PV/T SYSTEMS FOR ENERGY EFFICIENCY BY USING ADVANCED DEEP NEURAL NETWORK (DNN) AND NANOFLUID IN SOLAR SYSTEMS
Yazarlar (3)
Prof. Dr. Aybaba HANÇERLİOĞULLARI
Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (Diğer hakemli uluslarası dergilerde yayınlanan tam makale) | ||
| Dergi Adı | Inspiring Technologies and Innovation | ||
| Dergi ISSN | 2822-6062 | ||
| Dergi Tarandığı Indeksler | GOOGLE SCHOLAR | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | – / 0 / – | DOI | 10.5281/zenodo.10119483 |
| Makale Linki | https://zenodo.org/records/10119483 | ||
| Özet |
| Today, solar energy is a very popular alternative energy source due to its enormous availability in nature. In this study, focusing on the electro-mechanical production industry of advanced PV/T solar panels, studies carried out on the development of new methodological methods for the efficiency of existing asset management practices of the infrastructure of this industry and the optimal improvement . For this, it is to integrate a power-generating PV/T panel and a solar thermal heating panel within the same collection surface. In this research, it was implemented using a new roof-mounted PV/T multi-reflection panel, which not only increases the power output of the PV/T panel, but most importantly, the aesthetic aspect is a major barrier to large-scale uptake of PV/TIn this study, we developed a new advanced MPPT(maximum power point tracking) algorithm such as Deep Neural Network (DNN) controller especially for photovoltaic system. The proposed DNN based MPPT algorithm is developed PV/T voltage, current and corresponding duty cycle. |
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BM Sürdürülebilir Kalkınma Amaçları
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