The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model.
Combustion of fuels to produce heat or other forms of power has been the cornerstone of industrial processes. Liquefied Petroleum Gas (LPG) is an alternative fuel, which is used primarily as a fuel in most spark-ignited internal combustion engines. Therefore, the incidence of leakage in gas cylinders needs to be considered, in order to avoid fire, poisoning, and even death to those around it. Olfactory mobile robot can be used to detect gas content. Thus, the source of the gas leak in the industry and in the storage area for LPG gas cylinders can be identified. This paper emphasizes on robot navigation by using Machine Vision System (MVS) to speed up the process of finding the exact location of the gas leak source. Several tests were carried out on several aspects. The test results on the motor drivers show that the robot can move well. While the MVS testing shows that the programming algorithm for image processing is able to recognize track borders. Then, the results on gas sensors testing show that the robot can find the source of the gas leak and can adjust it to the required robot speed. The last test, which is on the whole system, shows that the duration needed to find the source of the gas leak is in accordance with the distance from the source of the gas leak.
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