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Design Support Using a Neural Network Algorithm

Design Support Using a Neural Network Algorithm

Copyright: © 2014 |Pages: 39
ISBN13: 9781466657960|ISBN10: 1466657960|EISBN13: 9781466657977
DOI: 10.4018/978-1-4666-5796-0.ch009
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MLA

Shin’ya Obara. "Design Support Using a Neural Network Algorithm." Optimum Design of Renewable Energy Systems: Microgrid and Nature Grid Methods, IGI Global, 2014, pp.282-320. https://doi.org/10.4018/978-1-4666-5796-0.ch009

APA

S. Obara (2014). Design Support Using a Neural Network Algorithm. IGI Global. https://doi.org/10.4018/978-1-4666-5796-0.ch009

Chicago

Shin’ya Obara. "Design Support Using a Neural Network Algorithm." In Optimum Design of Renewable Energy Systems: Microgrid and Nature Grid Methods. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5796-0.ch009

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Abstract

This chapter consists of two sections, ‘Dynamic Operational Scheduling Algorithm for an Independent Microgrid with Renewable Energy’ and ‘Operation Prediction of a Bioethanol Solar Reforming System Using a Neural Network’. In the 1st section, a dynamic operational scheduling algorithm is developed using a neural network and a genetic algorithm to provide predictions for solar cell power output (PAS). The section shows that operating the microgrid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data. Because solar radiation and outside air temperature are unstable, it is difficult to predict operation of the system with accuracy. Therefore, the 2nd section developes an operation prediction program of the FBSR (bioethanol reforming system) using a layered neural network (NN) with the error-correction learning method.

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