Time series analysis and long short-term memory (LSTM) network prediction of BPV current density-Reference-Cited by-同舟云学术

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Time series analysis and long short-term memory (LSTM) network prediction of BPV current density

Published:2021 Issue:4 Volume:14 Page:2408-2418
ISSN:1754-5692
Container-title:Energy & Environmental Science
language:en
Short-container-title:Energy Environ. Sci.

Author:

Okedi Tonny I.¹²³⁴⁵^ORCID,Fisher Adrian C.¹²³⁴⁵^ORCID

Affiliation:

1. Department of Chemical Engineering and Biotechnology

2. University of Cambridge

3. Phillipa Fawcett Drive

4. Cambridge CB3 0AS

5. UK

Abstract

LSTM networks are shown to predict the seasonal component of biophotovoltaic current density and photoresponse to high accuracy.

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/EE/D0EE02970J

Reference35 articles.

1. Biophotovoltaics: oxygenic photosynthetic organisms in the world of bioelectrochemical systems

2. Biophotovoltaics: Green Power Generation From Sunlight and Water

3. Light-Dependent Electrogenic Activity of Cyanobacteria

4. Quantitative analysis of the factors limiting solar power transduction by Synechocystis sp. PCC 6803 in biological photovoltaic devices

5. A Bioelectrochemical Approach to Characterize Extracellular Electron Transfer by Synechocystis sp. PCC6803

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