Enhancing Electrical Power Demand Prediction Using LSTM-Based Deep Learning Models for Local Energy Communities

References

• O. Izquierdo-Monge, P. Peña-Carro, R. Villafafila-Robles, O. Duque-Perez, A. Zorita-Lamadrid, and L. Hernandez-Callejo, “Conversion of a network section with loads, storage systems and renewable generation sources into a smart microgrid,” Appl. Sci., vol. 11, no. 11, pp. 5012, 2021. DOI: 10.3390/app11115012.
   Google Scholar
• D. Liu, J. Xiao, J. Liu, X. Yuan, and S. Zhang, “Dynamic energy trading and load scheduling algorithm for the end-user in smart grid,” IEEE Access, vol. 8, pp. 189632–189645, 2020. DOI: 10.1109/ACCESS.2020.3031325.
   Google Scholar
• D. Chippada, “Mathematical modeling and simulation of energy management in smart grid,” Int. J. Smart Grid Clean Energy, vol. 9, no. 4, pp. 746–755, 2020. DOI: 10.12720/sgce.9.4.746-755.
   Google Scholar
• M. Abdelsattar, I. Hamdan, A. Mesalam, and A. Fawzi, “An overview of smart grid technology integration with hybrid energy systems based on demand response,” International Middle East Power Systems Conference, pp. 1–6. DOI: 10.1109/MEPCON55441.2022.10021687.
   Google Scholar
• M. Orlando et al., “A smart meter infrastructure for smart grid IoT applications,” IEEE Internet Things J., vol. 9, no. 14, pp. 12529–12541, 2022. DOI: 10.1109/JIOT.2021.3137596.
   Web of Science ®Google Scholar
• A. Chakir, M. Tabaa, F. Moutaouakkil, H. Medromi, and K. Alami, “Smart multi-level energy management algorithm for grid-connected hybrid renewable energy systems in a micro-grid context,” J. Renew. Sustain. Energy, vol. 12, no. 5, pp. 055301, 2022. DOI: 10.1063/5.0015639.
   Web of Science ®Google Scholar
• B. Kumar, A. Bihari, and S. Khan, “Analysis of 13 nodes test feeder integrated with renewable energy sources and electrical vehicle,” International Conference on Computing, Power and Communication Technologies, pp. 1–5, 2021. DOI: 10.1109/GUCON50781.2021.9573673.
   Google Scholar
• S. Karimulla and K. Ravi, “Integration of Renewable Energy Sources into the Smart Grid Using Enhanced SCA,” Intell. Automat. Soft Comput., vol. 32, no. 3, pp. 1557–1572, 2022. DOI: 10.32604/iasc.2022.022953.
   Web of Science ®Google Scholar
• P. H. Femy and J. Jayakumar, “A review on the feasibility of deployment of renewable energy sources for electric vehicles under smart grid environment,” Int. J. Electr. Electron. Res., vol. 9, no. 3, pp. 57–65, 2021.
   Google Scholar
• P. Chaudhary, “Demand response for energy-efficient and optimal integration of renewable energy sources in a smart grid,” Smart Energy Manag. Syst. Renew. Energy Res., 2021. DOI: 10.1063/9780735422827_005.
   Google Scholar
• C. Ramesh Kumar and R. K. Sharma, “Renewable energy systems with improved power quality in smart grid using fuzzy logic method,” International Conference for Innovation in Technology, pp. 1–7, 2023. DOI: 10.1109/INOCON57975.2023.10101322.
   Google Scholar
• R. Satish, P. Kantarao, and K. Vaisakh, “A new algorithm for harmonic impacts with renewable DG and non-linear loads in smart distribution networks,” Technol. Econom. Smart Grids Sustain. Energy, vol. 7, pp. 1–19, 2022. DOI: 10.1007/s40866-022-00134-1.
   Google Scholar
• X. Chen, C. Miller, M. Goutham, P. Hanumalagutti, R. Blaser, and S. Stockar, “Development and evaluation of an online home energy management strategy for load coordination in smart homes with renewable energy sources,” Energy, vol. 290, pp. 130134, 2023. DOI: 10.1016/j.energy.2023.130134.
   Web of Science ®Google Scholar
• F. L. Franco, M. Ricco, R. Mandrioli, R. F. Paternost, and G. Grandi, “State of charge optimization-based smart charging of aggregate electric vehicles from distributed renewable energy sources,” International Conference on Compatibility, Power Electronics and Power Engineering, pp. 1–6, 2021. DOI: 10.1109/CPE-POWERENG50821.2021.9501214.
   Google Scholar
• A. M. Eltamaly, M. A. Alotaibi, W. A. Elsheikh, A. I. Alolah, and M. A. Ahmed, “Novel demand side-management strategy for smart grid concepts applications in hybrid renewable energy systems,” International Youth Conference on Radio Electronics, Electrical and Power Engineering, pp. 1–7, 2022. DOI: 10.1109/REEPE53907.2022.9731431.
   Google Scholar
• P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar, and R. Ayyasamy, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electr. Power Comp. Syst., vol. 52, no. 2, pp. 219–232, 2023. DOI: 10.1080/15325008.2023.2217175.
   Web of Science ®Google Scholar
• H. Shekhar et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electr. Power Comp. Syst., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
   Web of Science ®Google Scholar
• D. Sarkar and P. K. Sadhu, “A novel fixed BIPV array for improving maximum power with low mismatch losses under partial shading,” IETE J. Res., pp. 1–21, 2022. DOI: 10.1080/03772063.2022.2060871.
   Web of Science ®Google Scholar
• D. Sarkar and P. K. Sadhu, “A new hybrid BIPV array for enhancing maximum power with reduced mismatch losses under extreme partial shading scenarios,” Energ. Sour. Recov. Utiliz. Environ. Effects, vol. 44, no. 2, pp. 5172–5198, 2022. DOI: 10.1080/15567036.2022.2083271.
   Google Scholar
• D. Sarkar and P. K. Sadhu, “Critical comprehensive performance analysis of static BIPV array configurations to reduce mismatch loss and enhance maximum power under partial shading,” IETE Tech. Rev., vol. 40, no. 4, pp. 465–497, 2023. DOI: 10.1080/02564602.2022.2127944.
   Web of Science ®Google Scholar
• D. Sarkar and P. K. Sadhu, “GMPP improvement with fewer power peaks and lower mismatch losses using a new hybrid BIPV array configuration,” International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies, pp. 1–6, 2023. DOI: 10.1109/ICEPE57949.2023.10201617.
   Google Scholar
• D. Sarkar and P. K. Sadhu, “Power enhancement by hybrid BIPV arrays with fewer peaks and reduced mismatch lossess under partial shading,” International Conference on Intelligent Technologies, pp. 1–6, 2023. DOI: 10.1109/CONIT59222.2023.10205941.
   Google Scholar
• S. Bhattacharya, P. K. Sadhu, and D. Sarkar, “Performance evaluation of building integrated photovoltaic system arrays (SP, TT, QT, and TCT) to improve maximum power with low mismatch loss under partial shading,” Microsyst. Technol., pp. 1–15, 2023. DOI: 10.1007/s00542-023-05564-0.
   PubMed Web of Science ®Google Scholar