{"id":158,"date":"2022-04-21T02:41:49","date_gmt":"2022-04-21T02:41:49","guid":{"rendered":"https:\/\/icwt-seei.org\/2022\/?page_id=158"},"modified":"2022-05-31T04:57:07","modified_gmt":"2022-05-31T04:57:07","slug":"keynote-speakers","status":"publish","type":"page","link":"https:\/\/icwt-seei.org\/2022\/keynote-speakers\/","title":{"rendered":"KEYNOTE SPEAKERS"},"content":{"rendered":"\t\t
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Andrew, Chen-Yeon CHU Ph.D.\u00a0<\/strong><\/h4>

Professor<\/strong>, Feng Chia University\u00a0<\/strong><\/h4>

Director, Institute of Green Products, Feng Chia University\u00a0<\/strong><\/p>

Executive Secretary, APEC Research Center for Advanced Biohydrogen Technology<\/strong><\/p>

Title<\/strong>: Deep Learning Model Established Using Satellite Cloud Images for Short-term Power Generation Forecasting of Solar Power Plants<\/p>

As the demand for solar power generation increases, the forecast of solar energy becomes more and more important. Solar photovoltaic system is the main source of solar power. Solar power plants\u2019 management and operation with energy storage systems need reliable solar power generation forecasts. These power plants can work like conventional power plants, avoid the uncertainty of power output and can compete in the energy market. Solar power generation prediction technology will have a significant impact on the future of large-scale solar energy plants. Forecast the power generation of solar photovoltaic systems dramatically depends on Climatic conditions, which will fluctuate over time.<\/p>

This research is based on the historical data of the solar power generation systems of the Ming-Lun Senior High School, Taipei City and Shu Guang Girl\u2019s Senior High School, Hsinchu City in Taiwan, and Asian Development College for Community Economy and Technology, Chiang Mai Rajabhat University in Thailand. The data also collected from Japanese himawari-8 satellite cloud images and the parameters of the position of the sun relative to the power plant location. The cloud image uses CNN to extract features and LSTM to forecast the next set of features. The feature selection is performed by comparing the direction vector X and Y of the sun relative to the power plant location, and the prediction results are corrected according to the angle between the direction of sunlight and the solar panel. A deep learning model for prediction solar power generation based on wide-range cloud obscuration rate was established. Use deep learning methods to train the model to forecast power generation. The final forecast results can be obtained that the revised forecast accuracy rates of the Chiang Mai case in Thailand, Hsinchu city case and Taipei city case were 92%, 90% and 85%, respectively. Finally, it was found that the Thailand case’s revised forecast accuracy rate was higher than 90% of the general revised forecast accuracy rate of the single international case.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Elhadj Dogheche\u00a0<\/strong><\/h4>

Univ. Polytechnique Hauts-de-France, CNRS, Univ. Lille, UMR 8520 – IEMN – Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie, F-59313 Valenciennes, France<\/strong><\/p>

Title: <\/strong>Light Fidelity for Hospital of The Future<\/p>

Light Fidelity Communication (LiFi) is actually an emergent wireless communication technology which uses the visible white light not just for illumination or signaling purposes but also as a carrier for digital transmission. By enabling wireless communication among e-health instruments\u00a0\u00a0 with the infrastructure, the safety and efficiency communications into hospital can be substantially increased. A main issue is to design a communication system allowing the enhancement of the conditioning signal and avoiding disturbances due to the electromagnetic environment. In this context, the emergence of novel photonic technologies based on III-nitrides semiconductors has open new opportunities to develop innovative systems. Gathering the progress in materials maturity and the advance in manufacturing process, Solid-State Lighting based upon GaN-based light-emitting diodes (LEDs) has emerged as one the dominant technology for indoor\/outdoor lighting as in hospital and transportation. In addition, the opportunity to apply LED for high speed communications is a major innovation in research for the community. We have developed the proper design and the clean room fabrication of micro sized visible LEDs based on InGaN\/GaN multiple quantum wells (MQWs) grown on sapphire substrates [1]. While p-i-n configuration is selected for the design, global experiments have been conducted by reducing the LED dimension (from 300 to 5\u00b5m) in order to minimize the total capacitance, the internal electric \ufb01eld of InGaN MQWs and therefore to increase the LED\u2019s emission ef\ufb01ciency. Optical and electrical characterizations of the fabricated samples have performed to extract the cut-off frequency. Measurements are performed under reverse bias both in the dark and under illumination by a laser source. Experimental results have demonstrated that a frequency bandwidth of 1.5GHz could be attain for a 25\u00b5m size LED structures [2]. This performance constitutes a first success for the implementation of LiFi communications for e-health into hospital of the future.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Andrew, Chen-Yeon CHU Ph.D.\u00a0 Professor, Feng Chia University\u00a0 Director, Institute of Green Products, Feng Chia University\u00a0 Executive Secretary, APEC Research Center for Advanced Biohydrogen Technology Title: Deep Learning Model Established Using Satellite Cloud Images for Short-term Power Generation Forecasting of Solar Power Plants As the demand for solar power generation increases, the forecast of solar…
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