Agricultural irrigation developments have gained attention to improve crop yields and reduce water use. However, traditional irrigation requires excessive amounts of water and consumes high electrical energy to schedule irrigations. This paper proposes a fuzzy-based intelligent irrigation scheduling system using a low-cost wireless sensor network (WSN). The fuzzy logic system takes crop and soil water variabilities into account to adaptively schedule irrigations. The theoretical crop water stress index (CWSI) is calculated to indicate plant water status using canopy temperature, solar irradiation, and vapor pressure deficit. Furthermore, the soil moisture content obtained by a capacitive soil moisture sensor is used as a determination of water status in soil. These two variables are thus incorporated to improve the precision of the irrigation scheduling system. In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance. Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%. The experimental results reveal that the proposed irrigation scheduling system is effective in terms of precision irrigation scheduling and efficient regarding water use and energy consumption. Finally, the cost analysis is performed to confirm the economic benefit of the proposed irrigation scheduling system.

With the rapid increase in photovoltaic (PV) power generation in microgrids, PV power fluctuations can initiate negative impacts on microgrid operations. This study presents a simulation analysis of PV power smoothing method based on hull enhanced linear exponential smoothing (HELES) technique using an energy storage system (ESS). The proposed method is employed to mitigate PV power fluctuations in microgrid systems. The ESS is allowed to charge and discharge for smoothing the PV output power based on a smoothing power reference provided by the HELES technique. The proposed method is investigated to acquire the smoothing performance considering smoothness and smoothing accuracy. In simulation analysis, the proposed method is analysed on the microgrid under both grid‐connected and islanding modes to obtain its dynamic performance. The simulation results demonstrate that the proposed method evidently offers superior smoothing performance compared with the existing methods. Accordingly, the smoothing accuracy is successfully improved, and the ESS capacity is significantly reduced by improving smoothing accuracy. Moreover, the smoothness is effective to mitigate PV power fluctuations by using the proposed method. Eventually, the dynamic performance can be satisfactorily improved, and state of charge variation is reduced and also conserved to be available for unexpected PV power fluctuations.