Soft Computing techniques applied to wind prediction for generation

Summary of the technology

The Group of Modern Heuristics of Optimization and Design of Communication Networks of the University of Alcalá offers the use of modern soft computing techniques, widely contrasted, to obtain automated systems that allow wind prediction in wind farms and maximize the benefits associated with the production of this type of energy. The algorithms have already been developed and tested.

The group is looking for companies from the renewable energy sector to reach commercial agreements with technical assistance, cooperation agreements or joint venture agreements.

Universidad de Alcalá-OTRI

Details of the Technology Offer

Soft Computing is one of the most modern areas of artificial intelligence research. Among others, it focuses on the design of intelligent systems that can obtain and represent information given by series of information data, which can be uncertain, imprecise and / or incomplete.

The use of soft computing techniques for this work allows obtaining much more robust solutions while allowing to reduce the temporal and economic costs of its application to countless science applications.

The research group has focused its attention for many years, on three soft computing techniques of recent creation and study, such as: neural networks, vector machines, support and evolutionary techniques. The first two are two of the most important soft computing techniques of recent years.

The first one is based on the simulation of the behavior of the human brain for the learning of events and the reproduction of them. The second is based on complex statistical models for the search of models that respond more efficiently to future external variables.

Both techniques allow the development of regression models based on data from a sample of the actual process to be modeled, allowing to reproduce or predict approximately the real effects of the same process. Evolutionary techniques allow solving complex optimization problems in a fast efficient manner. Among others, they allow to make modifications on the previous regression models in order to improve their performance that could not be done by classical optimization techniques.

One of the most direct applications where the group has used this type of techniques is the wind prediction in wind farms. For this, given a series of variables of the park's environment, regression models can be constructed for the prediction of the wind module in each of the wind turbines of a specific wind farm.

These variables can be of a constant nature over time, such as the orography of the wind farm; past variables, such as the wind information in the different wind turbines in annual periods prior to the study period; or predictions of meteorological variables (wind, wind direction, temperature, etc.) using global prediction models.

Independently the prediction of the wind levels in each of the wind turbines, these techniques allow the evaluation of the power generated in the wind farm (due to the almost direct relationship between both variables) not only in normal operating conditions where all wind turbines remain in full performance, but also the prediction of wind when one or some of the wind turbines have to be stopped for reasons of safety or damage, without this being a reason for significant alterations in the prediction.

On the other hand, an extension in the use of soft computing techniques has allowed the development of prediction systems composed of multiple regression models based on multiple meteorological models. These models are complemented by the use of network grouping techniques, achieving a very significant improvement in the performance of these systems with respect to the typical way of using the models.


Our techniques are novel since multiple improvements have been made on the habitual use of them, this being demonstrated by the multiple scientific publications obtained on them. Among these improvements we can highlight the use of multiple meteorological models and multiple regression models simultaneously, as well as new training methods for these systems.


Any improvement in the use of renewable energy technologies is beneficial at an environmental level by reducing the use of other polluting technologies. But besides being good environmentally it is necessary also to be profitable for the companies that develop it. One of the main drawbacks of these companies are the economic penalties imposed by governments for the error in wind prediction. Our predictive tools aim to reduce their associated costs.


Intellectual property status

Other forms of protection

The group owns the industrial secret of technology


Current development status

Laboratory prototypes

The algorithms have already been developed and tested.



Desired business relationship

Technology selling

Joint ventures

Technology development

New technology applications

Adaptation of technology to other markets

The group is looking for companies from the renewable energy sector to reach commercial agreements with technical assistance, cooperation agreements or joint venture agreements.

Related Keywords

  • Electronics, IT and Telecomms
  • Artificial Intelligence (AI)
  • Energy Technology
  • Energy production, transmission and conversion
  • Renewable Sources of Energy
  • Wind Technology
  • Energy efficiency
  • Protecting Man and Environment
  • Environment
  • Artificial intelligence related software
  • Energy Market
  • Alternative Energy
  • Wind Market
  • Other alternative energy
  • Other Energy
  • wind farms
  • wind
  • wind prediction
  • learning techniques
  • soft computing techniques
  • renevable energy
  • wind technology

About Universidad de Alcalá-OTRI

The Technology Transfer Office at Alcalá University serves as a liaison between the University and its socioeconomic environment in terms of research and innovation. It encorages collaboration between research groups from universities and companies/institutions, with the objective to promote and commercialize research results and scientific capabilities.

Some of the services offered by this office are specified in the following list:

- Promotion of R & D and improvement of the relationships with companies.
- Promote the participation in R & D projects applicants to public calls (regional, national and European).
- Advising, processing and monitoring of patents and other forms of industrial protection.
- Support in the negotiation of contracts and agreements for R&D&i

Universidad de Alcalá-OTRI

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