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Solar energy has been widely used in various aspects as the greatest promising and pollution free energy comparing with other available resources in nature. Photovoltaic-thermal (PV/T) is the most generative technology, which has been invented to utilize electrical energy and heat from the solar system. The article presents a novelty of using Extreme Learning Machine (ELM) into the air type PV/T technology. For this purposes, two air type PV/T designs were fabricated and practiced for a cooling fin design in the collector and finally, collected the experimental data, which was adapted to estimate electrical and thermal efficiency for the PV/T system. Then, the results of ELM prediction model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental result was accommodated to improving the predictive accuracy of the ELM approach in comparison. Further, outcome results indicate that developed ELM models can be used satisfactorily to formulate the predictive algorithm for PV/T performances. The ELM algorithm made a good generalization, which can learn very faster comparing with other conventional popular learning algorithms. The results revealed that the improved ELM model is a well fitted tool to predict the thermal and electrical efficiency with higher accuracy.

Feasibility study of the renewable energy resources is usually employed as a primary step in the design of Hybrid Renewable Energy System (HRES), to highlight the most potential sources and the best combinations obtained by using HOMER software. Thus, the main objective of this study is to identify the potential areas in Malaysia utilize solar, the wind and Micro Hydropower (MHP) sources. For this purpose, the results of a study accomplished by Ibrahim Hussein in 2010 are considered to obtain sites with the greatest hydropower potential in Malaysia. Then, a renewable energy resource assessment for these sites was performed to identify the sites that have the greatest solar, wind and hydropower potentials based on total Net Present Cost (NPC) comparison. Based on the historical data, a daily pattern emerged in electricity consumption among rural residents in Malaysia. The results of this study indicate Langkawi is the most potential region for the solar/wind combination by the significant difference with other regions (total NPC of 696,083 USD). After Langkawi, Tioman Islands has the second rank (total NPC of 818,931 USD), while villages in Tioman are the most appropriate sites for installing hybrid renewable energy system with the current specifications. In terms of the premiered locations for solar and hydropower potential, four sites at Lawas in Sarawak and Ranau in Sabah were identified as the greatest potential (total NPC of 354,000 USD). These findings demonstrate that Tioman Islands and Malaysian Borneo Island (Sabah and Sarawak) can be considered as the most attractive regions for the solar/wind/MHP and solar/MHP, respectively.

Photovoltaic thermal (PV/T) system was introduced to meet the thermal and electrical energy. The heat removal by air or water prevents the deterioration of the PV cell efficiency due to the overheating of cells. In this study, an air type single pass PV/T collector system was proposed where a number of thin rectangular fins were introduced for heat dissipation. The collector’s performance was analyzed with a fin system that was integrated by a thin flat metallic sheet (TFMS). Then, the temperature parameters were measured and compared to several operating conditions and configurations. Analytical expression was derived from the energy balance equations for each component of the design. Average temperatures from the top/rear PV surfaces, the collector back wall surface and the collector inlet/outlet temperatures were experimentally recorded under different fin numbers (0–4), mass flow rates (0.02 kg/s–0.14 kg/s) and solar radiations (200 W/m2–700 W/m2). These readings were used in calculating the thermal and electrical efficiency of the proposed PV/T system. The maximum thermal efficiency and PV efficiency were obtained about 56.19% and 13.75% respectively for four fins at 0.14 kg/s of mass flow rate and 700 W/m2 of solar radiation. Besides, the root mean square percentages of deviation (e) and coefficient of correlation (r) were used to validate the result while also discussing the uncertainty values. This research will be helpful to design thermal collectors and provide valuable information regarding performance improvement methods in PV/T systems.

A technical feasibility study of an innovative hybrid solar-wind-rain eco-roof system with natural ventilation and skylight for electrical energy generation and saving is presented in this paper. The system integrates and optimizes five green technologies: wind turbine, solar photovoltaic system, rain water harvesting and utilization system, natural ventilation and roof sky lighting. The design was conceptualized based on the experiences acquired from meteorological data for Malaysian application. The V-shape top roof which was designed above the wind turbine was able to increase the wind speed by the venturi effect before the wind interacts with the wind turbine located between the roofs, hence improve the performance of the wind turbine. An automatic cooling and cleaning system with a stream of water was designed to clean and cool the roof which can improve the electrical efficiency of the solar photovoltaic modules. The ventilation vents of the system allow warm air inside the building to be ventilated out from the building by the pressure difference caused by high speed wind due to the roof design. The skylighting in the system improve the living comfort level and reduce power consumption of artificial lighting. Technical assessment of the system showed the estimated annual energy generated was 21205.65 kWh, estimated energy savings was 1839.6 kWh, the estimated annual ventilation rate was 2.17 × 108 m3 and reduction of CO2 emissions was 17767.89 kg/yr.

Thermal comfort in the building is a major concern among the design expertise especially in tropical countries. Therefore, it is important to design a smart roof system to reduce the heat gain of the building and enhance the resident's thermal comfort. The leading feature of this system is using PVC tubes integrated with a layer of insulation material and fans and located at the underside of the roof to act as a moving-air path (MAP) system. Indoor experiment is carried out on the scale roof model and the heat transfer rate into the attic is determined using experimental data. Next, Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as a soft-computing method determine the predominant variables that affecting the thermal comfort in the building. Training and testing data of the ANFIS model are collected from the experimental measurement. Mass flow rate, solar-air temperature, inlet air temperature, outlet air temperature and ambient temperature are the input parameters used to compute the output parameter which is the attic temperature. The results indicated that the combination of mass flow rate and ambient temperature is the primary factor and the best predictor accuracy for thermal comfort in the building.

In photovoltaic–thermal (PV/T) system analysis, solar collectors with numerous design concepts have been used to purvey the thermal and electrical energy effectively. In this study, two types of solar thermal collectors in PV/T system are proposed and fabricated called design A and design B respectively. In order to investigate the effects of collector type on the system performance a thin flat metallic sheet (TFMS) and fins were introduced as an effective heat absorber and heat sink in the collectors. Extensive experiments were carried out for different conditions under indoor solar simulator. Then PV/T thermal and electrical efficiency were calculated by using data obtained from experiments. Here, support vector machine (SVM) model is designed to estimate the thermal and electrical output which predicts the values for some input variables. For this purpose, three SVM models namely SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA) and with using the radial basis function (SVM-RBF) were analyzed. The estimation and prediction results of these models were compared with each other using statistical indicators i.e. root means square error, coefficient of determination and Pearson coefficient. The experimental results show that a significant improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicate that proposed SVM-Wavelet model can adequately predict the electrical and thermal efficiencies of PV/T system. In the final analysis, a proper sensitivity analysis is performed to identify the influence of considered input elements on performance prediction of PV/T system.