work force as explanatory variables, and the production of the different technologies, among which There are wind, solar, hydroelectric and nuclear. Furthermore, the price forecasts of the interconnected countries are taken into account and the forecast is optimised with the exchange capacity available in international interconnections.
The AleaSoft medium-term electricity market price simulations service includes 1000 price simulations with a horizon up to three years and hourly or monthly granularity. These simulations are an essential input for advanced analyses for risk management and allow obtaining probability distributions, estimates of Value-at-Risk and confidence bands. All this with a probabilistic metric with a completely scientific basis.
To obtain the price simulations, 1000 simulations of the following explanatory variables are carried out, taking into account the most probable average scenarios, the historical variability, the expected variability and the temporal correlations between them:
