Table 5 - uploaded by Michael Saulo
Content may be subject to copyright.
Source publication
As the energy costs continue to rise steadily, researchers are looking for alternative sources of energy to meet the rising demand for sustainable energy. Finding an inexpensive and reliable energy generation technology is a big challenge both in developed and developing countries. Innovation and invention of new technologies, mass production and e...
Contexts in source publication
Context 1
... shows the monthly DNI of Lodwar which is obtained from the weather database from the NREAl website. [8] The collector model shown in Fig.8 assumes is a physical parabolic solar collector which has the following had the following parameters as listed in Table 5. Table 6 shows the varying DNI in the 1st day of January in Lodwar. ...
Context 2
... plant has their own power conversion units (PCU). Table 8 shows the monthly average energy production from the CSP plant from the DNI with the parameters listed in Table 5. The CSP part of the hybrid power plant relies on the DNI from the sun to heat up a HTF which is used to run the turbine. ...
Similar publications
Nanocarbon materials have great potential for sustainable energy harvest and energy utilizations, such as solar thermal stream generation, and interfacial evaporation. However, the evaporation rate is far too low for practical applications. The technologies are not ready yet for industries requiring rapid, energy‐efficient, and low‐cost evaporation...
Citations
The photovoltaic thermal greenhouse system highly supports the production of biogas. The system’s prime advantage is biogas heating and crop drying through varied directions of air flow. Further, it diminishes the upward loss of the system. This paper aims to model a practical greenhouse system for obtaining the precise estimation of the heating efficiency, given by the solar radiance. The simulation model adopts the self-adaptive firefly neural network model that applies on known experimental data. Therefore, the error function between the model outcome and the experimental outcome is substantially minimized. The performance analysis involves an effective comparative study on the root mean square error between the adopted self-adaptive firefly neural network model and the conventional models such as Levenberg–Marquardt neural network and firefly neural network. Later, the impact of self-adaptiveness, FF update and learning performance on attaining the knowledge regarding the characteristics of SAFF algorithm is analysed to yield better performance.