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PRR Project

Auxiliary Researcher in Short Term Forecasting for Power and Energy Systems based on Machine Learning

Project sheet

Name

Auxiliary Researcher in Short Term Forecasting for Power and Energy Systems based on Machine Learning

Total project amount

246,79 thousand €

Amount paid

0 €

Non-refundable funding

246,79 thousand €

Loan funding

0 €

Start date

01.02.2025

Expected end date

31.03.2026

Dimension

Resilience

Component

Qualifications and Skills

Investment

Science Plus Training

Operation code

02/C06-i06/2024.P2023.15797.TENURE.005

Summary

The researcher will work on short-term forecasting in Power and Energy systems (PES), including load forecasting, forecasting of distributed energy generation installations (with emphasis on renewable energy sources as wind and solar), forecasting of consumers’ actual flexibility delivery, and market price forecasting. The accuracy and effective automated performance of the forecasting methods is of most importance to ensure the sustainability of energy generation and use and reasonable energy costs enabling economic development and fair solutions to energy-related societal challenges. The advancement of the state of the art in this field is crucial to enable the efficient energy resource management, promoting energy efficiency, the increased use of renewable energy sources, and reasonable energy costs. The research should consider the existing models that are adequate to address the problem, with focus on Machine Learning (ML) methods. The overall accuracy of ML learning methods is remarkable, but significant errors for periods with particular conditions prevent their regular use in practice. Despite the recent advancement on ML methods for forecasting, significant and faster advancements are required so that energy efficiency and renewable energy goals are met ensuring a fast and just energy transition.The Auxiliary Researcher to be hired will perform the following tasks:- Study of the state of the art in the research field and experimentation of existing models- Design and development of ML contextual models as well as intelligent and automated choice through reinforcement learning of the most adequate forecasting method for each context- Design and development of new explainability models to overcome the issue of the black-box nature of ML methods- Design and development of models to automatically process the explanations and use the obtained knowledge in coordination with experts’ knowledge to generate a hybrid forecasting model based on data and knowledge- Test and validation of the proposed models using GECAD’s labs and demonstration infrastructures using use cases and large sets of realistic case studies considering buildings, energy communities, local markets, demand response, energy storage, and electric vehicles.- Preparation and submission of proposals for R&D project funding in competitive calls, with emphasis on internacional projects- Projects’ teams, work packages, and tasks coordination, including  international projects and tasks with partners from different countries and of different nature- Contribution to GECAD R&D Projects, including participation in meetings and production of Deliverables and other Reports- Cooperation with other researchers of GECAD, from senior researchers to young researchers, in the aims of the research activities in the scope of the job- Coordination of Laboratory activities, including prototyping, and preparation of case studies, simulation studies, and the respective analysis and presentation of results- Supervision of MSc and PhD theses- Ensuring that the R&D activities impact is significant, at national and international level- Coordination of design and computational implementation of methods, algorithms, and computational applications and systems- Coordination of the integration and reformulation of existing and developed computational applications and systems- Coordination of the availability of all the source codes regarding the developed software, within the scope of the work plan, and the proper manual documentation- Communication and dissemination activities, and knowledge transfer actions- Writing of Scientific Papers to be submitted and published in reference scientific journals- Proactive activities regarding Ethics, Data Protection, Security, and Inclusive and Open ScienceThe candidate scientific profile should include:- PhD degree in the position scientific domain and demonstrate a strong research record in the field of Artificial Intelligence (AI) methods with applicability to PES- Relevant scientific work in at least two of the following areas: Electrical Engineering, Informatics Engineering, PES, Energy Forecasting, ML, Explainable AI- Extensive experience in preparing proposals and participating in scientific research and development projects with external funding, with emphasis on international project,- Participation and coordination of teams, work packages, and tasks in scientific projects with external funding, with emphasis on international projects- Have authored a minimum of 6 papers published in journals indexed in the Science Citation Index Expanded (SCI), in the last 5 years prior to the submission of the application, in the activities field to be developed.- Scientific dissemination and knowledge transfer actions, including participation in events with oral presentation and demonstration of scientific activities results, and students’ supervision- Very good oral and written communication skills in English.

Beneficiaries

Within the scope of the Recovery and Resilience Plan, two types of beneficiaries are responsible for carrying out the projects and using the funding provided. Due to their similar role, the reference to these two types of beneficiaries has been simplified and unified under the term "Beneficiary".
The two types are::
  • Direct Beneficiaries are those whose funding and projects to implement are part of the Recovery and Resilience Plan that has been negotiated and approved by the European Union;
  • Final Beneficiaries are those whose funding and projects to implement are approved following a selection process through Calls for Applications.

Call for applications

As part of the Call for Applications, submissions are requested to select the projects and final beneficiaries to whom funding will be awarded. Specific selection criteria are defined for each call, which must be reflected in the applications submitted and assessed.

The project is appraised on the basis of its compliance with the selection criteria laid down in the calls for applications, and a final score may be awarded, where applicable.

Final evaluation score

9,0
Important note

The components for calculating the assessment score can be found in the selection criteria document mentioned below.

Selection criteria

The funding selection criteria to which this project and its final beneficiary were subject and its score can be found in detail on the Recuperar Portugal platform.

Beneficiaries

Intermediate beneficiaries

Beneficiaries

Procurement

Beneficiaries representing public entities implement their project by signing one or more contracts with suppliers for goods or services through public procurement procedures.

To ensure and provide the utmost transparency in all these contracts, a list of the contracts that were signed under this project is available here, along with the information available on the Base.Gov platform. Please note that, according to the legislation in force at the time the contract was signed, some exceptions do not require the publication of the contracts signed on this platform, and, therefore, no information is available in such cases.

Geographic distribution

246,79 thousand €

Total amount of the project

Where was the money spent

By county

1 county financed .

  • Porto 246,79 thousand € ,
Source EMRP
10.02.2026
All themes
Transparency without leading