Adaptive energy management in smart buildings using deep neural networks.
Školitel
Ing. Michal Hodoň, PhD.
Študijný program
Aplikovaná informatika
Študijný odbor
Informatika
Detailnejší opis problému
The project is focused on smart buildings which uses photovoltaic (PV) panels as an alternative energy source. This approach involves an intelligent system capable of switching energy sources in real-time based on current energy demands and predictive forecasts. PV panels generate renewable energy, which can be dynamically allocated to various building operations through a smart hardware device that decides the most efficient energy source at any given time. The problem is in accurately predicting energy needs and efficiently managing the switch between renewable and non-renewable sources to maximize the use of solar power while maintaining the supply of energy to critical building functions. This management requires a deep neural network that can learn from complex datasets and make immediate, data-driven decisions, ensuring that energy savings are achieved without compromising the building functioning.
Predpokladaný vedecký prínos (algoritmy, metodológie, ...)
Uses real-time data and forecasts to dynamically optimize energy consumption. Minimizes unnecessary energy consumption by adjusting usage based on actual and anticipated needs. Enhances the ability to handle sudden changes in energy demand without overloading the system
Odporučány postup vedeckého bádania
- Literature Review - Data Collection - Testing the neural networks to predict and optimize energy usage. - Integration of validated models into real-world systems.
Druh výskumu
aplikovaný výskum a experimentálny vývoj
Výskumná úloha, ktorej súčasťou bude riešená téma
APVV-24-0429 - Experimentálne skúmanie a počítačové modelovanie prúdenia vzduchu pri požiari v cestnom tuneli - podaný projekt APVV-24-0653 - Inovatívne riešenie pre dočasné elektrické a vodovodné prípojky v stavebníctve - podaný projekt UNIZA MVP - Nízkoprikonový senzor pre aplikácie inteligentnej dopravy
Doterajšie výsledky riešenia na školiacom pracovisku, vrátane odkazov na dostupné zdroje
Solar energy harvesting for the mobile robotic platform [electronic] / Michal Hodoň ... [et al.]. In: Innovations for community services [print, electronic] : proceedings. - 1. vyd. - Cham: Springer Nature, 2022. - ISBN 978-3-031-06668-9 (online). - s. 17-27 [print, online]. Zaradené v: SCOPUS, Spôsob prístupu: https://link.springer.com/chapter/10.1007/978-3-031-06668-9_4 Compressed sensing - a way to spare energy in WSN for UAV [electronic] / Ondrej Karpiš ... [et al.]. In: 17th IFAC Conference on Programmable Devices and Embedded Systems PDES 2022 — Sarajevo, Bosnia and Herzegovina, 17-19 May 2022. - 1. vyd. - Amsterdam: Elsevier, 2022. - s. 17-176. Zaradené v: SCOPUS ; Web of Science Core Collection Spôsob prístupu: https://www.sciencedirect.com/science/article/pii/S2405896322003433?via%3Dihub Impact of external phenomena in compressed sensing methods for wireless sensor networks [print] / Michal Kochláň, Michal Hodoň. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems [electronic]. - 1. vyd. - Varšava: Polskie Towarzystwo Informatyczne, 2017. - ISBN 978-83-946253-7-5. - s. 857-863 [print]. Zaradené v: Web of Science Core Collection ; SCOPUS Spôsob prístupu: https://ieeexplore.ieee.org/abstract/document/8104651/ Human-Centric Based Energy and Comfort Optimization in Cognitive Buildings: A Review Irfanullah Khan; Antonio Guerrieri; Franco Cicirelli; A. Stephen McGough; Giandomenico Spezzano 2024 IEEE Conference on Pervasive and Intelligent Computing (PICom) Year: 2024 | Conference Paper | Publisher: IEEE Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks Fath U Min Ullah; Amin Ullah; Ijaz Ul Haq; Seungmin Rho; Sung Wook Baik IEEE Access Year: 2020 | Volume: 8 | Journal Article | Publisher: IEEE Ahmed, Ijaz, et al. "A review on enhancing energy efficiency and adaptability through system integration for smart buildings." Journal of Building Engineering (2024): 109354. Nabavi, Seyed Azad, et al. "Deep learning in energy modeling: Application in smart buildings with distributed energy generation." IEEE Access 9 (2021): 125439-125461.
V prípade otázok sa obráťte na vedúceho témy:
hodon6@uvp.uniza.sk