Renewable energy systems are technologies that can generate electricity from solar, wind, hydroelectric, biomass, and other renewable energy resources. This research project aims to find the best renewable energy technology combinations for several scenarios in Malaysia. The strategies are analysed by evaluating the investments in the renewable energy systems in each of the decided scenarios in Malaysia, Pekan, Pahang and Mersing, Johor, using HOMER Pro software. The finding shows that the PV–wind hybrid system has a better net present cost (NPC) than the other systems for both scenarios, which are USD −299,762.16 for Scenario 1 and USD −642,247.46 for Scenario 2. The PV–wind hybrid system has 4.86-year and 2.98-year payback periods in Scenarios 1 and 2. A combination of RE technologies yielded fewer emissions than one kind alone. The PV–wind hybrid system provides a quicker payback period, higher money savings, and reduced pollutants. The sensitivity results show that resource availability and capital cost impact NPC and system emissions. This finding reveals that integrated solar and wind technologies can improve the economic performance (e.g., NPC, payback period, present worth) and environmental performance (e.g., carbon dioxide emissions) of a renewable energy system.

Solar irradiation data is essential for the feasibility of solar energy projects. Notably, the intermittent nature of solar irradiation influences solar energy use in all forms, whether energy or agriculture. Accurate solar irradiation prediction is the only solution to effectively use solar energy in different forms. The estimation of solar irradiation is the most critical factor for site selection and sizing of solar energy projects and for selecting a suitable crop selection for the area. But the physical measurement of solar irradiation, due to the cost and technology involved, is not possible for all locations across the globe. Numerous techniques have been implemented to predict solar irradiation for this purpose. The two types of approaches that are most frequently employed are empirical techniques and artificial intelligence (AI). Both approaches have demonstrated good accuracy in various places of the world. To find out the best method, a thorough review of research articles discussing solar irradiation prediction has been done to compare different methods for solar irradiation prediction. In this paper, articles predicting solar irradiation using AI and empirical published from 2017 to 2022 have been reviewed, and both methods have been compared. The review showed that AI methods are more accurate than empirical methods. In empirical models, modified sunshine-based models (MSSM) have the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM). The NSM has a little lower accuracy than MSSM and SSM, but the NSM can give good results in sunshine data unavailability. Also, the literature review confirmed that simple empirical models could predict accurately, and increasing the empirical model's polynomial order cannot improve results. Artificial neural networks (ANN) and Hybrid models have the highest accuracy among AI methods, followed by support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS). The increase in efficiency by hybrid models is minimal, but the complexity of models requires very sophisticated programming knowledge. ANN's most important input factors are maximum and minimum temperatures, temperature differential, relative humidity, clearness index and precipitation.

This paper investigates double-pass solar air thermal collectors with lava rock as the porous media. The addition of lava rock serves as short-term sensible thermal storage for a solar drying system. It also enhances the convective heat transfer rate to the airflow due to an increased heat transfer area and increased turbulence in the air channel. A mathematical model was developed based on energy balance equations and was numerically solved in MATLAB. The collector’s thermal performance was studied at various levels of solar intensity and at different wind speeds for different design parameters: collector size, air mass flow rate, and lava rock volume. From the study, the optimum efficiencies that were obtained in the range between the intensities of 500 W/m² and 800 W/m² were 62% to 64%, respectively, with an optimum flow rate of 0.035 kg/s. The optimum porosity of about 89% was selected for the collector by considering the pressure drop and thermal efficiency. An optimal temperature output range between 41.7 °C and 48.3 °C could be achieved and was suitable for agricultural and food drying applications. Meanwhile, compared to conventional DPSAHs, the average percentage increase in the output temperature of the DPSAH with lava rock was found to be higher by 17.5%.

Access to uninterrupted power is not a luxury but a basic need. Rural communities living far from the national grid, particularly those in the southern region of Pakistan’s Khyber Pakhtunkhwa province, have limited access to a reliable power supply. In order to provide sustainable electricity, small-scale off-grid renewable energy systems are increasingly used for rural electrification. These systems are commonly known as stand-alone home systems or community micro-grids. This paper proposes an off-grid solar–biogas micro-grid for rural communities in the Lakki Marwat district of Khyber Pakhtunkhwa, Pakistan. The area is mainly dependent upon income from the agricultural and livestock sectors. HOMER was used to simulate the electric power system, while RET-Screen was used to analyze the economics of the system. The optimized system’s results demonstrate that the most economically and technically possible system, which produces 515 kWh and 338.50 m³ biogas daily, is made up of a 30-kW photovoltaic system coupled with a 37-kW biomass hybrid system, a 64-kWh battery storage capacity, and a 20-kW invertor. The system will meet the cooking and power needs of 900 individuals who reside in 100 homes. In addition to household users, the system will provide fixed-priced electricity to productive buildings, and free electricity to community buildings. The system will generate 1300 kg of organic fertilizer each day, which will be sold to local farmers for 50% less than what it would cost on the open market. The proposed approach is techno-economically viable based on the payback period and internal rate of return.

This study presents a prediction model for comparing the performance of six different photovoltaic (PV) modules using artificial neural networks (ANNs), with simple inputs for the model. Cell temperature (T_c), irradiance, fill factor (FF), short circuit current (I_sc), open-circuit voltage (V_oc), maximum power (P_m), and the product of V_oc and I_sc are the inputs of the neural networks’ processes. A Prova 1011 solar system analyzer was used to extract the datasets of IV curves for six different PV modules under test conditions. As for the result, the highest FF was the mono-crystalline with an average of 0.737, while the lowest was the CIGS module with an average of 0.66. As for efficiency, the most efficient was the mono-crystalline module with an average of 10.32%, while the least was the thin-film module with an average of 7.65%. It is noted that the thin-film and flexible mono-modules have similar performances. The results from the proposed model give a clear idea about the best and worst performances of the PV modules under test conditions. Comparing the prediction process with the real dataset for the PV modules, the prediction accuracy for the model has a mean absolute percentage error (MAPE) of 0.874%, with an average root mean square error (RMSE) and mean absolute deviation (MAD) of, respectively, 0.0638 A and 0.237 A. The accuracy of the proposed model proved its efficiency for predicting the performance of the six PV modules.

In this study, the heat transfer characteristics of the flat plate collectors with circular and elliptical serpentine tubes are theoretically analyzed and compared in terms of $\dot{m}$, $Re$, $Nu$, $h_{fi}$, $F_R$, $Q_u$, and ${\mu }_{Th}$ under various water flow rates and the standard test conditions. The results reveal that the maximum ${\mu }_{Th}$ correspondence to the elliptical serpentine design with 56% under turbulent flow, and the minimum ${\mu }_{Th}$ of 47% for the circular cross-section under laminar flow. In addition, it was found that the highest useful energy gain per unit time (493.8 W) through the system is possible when $F_R$, $h_{fi}$, $Nu$, and $\dot{m}$ are maximum and vice versa. It was concluded that, at the same area, the larger contact area in the elliptical cross-section compared to the circular would improve $F_R$ and $Q_u$ by an average of 2%. Overall, it is crucial to evaluate the thermal parameters of the thermal collector during the preliminary design stage to fabricate a highly efficient system and save time as well as initial cost.