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

“使用希尔伯特-黄变换（HHT）建立长短期记忆（LSTM）模型来预测生物光伏开发的当前输出

• 批准号: 2801696
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2801696
• 关键词: Establishment; long; short; term; memory

Striving towards renewable energy technologies, biophotovoltaic devices (BPVs), which generate electricity usingsunlight and microbes like cyanobacteria, can pose a great potential. However, several challenges such as a low poweroutput 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 researchgroup) decomposed BPVs' current outputs using the Hilbert-Huang transform (HHT). He concluded that the obtainedoscillatory 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'swork, 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 andinorganic carbon levels, a more accurate LSTM model can be developed that estimates current outputs for a wider rangeof operating conditions. As a result, the obtained LSTM model of BPVs could represent a great advancement withregards to BPVs' optimisation and, consequently, their commercialisation.

在可再生能源技术方面，利用太阳光和蓝藻等微生物发电的生物光伏设备（BPV）具有巨大的潜力。然而，BPV的商业化仍面临一些挑战，如低功率输出和优化困难（由于许多不可预测的参数）。为了克服其中一些挑战，Tonny Okedi（Adrian Fisher博士研究小组的博士生）使用希尔伯特-黄变换（HHT）分解了BPV的电流输出。他的结论是，获得的振荡子组件，即所谓的内在模型函数（IMF），可以为长短期记忆（LSTM）模型奠定基础，该模型可以更准确地预测BPV在各种条件下的电流输出。基于托尼的工作，我的博士研究可能旨在通过实验确定IMF的个人物理意义。此外，可以对各个IMF进行建模，并随后进行组合，以建立预测BPV当前输出的LSTM模型。最后，通过进一步检查BPV对例如光强度和无机碳水平的敏感性，可以开发出更准确的LSTM模型，以估计更广泛的操作条件下的电流输出。因此，所获得的BPVs的LSTM模型可以代表BPVs优化及其商业化的巨大进步。