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 Austria, Belgium, The 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:
AleaSoft offers price forecasting services at the long term for European markets. Price forecasts have an hourly granularity and 30 years of horizon. The main variables that are taken into account to generate the price forecasts are:
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, batteries, self-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.
The AleaSoft’s short‑term energy markets prices forecasts have a 10‑day horizon with an hourly interval.
The short‑term forecasts are essential for any actor that participates in the daily market: producers, direct consumers, retailers, traders, etc.
The forecasts are offered in product format and service format.
The product format consists of the installation of an application that works automatically, both for updating the data and for obtaining the forecasts.
The service format consists of the daily sending of the updated forecasts of the prices and of the main explanatory variables.
The main explanatory variables used in the short‑term energy markets prices forecasts are the demand, which in turn uses temperature and labour as explanatory variables, and the production of the different technologies, including wind, solar, hydroelectric and nuclear energy. In addition, the price forecasts of the interconnected countries are taken into account and the forecast is optimised with the available exchange capacity in the international interconnections.
The AleaSoft‘s electricity market prices simulations service includes 1000 price simulations with a horizon of up to three years and monthly granularity. These simulations are an essential input for advanced analysis for the risk management and allow obtaining probability distributions, Value-at-Risk estimates and confidence bands. All this with a completely scientific based probabilistic metric.
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:
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