With increasing the number of wind power generators, the consumption time of electromagnetic simulation of the wind farm explodes. To reduce the simulation time while meeting the accuracy requirement, a genetic cluste...
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Many power electronic devices such as converters in wind–storage cogeneration systems introduce harmonic currents owing to their inherent nonlinear characteristics, considerably affecting the power quality of wind–s...
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Fossil fuel usage for heating applications must be reduced considering the issues related to the environment and the restriction of their resources. In this regard, attention is devoted to renewable energy sources to ...
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Fossil fuel usage for heating applications must be reduced considering the issues related to the environment and the restriction of their resources. In this regard, attention is devoted to renewable energy sources to supply the energy requirements of different sectors. In the building sector, solar energy is harnessed for heating and cooling. Solar energy is applicable both directly and indirectly for heating using different technologies. The intermittent nature of solar energy obliges the use of storage units to make the solar systems applicable at night hours or during periods the low solar intensity. Various thermal energy storage materials have been utilized in different kinds of solar heaters to stabilize their performance, improve their reliability, and avoid issues related to variations in solar radiation. In this article, studies on the usage of thermal energy storage units in solar water heaters are reviewed and their key results are reflected. As one of the main conclusions of the reviewed works, it can be denoted that several factors such as the operation condition and characteristics of the storage unit are effective on the function of the systems combined with the thermal storage component. Aside from an increment in the operating hours of solar heaters, usage of storage units can boost both energy and exergy efficiencies. Furthermore, the study denotes that the power saving rate is influenced by the abundance of solar energy resources. In addition, it could be denoted that the performance of the systems is improvable by employing some ideas, such as the application of nanotechnology in storage materials.
Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are i...
Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs’ influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.
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