Sustainable Energy Management with AI, Blockchain, and IoT: Forecasting and Load Optimization in Smart Grids
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Beshley Pavlo, Przystupa Krzysztof, Beshley Mykola |
| Dyscypliny: | |
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| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Strony: | 792 - 812 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | lnternational Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering 2024 |
| Skrócona nazwa konferencji: | TCSET 2024 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 22 lutego 2024 do 26 lutego 2024 |
| Miasto konferencji: | Lviv-Slavske |
| Państwo konferencji: | UKRAINA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The transition to sustainable energy systems requires innovative digital approaches to address growing challenges such as the instability of renewable energy sources (RES), unpredictable demand, high transmission losses, and insufficient grid flexibility. In this chapter, we propose a conceptual model based on Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technologies for smart energy management with a focus on improving forecasting accuracy, optimizing load balancing, and enabling decentralized, transparent energy transactions. The key contribution of this research is a thorough analysis of electricity production patterns using advanced data science techniques. Using a dataset covering more than 6.5 years of hourly electricity consumption and generation in Romania, we applied exploratory data analysis, time series decomposition, and correlation analysis to characterize the temporal behavior, variability, and relationships between multiple generation sources. In addition, we developed and evaluated deep learning models based on recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures to forecast electricity supply and demand. The developed models achieved high prediction accuracy, demonstrating their effectiveness for real-time energy forecasting and intelligent load management in sustainable smart grid systems. The proposed concept aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (affordable and clean energy), SDG 9 (industry, innovation, and infrastructure), and SDG 13 (climate action). This concept is particularly relevant for countries with unstable energy conditions and transitional economies, such as Ukraine, where digital innovation and energy independence are strategic priorities. Moreover, it aligns with the broader objectives of European countries in striving to enhance grid flexibility, decarbonize energy systems, and accelerate the deployment of smart grids. By combining AI forecasting, real-time IoT data, and blockchain-based trust mechanisms, the study contributes to the development of intelligent, sustainable, and decentralized smart grid infrastructures. |
