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Luciano Caroprese <[log in to unmask]>
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Thu, 7 Sep 2023 00:18:08 -0400
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First International Workshop on AI-Powered Renewable Energy Forecasting: Techniques and Challenges (AIPREF) @ IEEE BigData 2023
December 15-18, Sorrento (Italy)

Conference Website:
Workshop Website:

The European Green Deal, an extremely ambitious set of policies that should allow European citizens and businesses to profit from a sustainable green transition, aims to make Europe the first climate-neutral continent by 2050.
Renewable energy sources such as solar, and wind, are therefore becoming increasingly popular due to their clean and sustainable nature.
The use of renewable energy provides a number of potential advantages, such as a decrease in greenhouse gas emissions, the diversification of energy sources, and a decreased reliance on the markets for fossil fuels (especially oil and gas).
However, accurate estimation of energy production from these sources is crucial in ensuring a reliable and consistent supply. To achieve this goal, big data analysis supported by sophisticated models and forecasting techniques is required. These models have to accurately calculate the amount of energy that can be produced, which helps in planning and managing the power grid. This is where artificial intelligence (AI) comes into play. Machine learning, Deep learning models and other AI-based technologies can analyze large amounts of data, including historical weather patterns, sensor data, and satellite imagery, to make more accurate predictions about renewable energy production. By using AI to predict renewable energy output, grid operators can better manage the supply of energy, prevent outages, and ensure that energy is distributed efficiently. Additionally, AI can help to optimize the use of energy storage systems, allowing excess energy to be stored and used during times of low p

The AIPREF workshop is a gathering of experts in the fields of artificial intelligence and renewable energy. The purpose of the workshop is to share the latest research and developments in AI techniques for forecasting renewable energy production, such as solar and wind power. The workshop will be of interest to researchers, engineers, and industry professionals who are working on developing AI techniques for renewable energy forecasting. It will provide an opportunity for participants to learn from one another, share best practices, and collaborate on future research and development in this important field.

Topics of interest include but are not limited to:

- Machine learning and deep learning models for renewable energy forecasting
- Hybrid forecasting models using both physical and AI-based models
- Real-time renewable energy forecasting using AI
- Time series analysis for renewable energy forecasting
- Statistical models for renewable energy forecasting
- Big data preprocessing techniques for renewable energy forecasting
- Integration of AI-based renewable energy forecasting models into energy management systems
- Real-world case studies and applications of AI-based renewable energy forecasting models
- Uncertainty analysis and risk assessment in renewable energy forecasting using AI
- Data visualization and interpretation of renewable energy forecasts
- Data acquisition, pre-processing, and management for renewable energy forecasting
- Overview of machine learning and deep learning algorithms for renewable energy forecasting


Authors can submit their papers at this link:

All accepted papers will be included in the Workshop Proceedings published by the IEEE Computer Society Press and made available at the Conference. Proceedings will be included in the IEEE digital library indexed by Google Scholar and Scopus.

The workshop organizers are negotiating a special journal issue. Further details will be provided in the future.

The IEEE conference template can be found at this link: 

Oct 1, 2023: Due date for full workshop papers submission
Nov 1, 2023: Notification of paper acceptance to authors
Nov 20, 2023: Camera-ready of accepted papers
Dec 15-18, 2023: Workshops

Luciano Caroprese, InGeo, University “G. d’Annunzio” of Chieti-Pescara
Sergio Montelpare, InGeo, University “G. d’Annunzio” of Chieti-Pescara
Mariano Pierantozzi, InGeo, University “G. d’Annunzio” of Chieti-Pescara
Ester Zumpano, DIMES, University of Calabria

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