SUN’IY INTELLEKT ASOSIDA UNIVERSITET KOMPYUTER LABORATORIYALARIDA ENERGIYA SARFINI OPTIMALLASHTIRISH TIZIMINI ISHLAB CHIQISH
Abstract
Bugungi kunda raqamli texnologiyalarning jadal rivojlanishi natijasida oliy ta’lim muassasalarida kompyuter texnikalaridan foydalanish hajmi sezilarli darajada ortib bormoqda. Universitetlarning kompyuter laboratoriyalari o‘quv jarayonining muhim qismi hisoblanib, ularda yuzlab kompyuterlar, serverlar va tarmoq qurilmalari faoliyat yuritadi. Biroq ushbu qurilmalarning aksariyati foydalanilmayotgan vaqtlarda ham elektr energiyasini iste’mol qilishda davom etadi. Natijada energiya resurslarining samarasiz sarflanishi, ekspluatatsiya xarajatlarining ortishi va ekologik muammolarning kuchayishi kuzatiladi. Mazkur maqolada universitet kompyuter laboratoriyalarida energiya sarfini kamaytirish maqsadida sun’iy intellektga asoslangan aqlli boshqaruv tizimini ishlab chiqish masalalari ko‘rib chiqilgan. Tadqiqot davomida energiya iste’moliga ta’sir etuvchi omillar tahlil qilinib, mashinali o‘qitish algoritmlaridan foydalangan holda energiya sarfini prognozlash va optimallashtirish modeli taklif etilgan. Olingan natijalar sun’iy intellekt texnologiyalaridan foydalanish energiya samaradorligini oshirish va universitet infratuzilmasini yanada takomillashtirish imkonini berishini ko‘rsatadi.
References
Russell S., Norvig P. Artificial Intelligence: A Modern Approach. Pearson Education, 2021.
Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, 2016.
Mitchell T. Machine Learning. McGraw-Hill Education, 2017.
Alpaydin E. Introduction to Machine Learning. MIT Press, 2020.
Wang K., Wang Y. Energy Efficient Smart Campus Architecture Based on Artificial Intelligence. IEEE Access, 2022.
Batra N., Singh A. Machine Learning Approaches for Building Energy Optimization. Energy Informatics, 2021.
Zhang Y., Zhao X. Intelligent Energy Management Systems Using Artificial Intelligence Techniques. Sustainable Computing, 2023.
Gubbi J., Buyya R. Internet of Things: Vision, Applications and Future Directions. Future Generation Computer Systems, 2018.
Siano P. Demand Response and Smart Grids: A Survey. Renewable and Sustainable Energy Reviews, 2019.