An interdigitated electrode (IDE) capacitive humidity sensor fabricated on a silicon substrate was used to investigate sensing materials, which proved to be an ultrahigh-sensitivity humidity sensor. A sensing layer combination (SLC) between vertically aligned ZnO nanorods and optimal graphene oxide (GO) was prepared on the device and was tested as a humidity sensor. X-ray diffractometry (XRD) exhibited crystallized wurtzite structure of ZnO nanorods and transmission electron microscope (TEM) shown perfectly indexed hexagonal wurtzite ZnO structure dots position correspondence. A scanning electron microscope (SEM) was used to analyze ZnO nanorods/GO morphologies. Furthermore, Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) clearly exhibited GO presence and hydrophilic functional groups (carboxyl, epoxy, and hydroxyl), respectively. The SLC prominently demonstrated ultrahigh sensitivity (up to 196.95% or 1.97 times from commercial sensor; HS1101, Humirel) and linear responses behavior with 0.96 for coefficient of determination. The device sensitivity obviously improved as steps of 40, 50, 60, 70, 80, and 90% RH at values of 1.09, 1.41, 1.51, 1.65, 1.80, and 1.91 times, respectively. The device also exhibited fast response (25 s) and short recovery times (17 s). Its hysteresis (6.58%) manifestly improved to 1.84 times. Moreover, repeatability and long-term ability of the device demonstrated high accuracy (range ±0.37pF) and durability.

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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This paper proposes a new concept to improve accuracy of PV forecasting model. The model was implemented by MATLAB/Simulink software using solar irradiance and module temperature as measurement parameters for calculation. The model was developed by single-diode equivalent circuits (5-p model) for simulated PV module power output and compared with other software programs for validation which showed correct PV characteristics. To achieve high accuracy, the model was improved by weight function using one-year measured data. The accuracy of our developed model was verified by comparison with four commercial simulator software programs and the results from real system which were measured and recorded for 1 year. It was found that the model output was in a good agreement with the measured data. This research can be utilized in another area by adjusting the PV equation with weight function of that area.

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