AIPREF 2024
2nd International Workshop on
AI-Powered Renewable Energy Forecasting: Techniques and Challenges
Workshop type: ONLINE
December 15-18, Washington DC (USA)
DESCRIPTION
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 production.
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
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
PAPER SUBMISSION
The workshop invites full-length paper submissions that report ongoing or finished research (up to 10 pages), or short papers of early stage work (up to 6 pages). Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines using Letter page format (8.5 x 11).
Templates are available at IEEE conference template
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.
Authors can submit their papers at this link.
The workshop organizers are negotiating a special journal issue. Further details will be provided in the future.
IMPORTANT DATES
Oct 31, 2024 (Extended): Due date for full workshop papers submission
Nov 6, 2024 (Extended): Notification of paper acceptance to authors
Nov 20, 2024 (Extended): Camera-ready of accepted papers
Dec 15-18, 2024: Workshops
PROGRAM CHAIRS
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
PROGRAM COMMITTEE MEMBERS
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, Italy
Camilla Lops, InGeo, University “G. d’Annunzio” of Chieti-Pescara
Eugenio Vocaturo, CNR Nanotec, Italy
Tommaso Ruga, DIMES University of Calabria, Italy
INFO
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Combined workshops: AIPREF+AIDA+CH&BD online meeting link
Meeting ID: 392 903 593 631
Passcode: ov6zC3Bk
Please find the schedule in the bottom of the page
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SCHEDULE
December 16th
8:40: Workshop Introduction
Session 1: AIPREF(AIPowered Renewable Energy Forecasting: Techniques and Challenges), Chair: Luciano Caroprese
08:45-9:00
S30201, Effective Net Load Forecasting in Solar-Integrated Homes with Battery Systems, Stefano Cabiddu, Manuela Sanguinetti, Alessandro De Falco, Giulia Manca, and Maurizio Atzori,
09:00-9:15
S30202, Comparing Artificial Intelligence Techniques for Predicting Energy Consumption and Renewable Energy Production, Behzad Pirouz and Francesca Guerriero,
9:15 – 9:30
S30203, On the use of Machine Learning to Discover Novel Donor-Acceptor Pairs For Organic Photovoltaic Devices, Khoukha KHOUSSA, Patrick LEVEQUE, and Larbi Boubchir,
9:30 – 9:45
S30204, DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO FORECAST PV PLANT PRODUCTION, Nicola Sorrentino, Daniele Menniti, Giovanni Brusco, and Giovanni Schinelli,
9:45 – 10:00
S30205, PRECEDE: Climate and Energy Forecasts to Support Energy Communities with Deep Learning models, Francesco Dattola, Pasquale Iaquinta, Miriam Iusi, Deborah Federico, Raffaele Greco, Marco Talerico, Valentina Coscarella, Luca Legato, Ivana Pellegrino, Sonia Bergamaschi, Mirko Orsini, Riccardo Martoglia, Andrea Livaldi, Abeer Jelali, Simone Sbregia, Tommaso Ruga, Ester Zumpano, Luciano Caroprese, Camilla Lops, Sergio Montelpare, Mariano Pierantozzi, and Maira Aracne
10:00-10:30, Coffee Break
Session 2: AIDA (AI-Driven Agriculture: opportunities and challenges), Chair: Eugenio Vocaturo
10:30 – 10:45
S32201, A study on phenotype prediction using an artificial intelligence-based data augmentation approach, Jiho Choi, Sung-Woo Byun, Najeong Chae, Ji Hoon Lim, Taehoon Lim, Hye In Lee, and Hwa Seon Shin,
10:45 – 11:00
S32202, Forest fire prevention: Application of mathematical models for the realization of an IoT based monitoring system., Carmelo Scuro, Giuseppe Alì, Pierpaolo Antonio Fusaro, and Salvatore Nisticò,
11:00 – 11:15
S32203, Boosting Agricultural Diagnostics: Cassava Disease Detection with Transfer Learning and Explainable AI, Danilo Maurmo, Marco Gagliardi, Tommaso Ruga, Ester Zumpano, and Eugenio Vocaturo,
11:15 – 11:30
S32205, Towards Agent-based Disease Spread Modeling Combining Knowledge-driven Simulation and Machine Learning, Maurice Günder, Facundo Ramón Ispizua Yamati, Anne-Katrin Mahlein, and Christian Bauckhage,
11:15 – 11:30
S32206, An AI-Driven Architecture for Precision Agriculture: IoT, Machine Learning, and Digital Twin Integration for Sustainable Crop Protection, Gianni Costa, Agostino Forestiero, Riccardo Ortale, Antonio Francesco Gentile, Davide Macri, Bruno Bernardi, and Emanuele Cerruto,
Session 3: CH&BD (AI-Driven Agriculture: opportunities and challenges), Chair: Tommaso Ruga
11:30 – 11:45
S33201, AI Image-based systems for enhancing the cultural tourism experience, Fiorella Folino, Tommaso Ruga, Ester Zumpano, Danilo Maurmo, Maria Francesca Foresta, and Eugenio Vocaturo,
11:45 – 12:00
BigD603, Foundation Models for Big Data, Kranthi Godavarthi, JAYANNA HALLUR, and SUJAN DAS,
Closing Remarks