"Establishment of a long short-term memory (LSTM) model using the Hilbert-Huang transform (HHT) to forecast the current outputs of biophotovoltaic dev

Striving towards renewable energy technologies, biophotovoltaic devices (BPVs), which generate electricity using
sunlight and microbes like cyanobacteria, can pose a great potential. However, several challenges such as a low power
output and optimisation difficulties (due to many unpredictable parameters) still need to be met for commercialisation.
With the goal to overcome some of these challenges, Tonny Okedi (as a PhD student in Dr Adrian Fisher's research
group) decomposed BPVs' current outputs using the Hilbert-Huang transform (HHT). He concluded that the obtained
oscillatory subcomponents, so-called intrinsic model functions (IMFs), may create the basis for a long short-term memory
(LSTM) model which can more accurately predict the BPVs' current outputs at various conditions. Building upon Tonny's
work, my PhD research may aim to determine the IMFs' individual physical meanings through experiments. Moreover,
the individual IMFs could be modelled and subsequently combined to establish an LSTM model that forecasts the BPVs'
current outputs. Lastly, by further examining the BPVs' sensitivities with regards to, for example, light intensities and
inorganic carbon levels, a more accurate LSTM model can be developed that estimates current outputs for a wider range
of operating conditions. As a result, the obtained LSTM model of BPVs could represent a great advancement with
regards to BPVs' optimisation and, consequently, their commercialisation.