Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system. Issue 7 (November 2018)

• Record Type: Journal Article
• Authors: Singh, Gurinderpal; Jain, VK; Singh, Amanpreet
• Is Part Of: Energy & environment. Volume 29:Issue 7(2018)
• Page Start: 1073
• Page End: 1097
• Publication Date: 2018-11
• Subjects: Photovoltaic thermal greenhouse system -- biogas -- neural network -- firefly -- self-adaptive; Power resources -- Environmental aspects -- Periodicals; Power resources -- Periodicals; Renewable energy sources -- Periodicals; Energy policy -- Environmental aspects -- Periodicals; Ressources énergétiques; Politique énergétique; Energy policy -- Environmental aspects; Power resources; Power resources -- Environmental aspects; Renewable energy sources; Periodicals; 333.7
• DOI: 10.1177/0958305X18768819
• Languages: English
• ISSNs: 0958-305X

Abstract

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.