This work represents an effective design of a temperature regulated PV module by integrating phase change materials for Malaysian weather condition. Through the numerical analysis and experimental investigation it has been shown that if a PCM layer of width 0.02 m of RT 35 is used as a cooling arrangement with a PV module, the surface temperature of the module is reduced by 10°C, which remains constant for a period of 4–6 hours. This reduction of temperature implies the increase in conversion efficiency of the module. Experiment as well as investigation has been carried out considering typical Malaysian weather. Obtained result has been validated by using experimental prototype and comparative analysis.

With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness.

Manipulation of the LED illumination spectrum can enhance plant growth rate and development in grow tents. We report on the identification of the illumination spectrum required to significantly enhance the growth rate of sweet basil (Ocimum basilicum L.) plants in grow tent environments by controlling the LED wavebands illuminating the plants. Since the optimal illumination spectrum depends on the plant type, this work focuses on identifying the illumination spectrum that achieves significant basil biomass improvement compared to improvements reported in prior studies. To be able to optimize the illumination spectrum, several steps must be achieved, namely, understanding plant biology, conducting several trial-and-error experiments, iteratively refining experimental conditions, and undertaking accurate statistical analyses. In this study, basil plants are grown in three grow tents with three LED illumination treatments, namely, only white LED illumination (denoted W*), the combination of red (R) and blue (B) LED illumination (denoted BR*) (relative red (R) and blue (B) intensities are 84% and 16%, respectively) and a combination of red (R), blue (B) and far-red (F) LED illumination (denoted BRF*) (relative red (R), blue (B) and far-red (F) intensities are 79%, 11%, and 10%, respectively). The photosynthetic photon flux density (PPFD) was set at 155 µmol m⁻² s⁻¹ for all illumination treatments, and the photoperiod was 20 h per day. Experimental results show that a combination of blue (B), red (R), and far-red (F) LED illumination leads to a one-fold increase in the yield of a sweet basil plant in comparison with only white LED illumination (W*). On the other hand, the use of blue (B) and red (R) LED illumination results in a half-fold increase in plant yield. Understanding the effects of LED illumination spectrum on the growth of plant sweet basil plants through basic horticulture research enables farmers to significantly improve their production yield, thus food security and profitability.

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.