Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks

Alghamdi, Hisham; Maduabuchi, Chika; Okoli, Kingsley; Albaker, Abdullah; Alatawi, Ibrahim; Alsafran, Ahmed S.; Alkhedher, Mohammad; Chen, Wei‐Hsin

doi:10.1155/2023/6927245

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…198 Developing accurate models and theories for predicting and optimizing thermoelectric performance is ongoing. 199 Combining expertise from materials science, chemistry, physics, and engineering is essential to address these challenges. 197 Researchers and developers are currently working on overcoming these obstacles to harness the full potential of organic thermoelectric materials for energy harvesting and conversion.…”

Section: Conclusion Challenges and Future Scopementioning

confidence: 99%

Graphene-derived composites: a new Frontier in thermoelectric energy conversion

Rathi,

Brajpuriya,

Gupta

et al. 2024

Energy Adv.

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Section: Conclusion Challenges and Future Scopementioning

confidence: 99%

Graphene-derived composites: a new Frontier in thermoelectric energy conversion

Rathi,

Brajpuriya,

Gupta

et al. 2024

Energy Adv.

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“…These models primarily process sequence data, such as time series, text, and speech. RNN has a loop structure, allowing the retainment of information from previous steps [26]. This feature enables the RNN to process data sequences by considering the context from previous time steps.…”

Section: Proposed Schemementioning

confidence: 99%

Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

Park,

Kim,

Moon

et al. 2023

International Journal of Energy Research

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Accurate photovoltaic (PV) power forecasting is essential for the stable and reliable operation of PV power generation systems. Recently, various deep learning- (DL-) based forecasting models have been proposed for accurate forecasting, but newly built systems cannot benefit from them due to the absence of PV power data. Although zero-shot methods based on single site can be used for PV power forecasting, they suffer from performance degradation problems when the characteristics of the source data and target data are different. To address this issue, we propose a novel zero-shot PV power forecasting scheme that leverages historical data from multiple PV generation systems at different sites. The proposed scheme constructs an individual forecasting model using historical data from each PV generation system. Then, two correlation coefficients are calculated for each forecasting model: one based on the correlation between the input variables of the source data and target data and the other on the correlation between the input variables and output variables of the source data. Lastly, the final forecasting value is calculated as a weighted sum of the predicted values of the constructed forecasting models for the input variables of the target data. In the extensive experiments for diverse DL models for forecasting, correlation coefficient types for weights, and data time intervals, the combination of recurrent neural network, Pearson’s correlation coefficient, and solar-noon time yielded the best prediction performance, with an improvement of up to 34.47% in mean absolute error and up to 15.94% in root mean square error compared to the best single-site zero-shot prediction. In addition, in experiments on PV power data from 9 cities in Korea using this combination, the proposed scheme achieved the best predictive performance in almost all cases and the second-best performance with a very narrow margin only in a few cases.

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