Abstract
Research Background: In the face of climate change challenges and the need for sustainable energy, renewable energy systems are becoming increasingly important. However, to maximize the efficiency and performance of renewable energy systems, monitoring techniques are needed that can provide real-time information about the operational conditions of the system. Research Objectives: This research aims to optimize the performance of renewable energy systems through the application of real-time monitoring techniques. This is done by utilizing data obtained directly from sensors connected to the energy system. Research Methods: The research methods used include literature study, system requirements analysis, real-time monitoring infrastructure design, prototype implementation, and functionality testing. The collected data was analyzed to evaluate the system performance and effectiveness of real-time monitoring techniques. Research Results: The implementation of real-time monitoring techniques successfully improves the performance of renewable energy systems by providing accurate and timely information about operational conditions. This allows for more efficient management and responsiveness to changes in environmental conditions or energy demand. Research Conclusion: The application of real-time monitoring techniques can significantly improve the efficiency and performance of renewable energy systems. With real-time information, better decision-making can be made, enabling more effective management and responsiveness to system and environmental dynamics.
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References
Alma Çall?, B., & Ediz, Ç. (2023). Top concerns of user experiences in Metaverse games: A text-mining based approach. Entertainment Computing, 46, 100576. https://doi.org/10.1016/j.entcom.2023.100576
Arce, J. M. M., & Macabebe, E. Q. B. (2019). Real-Time Power Consumption Monitoring and Forecasting Using Regression Techniques and Machine Learning Algorithms. 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 135–140. https://doi.org/10.1109/IoTaIS47347.2019.8980380
Betlem, K., Kaur, A., Hudson, A. D., Crapnell, R. D., Hurst, G., Singla, P., Zubko, M., Tedesco, S., Banks, C. E., Whitehead, K., & Peeters, M. (2019). Heat-Transfer Method: A Thermal Analysis Technique for the Real-Time Monitoring of Staphylococcus aureus Growth in Buffered Solutions and Digestate Samples. ACS Applied Bio Materials, 2(9), 3790–3798. https://doi.org/10.1021/acsabm.9b00409
Cai, W., Wang, J., Jiang, P., Cao, L., Mi, G., & Zhou, Q. (2020). Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature. Journal of Manufacturing Systems, 57, 1–18. https://doi.org/10.1016/j.jmsy.2020.07.021
Cannas, B., Carcangiu, S., Carta, D., Fanni, A., Muscas, C., Sias, G., Canetto, B., Fresi, L., & Porcu, P. (2021). NILM techniques applied to a real-time monitoring system of the electricity consumption. ACTA IMEKO, 10(2), 139. https://doi.org/10.21014/acta_imeko.v10i2.1054
Chen, M., Li, C. B., Han, Z., & Lee, J. (2023). A simulation technique for monitoring the real-time stress responses of various mooring configurations for offshore floating wind turbines. Ocean Engineering, 278, 114366. https://doi.org/10.1016/j.oceaneng.2023.114366
Clarkson, L., & Williams, D. (2021). Catalogue of real-time instrumentation and monitoring techniques for tailings dams. Mining Technology, 130(1), 52–59. https://doi.org/10.1080/25726668.2021.1874094
Dattoma, V., Nobile, R., Panella, F. W., & Saponaro, A. (2019). Real-time monitoring of damage evolution by nonlinear ultrasonic technique. Procedia Structural Integrity, 24, 583–592. https://doi.org/10.1016/j.prostr.2020.02.051
Dhahak, A., Grimmer, C., Neumann, A., Rüger, C., Sklorz, M., Streibel, T., Zimmermann, R., Mauviel, G., & Burkle-Vitzthum, V. (2020). Real time monitoring of slow pyrolysis of polyethylene terephthalate (PET) by different mass spectrometric techniques. Waste Management, 106, 226–239. https://doi.org/10.1016/j.wasman.2020.03.028
Dong, H., & Liu, Y. (2023). Metaverse Meets Consumer Electronics. IEEE Consumer Electronics Magazine, 12(3), 17–19. https://doi.org/10.1109/MCE.2022.3229180
Dube, B. (2020). Rural online learning in the context of COVID 19 in South Africa: Evoking an inclusive education approach. Multidisciplinary Journal of Educational Research, 10(2), 135. https://doi.org/10.17583/remie.2020.5607
Dubey, J. P. (2021). Toxoplasmosis of Animals and Humans (3rd ed.). CRC Press. https://doi.org/10.1201/9781003199373
El-Shafeiy, E., Alsabaan, M., Ibrahem, M. I., & Elwahsh, H. (2023). Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique. Sensors, 23(20), 8613. https://doi.org/10.3390/s23208613
Farzana, F., Hossain, Md. M., Imtiaze, M. M., Hossain, Md. T., Jameel, A. S. M. M., & Islam, S. (2020). A Real-Time Motion Based Fuel Monitoring Technique For Vehicle Tracking Systems. 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), 1–6. https://doi.org/10.1109/ETCCE51779.2020.9350860
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
Gosal, A. S., Geijzendorffer, I. R., Václavík, T., Poulin, B., & Ziv, G. (2019). Using social media, machine learning and natural language processing to map multiple recreational beneficiaries. Ecosystem Services, 38, 100958. https://doi.org/10.1016/j.ecoser.2019.100958
Hao, J., & Ho, T. K. (2019). Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. Journal of Educational and Behavioral Statistics, 44(3), 348–361. https://doi.org/10.3102/1076998619832248
Ibrar, I., Naji, O., Sharif, A., Malekizadeh, A., Alhawari, A., Alanezi, A. A., & Altaee, A. (2019). A Review of Fouling Mechanisms, Control Strategies and Real-Time Fouling Monitoring Techniques in Forward Osmosis. Water, 11(4), 695. https://doi.org/10.3390/w11040695
Islam, Md. A., & Volakis, J. L. (2021). Wearable Microwave Imaging Sensor for Deep Tissue Real-Time Monitoring Using a New Loss-Compensated Backpropagation Technique. IEEE Sensors Journal, 21(3), 3324–3334. https://doi.org/10.1109/JSEN.2020.3023482
Jafari, N. H., Li, X., Chen, Q., Le, C.-Y., Betzer, L. P., & Liang, Y. (2021). Real-time water level monitoring using live cameras and computer vision techniques. Computers & Geosciences, 147, 104642. https://doi.org/10.1016/j.cageo.2020.104642
Kar, P., Chen, R., & Qian, Y. (2022). An efficient producer mobility management technique for real-time communication in NDN-based Remote Health Monitoring systems. Smart Health, 26, 100309. https://doi.org/10.1016/j.smhl.2022.100309
Kazemian, A., & Khoshnevis, B. (2021). Real-time extrusion quality monitoring techniques for construction 3D printing. Construction and Building Materials, 303, 124520. https://doi.org/10.1016/j.conbuildmat.2021.124520
Khel, M. H. K., Kadir, K., Albattah, W., Khan, S., Noor, M., Nasir, H., Habib, S., Islam, M., & Khan, A. (2021). Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique. Emerging Science Journal, 5, 182–196. https://doi.org/10.28991/esj-2021-SPER-14
Kundu, T., Pal, A., Roy, P., Datta, A. K., & Topdar, P. (2024). Development of a novel real-time AE source localisation technique using ANN for health monitoring of rail section: An experimental study. Structural Health Monitoring, 23(1), 479–494. https://doi.org/10.1177/14759217231171026
Majumder, P., Majumder, M., Saha, A. K., Sarkar, K., & Nath, S. (2019). Real time reliability monitoring of hydro?power plant by combined cognitive decision?making technique. International Journal of Energy Research, 43(9), 4912–4939. https://doi.org/10.1002/er.4530
Moayedi, H., Nazir, R., Gör, M., Anuar Kassim, K., & Kok Foong, L. (2020). A new real-time monitoring technique in calculation of the p-y curve of single thin steel piles considering the influence of driven energy and using strain gauge sensors. Measurement, 153, 107365. https://doi.org/10.1016/j.measurement.2019.107365
Nagarathna, S. B., Gehlot, A., Tiwari, M., Tiwari, T., Chakravarthi, M. K., & Verma, D. (2022). A Review of Bio-Cell Culture Processes in Real-Time Monitoring Approach with Cloud Computing Techniques. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 618–621. https://doi.org/10.1109/ICACITE53722.2022.9823543
Nie, H., Yang, Z., Dang, W., Chen, Q., Li, P., Li, D., & Wang, R. (2020). Study of Shale Gas Release from Freshly Drilled Core Samples Using a Real-Time Canister Monitoring Technique: Release Kinetics, Influencing Factors, and Upscaling. Energy & Fuels, 34(3), 2916–2924. https://doi.org/10.1021/acs.energyfuels.9b04122
Pardo, A., Jovanovic, J., Dawson, S., Gaševi?, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128–138. https://doi.org/10.1111/bjet.12592
Park, S., Kim, S., Park, J., & Cho, K. H. (2020). Real-time monitoring the spatial distribution of organic fouling using fluorescence imaging technique. Journal of Membrane Science, 597, 117778. https://doi.org/10.1016/j.memsci.2019.117778
Payal, R., Sharma, N., & Dwivedi, Y. K. (2024). Unlocking the impact of brand engagement in the metaverse on Real-World purchase intentions: Analyzing Pre-Adoption behavior in a futuristic technology platform. Electronic Commerce Research and Applications, 65, 101381. https://doi.org/10.1016/j.elerap.2024.101381
Rudolph, G., Virtanen, T., Ferrando, M., Güell, C., Lipnizki, F., & Kallioinen, M. (2019). A review of in situ real-time monitoring techniques for membrane fouling in the biotechnology, biorefinery and food sectors. Journal of Membrane Science, 588, 117221. https://doi.org/10.1016/j.memsci.2019.117221
Sayyad, S., Parmar, S., Jadhav, M., & Khadayate, K. (2020). Real-Time Garbage, Potholes and Manholes Monitoring System using Deep Learning Techniques. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 826–831. https://doi.org/10.1109/ICOEI48184.2020.9143030
Selwyn, N. (2019). What’s the Problem with Learning Analytics? Journal of Learning Analytics, 6(3). https://doi.org/10.18608/jla.2019.63.3
Shadiev, R., & Yang, M. (2020). Review of Studies on Technology-Enhanced Language Learning and Teaching. Sustainability, 12(2), 524. https://doi.org/10.3390/su12020524
Spernjak, A. (2021). Using ICT to Teach Effectively at COVID-19. 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 617–620. https://doi.org/10.23919/MIPRO52101.2021.9596878
Weidner, L., Hemmler, D., Rychlik, M., & Schmitt-Kopplin, P. (2023). Real-Time Monitoring of Miniaturized Thermal Food Processing by Advanced Mass Spectrometric Techniques. Analytical Chemistry, acs.analchem.2c04874. https://doi.org/10.1021/acs.analchem.2c04874
Wu, F., Wu, T., & Yuce, M. (2018). An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications. Sensors, 19(1), 21. https://doi.org/10.3390/s19010021
Yang, L., Dai, M., Cao, Q., Ding, S., Zhao, Z., Cao, X., Wen, Z., Wang, H., Xie, M., & Fu, F. (2021). Real-time monitoring hypoxia at high altitudes using electrical bioimpedance technique: An animal experiment. Journal of Applied Physiology, 130(4), 952–963. https://doi.org/10.1152/japplphysiol.00712.2020
Zhang, Q., Jindapetch, N., & Buranapanichkit, D. (2019). Investigation of Image Edge Detection Techniques Based Flood Monitoring in Real-time. 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 927–930. https://doi.org/10.1109/ECTI-CON47248.2019.8955273
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