|Commenced in January 2007||Frequency: Monthly||Edition: International||Paper Count: 13|
The use of alternative energy sources to meet energy demand reduces environmental damage. To diversify an energy matrix and to minimize global warming, a solar energy is gaining space, being an important source of renewable energy, and its potential depends on the climatic conditions of the region. Brazil presents a great solar potential for a generation of electric energy, so the knowledge of solar radiation and its characteristics are fundamental for the study of energy use. Due to the above reasons, this article aims to verify the climatic variability corresponding to the variations in solar radiation anomalies, in the face of climate change scenarios. The data used in this research are part of the Intercomparison of Interconnected Models, Phase 5 (CMIP5), which contributed to the preparation of the fifth IPCC-AR5 report. The solar radiation data were extracted from The Australian Community Climate and Earth System Simulator (ACCESS) model using the RCP 4.5 and RCP 8.5 scenarios that represent an intermediate structure and a pessimistic framework, the latter being the most worrisome in all cases. In order to allow the use of solar radiation as a source of energy in a given location and/or region, it is important, first, to determine its availability, thus justifying the importance of the study. The results pointed out, for the 75-year period (2026-2100), based on a pessimistic scenario, indicate a drop in solar radiation of the approximately 12% in the eastern region of Rio Grande do Sul. Factors that influence the pessimistic prospects of this scenario should be better observed by the responsible authorities, since they can affect the possibility to produce electricity from solar radiation.
The purpose of this paper is to establish the influence of shading on a Building Integrated Photovoltaic (BIPV) system´s performance in an urban context. The PV systems of the Science Center of Complexity (Centro de Ciencias de la Complejidad) Building based in the Main Campus of the National and Autonomous University of Mexico (UNAM) in Mexico City was taken as case study. The PV systems are placed on the rooftop and on the south façade of the building. The south-façade PV system, operating as sunshades, consists of two strings: one at the ground floor and the other one at the first floor. According to the building’s facility manager, the south-façade PV system generates 42% less electricity per kilowatt peak (kWp) installed than the one on the roof. The methods applied in this study were Solar Radiation Analysis (SRA) simulations performed with the Insight 360 Plug-in from Revit 2018® and an on-site measurement using specialized tools. The results of the SRA simulations showed that the shading casted by the PV system placed on the first floor on top of the PV system of the ground floor decreases its solar incident radiation over 50%. The simulation outcome was compared and validated to the measured data obtained from the on-site measurement. In conclusion, the loss factor achieved from the shading of the PVs is due to the surroundings and the PV system´s own design. The south-façade BIPV system’s deficient design generates critical losses on its performance and decreases its profitability.
This paper deals with the optimum tilt angle for the solar collector in order to collect the maximum solar radiation. The optimum angle for tilted surfaces varying from 0◦ to 90◦ in steps of 1◦ was computed. In present study, a theoretical model is used to predict the global solar radiation on a tilted surface and to obtain the optimum tilt angle for a solar collector in Bursa, Turkey. Global solar energy radiation on the solar collector surface with an optimum tilt angle is calculated for specific periods. It is determined that the optimum slope angle varies between 0◦ (June) and 59◦ (December) throughout the year. In winter (December, January, and February) the tilt should be 55◦, in spring (March, April, and May) 19.6◦, in summer (June, July, and August) 5.6◦, and in autumn (September, October, and November) 44.3◦. The yearly average of this value was obtained to be 31.1◦ and this would be the optimum fixed slope throughout the year.
Per capita energy usage in any country is exponentially increasing with their development. As a result, the country’s dependence on the fossil fuels for energy generation is also increasing tremendously creating economic and environmental concerns. Tropical countries receive considerable amount of solar radiation throughout the year, use of solar energy with different energy storage and conversion methodologies is a viable solution to minimize the ever increasing demand for the depleting fossil fuels. Salinity gradient solar pond is one such solar energy application. This paper reports the characteristics and performance of a thermally insulated, experimental salinity-gradient solar pond, built at the premises of the University of Kelaniya, Sri Lanka. Particular stress is given to the behavior of the evolution of the three layer structure exist at the stable state of a salinity gradient solar pond over a long period of time, under different environmental conditions. The operational procedures required to maintain the long term thermal stability are also reported in this article.
A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled direct normal irradiance field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.
The mathematical analysis on radiation obtained and the development of the solar photovoltaic (PV) array groundwater pumping is needed in the rural areas of Thohoyandou for sizing and power performance subject to the climate conditions within the area. A simple methodology approach is developed for the directed coupled solar, controller and submersible ground water pump system. The system consists of a PV array, pump controller and submerged pump, battery backup and charger controller. For this reason, the theoretical solar radiation is obtained for optimal predictions and system performance in order to achieve different design and operating parameters. Here the examination of the PV schematic module in a Direct Current (DC) application is used for obtainable maximum solar power energy for water pumping. In this paper, a simple efficient photovoltaic water pumping system is presented with its theoretical studies and mathematical modeling of photovoltaics (PV) system.
Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.
Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.