{"id":285,"date":"2021-04-26T13:07:21","date_gmt":"2021-04-26T13:07:21","guid":{"rendered":"https:\/\/easyconferences.eu\/iisa2021\/?page_id=285"},"modified":"2025-06-02T06:53:36","modified_gmt":"2025-06-02T06:53:36","slug":"5th-international-workshop-on-big-data-analytics-in-the-energy","status":"publish","type":"page","link":"https:\/\/easyconferences.eu\/iisa2025\/5th-international-workshop-on-big-data-analytics-in-the-energy\/","title":{"rendered":"5th International Workshop on Big Data Analytics in the Energy"},"content":{"rendered":"

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Workshops<\/h1>\n

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5th International Workshop on AI & Data Analytics in the Energy Sector (featuring the #SmartEnergyCluster)<\/strong><\/h3>\n

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Background and Goals:<\/span><\/strong><\/p>\n

The energy sector is on the brink of an AI-driven revolution. Traditional analytics are evolving into self-learning, autonomous systems powered by cutting-edge technologies such as Generative AI, Large Language Models (LLMs), neuromorphic computing, federated learning, and quantum-enhanced data processing.<\/p>\n

As energy networks generate exponentially growing real-time data streams from IoT sensors, smart meters, decentralized grids, and consumer interactions, the challenge is no longer just about processing data\u2014it\u2019s about understanding, predicting, and dynamically optimizing energy systems with minimal human intervention.<\/p>\n

This workshop explores beyond-state-of-the-art methodologies that will define the next decade of energy analytics:<\/p>\n

\ud83d\udd39 Generative AI & Large Language Models (LLMs) for energy optimization: How LLMs can autonomously generate real-time grid management strategies, provide AI-powered decision support, and simulate energy markets in real-time for predictive energy trading.<\/p>\n

\ud83d\udd39 AI-driven autonomous energy systems: Can we develop self-regulating energy grids that predict, optimize, and balance loads without centralized human control?<\/p>\n

\ud83d\udd39 Physics-informed neuromorphic computing for energy forecasting: Leveraging brain-inspired AI architectures to enable real-time, ultra-low-power decision-making in energy networks.<\/p>\n

\ud83d\udd39 Swarm Intelligence for decentralized energy trading: Using multi-agent AI systems that mimic biological swarms to optimize P2P energy transactions, adaptive demand response, and self-healing grids.<\/p>\n

\ud83d\udd39 Federated learning and privacy-preserving AI: Training AI models across multiple organizations without sharing raw data, unlocking new frontiers in secure energy analytics.<\/p>\n

\ud83d\udd39 Quantum-enhanced machine learning for energy analytics: How quantum computing can break current limitations in ultra-fast grid simulation, market optimization, and high-dimensional energy modelling.<\/p>\n

By bringing together leading AI researchers, energy experts, and policymakers, this workshop aims to redefine the future of energy efficiency, forecasting, and autonomous operations. This workshop is closely linked to research activities from HE Crete Valley<\/u><\/strong>, EnerTEF<\/u><\/strong>, ENPOWER, ENERGATE<\/u> <\/strong>and METABUILD<\/u> <\/strong>(coordinated by DSS Lab, EPU-NTUA<\/strong><\/a><\/span>). The workshop also features HE DEDALUS, DATAWiSE, Hedge-IoT, BuildON, CLIMRES <\/strong>and BUILDSPACE.<\/strong><\/p>\n

Topics of Interest:<\/span><\/strong><\/p>\n

\ud83d\udca1 Generative AI & LLMs<\/strong> for real-time energy system management
\ud83d\udca1 AI-powered digital twins<\/strong> for self-learning buildings and grids
\ud83d\udca1 Neuromorphic AI architectures<\/strong> for ultra-low-power energy optimization
\ud83d\udca1 Swarm intelligence & agent-based models<\/strong> for decentralized energy networks
\ud83d\udca1 Federated learning & privacy-preserving AI<\/strong> in energy ecosystems
\ud83d\udca1 Quantum machine learning<\/strong> for next-gen energy forecasting
\ud83d\udca1 Blockchain & AI convergence<\/strong> for secure and autonomous energy trading
\ud83d\udca1 Self-healing energy networks<\/strong> powered by reinforcement learning<\/p>\n

Chairs:<\/span><\/strong><\/p>\n