000			02336nam a22002657a 4500
003			KOHA
005			20241008134057.0
008			240927d2023 cy ||||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aD 440 _bS67 2023
100	1		_aSorguli, Sarhang
245	1	2	_aA NOVEL ENERGY ACCOUNTING MODEL USING FUZZY RESTRICTED BOLTZMANN MACHINE RECURRENT NEURAL NETWORK / _cSARHANG SORGULI ; SUPERVISOR, PROF. DR. MEHMT AĞA
264			_c2023
300			_a157 sheets ; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Business Administration
520			_aEnergy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5).
650		0	_aBusiness Administration _vDissertations, Academic
700	1		_aAğa, Mehmt _esupervisor
942			_2ddc _cTS
999			_c292876 _d292876