Research Information System
5th International Conference on Engineering and Applied Natural Sciences, Konya, Turkey, 25 - 26 August 2024, pp.751-761, (Full Text)
In this study, the parameters including power generations as well as waste-water parameters
obtained from a waste-water treatment facility were forecasted, using long short-term memory (LSTM)
method. Accordingly, a total of 445 data, that is found in the data folder of power generation (P) and data
cluster involving of physical and chemical parameters has been utilized, in the estimations depending on
LSTM tool. In this context, every instantaneous data of these data clusters formed of 445 reading points
corresponds to daily mean energy generation (P) obtained from the waste water treatment facility’s gas
turbines as well as corresponds to physical and chemical parameters involving of conductivity (σ), degree
of acidity (pH), temperature (T), and the daily cumulative volumetric flow of the generated methane gas
that is burned in the gas generator (Q). All in all, based on the power generation (P) outcomes, the
statistical error results have indicated that the best forecasting result was obtained for LSTM at the hidden
layer number of HL=100. Namely, at this HL number, error results of mean absolute error (MAE), root
mean square error (RMSE), and the correlation coefficient (R) were reported to correspond 2.4956
MWh/day, 3.7002 MWh/day, and 0.9541, respectively.