"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.

致力于可再生能源技术的生物光伏设备（bpv）利用阳光和蓝藻等微生物发电，具有巨大的潜力。然而，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）仍然需要满足商业化。为了克服这些挑战，tony Okedi（作为Adrian Fisher博士研究小组的博士生）使用Hilbert-Huang变换（HHT）分解了bpv的当前输出。他的结论是，所获得的振荡子分量，即所谓的内在模型函数（IMFs），可能为长短期记忆（LSTM）模型创造基础，该模型可以更准确地预测bpv在各种条件下的当前输出。在托尼工作的基础上，我的博士研究可能旨在通过实验确定imf的个体物理意义。此外，可以对单个imf进行建模，并随后将其组合起来，建立一个LSTM模型，以预测bpv的当前输出。最后，通过进一步检查bpv的敏感性，例如光强度和无机碳水平，可以开发更准确的LSTM模型，以估计更广泛的操作条件下的当前输出。因此，所获得的bpv的LSTM模型可以代表bpv的优化方面的巨大进步，从而实现其商业化。