Е-Публикации
Технически университет - София

Abstract: An analysis of the possibilities of using alternative energy to solve the problem of electricity shortages in developing countries shows that solar energy can potentially play an essential role in the fuel and energy complex. The geographical location, on the one hand, and the global development of solar energy technologies, on the other, create an opportunity for a fairly complete and rapid solution to problems of insufficient energy supply. An autonomous solar installation is expensive; 50% of the cost is solar modules, 45% of the cost consists of other elements (battery, inverter, charge controller), and 5% is for other materials. This work proposes the most efficient PV system, based on the technical characteristics of the SB and AB. It has a direct connection between the SB and AB and provides almost full use of the solar panel’s installed power with a variable orientation to the Sun. The development of a small solar photovoltaic (PV) installation, operating both in parallel with the grid and in autonomous mode, can improve the power supply of household consumers more efficiently and faster than the development of a large energy system. It is suggested that two minimized criteria be used to create a model for forecasting FOU. This model can be used with a genetic algorithm to make a prediction that fits a specific case, such as a time series representation based on discrete fuzzy sets of the second type. The goal is to make decisions that are more valid and useful by creating a forecast model and algorithms for analyzing small PV indicators whose current values are shown by short time series and automating the processes needed for forecasting and analysis.

References

1. Stock R. Power for the Plantationocene: Solar parks as the colonial form of an energy plantation J. Peasant Stud. 2023 50 162 184 10.1080/03066150.2022.2120812
2. Maxmut O. Renewable energy sources: Advancements, challenges, and prospects Int. J. Adv. Sci. Res. 2023 3 14 25
3. Metodieva I. Bozhkov S. The Field Intensity Study of a Three-Phase AC Power Line Proceedings of the 13th Electrical Engineering Faculty Conference (BulEF) Varna, Bulgaria 8–11 September 2021 1 3 10.1109/BulEF53491.2021.9690806
4. Metodieva I. Bozhkov S. Neural Network a Way to Forecast the Generation of Electricity from a Wind Turbine Proceedings of the 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE) Ruse, Bulgaria 30 June–2 July 2022 978-1-6654-0709-0
5. Pratt B. Vries J. Where is knowledge from the global South? An account of epistemic justice for a global bioethics J. Med. Ethics 2023 49 325 334 10.1136/jme-2022-108291 36657964
6. Randolph G. Storper M. Is urbanisation in the Global South fundamentally different? Comparative global urban analysis for the 21st century Urban Stud. 2023 60 3 25 10.1177/00420980211067926
7. Franco A. Shaker M. Kalubi D. Hostettler S. A review of sustainable energy access and technologies for healthcare facilities in the Global South Sustain Energy Technol. Assess. 2017 22 92 105
8. Nie X. Daud W. Pu J. A novel transactive integration system for solar renewable energy into smart homes and landscape design: A digital twin simulation case study Sol. Energy 2024 262 111871 10.1016/j.solener.2023.111871
9. Padilla C. Moyano L. Germán C. Espín S. Humanante D. Lozano I. Solar-powered vehicles: Harnessing sustainable energy for transportation Ann. For. Res. 2024 66 4362 4372
10. Zaheb H. Amiry H. Ahmadi M. Fedayi H. Amiry S. Yona A. Maximizing annual energy yield in a grid-connected PV solar power plant: Analysis of seasonal tilt angle and solar tracking strategies Sustainability 2023 15 11053 10.3390/su151411053
11. Ni Z. Yu H. BasisFormer: Attention-Based Time Series Forecasting with Learnable and Interpretable Basis Adv. Neural Inf. Process. Syst. 2024 36 71222 71241
12. Zhang Y. Ma L. Pal S. Zhang Y. Coates M. Multi-Resolution Time-Series Transformer for Long-Term Forecasting Proceedings of the 27th International Conference on Artificial Intelligence and Statistics Valencia, Spain 2–4 May 2024 4222 4230
13. Fan W. Wang P. Wang D. Wang D. Zhou Y. Fu Y. Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting Proc. AAAI Conf. Artif. Intell. 2023 37 7522 7529 10.1609/aaai.v37i6.25914
14. Li Z. Li S. Yan X. Time Series as Images: Vision Transformer for Irregularly Sampled Time Series Adv. Neural Inf. Process. Syst. 2023 36 49187 49204
15. Chang P. Li H. Quan S.F. Lu S. Wung S.-F. Roveda J. Li A. A Transformer-Based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unit Comput. Methods Programs Biomed. 2024 246 108060 10.1016/j.cmpb.2024.108060 38350189
16. Golnary F. Moradi H. Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed Appl. Math. Model. 2019 65 566 585 10.1016/j.apm.2018.08.030
17. Shi L. Zhengrong X. Sampled-data decentralized output feedback control for a class of switched large-scale stochastic nonlinear systems IEEE Syst. J. 2020 14 1602 1610
18. Afaghi A. Ghaemi S. Ghiasi A. Badamchizadeh M.A. Adaptive fuzzy observer-based cooperative control of unknown fractional-order multi-agent systems with uncertain dynamics Soft Comput. 2020 24 3737 3752 10.1007/s00500-019-04142-9
19. Miller D. Shahab M. Adaptive tracking with exponential stability and convolution bounds using vigilant estimation Math. Control Signals Syst. 2020 32 241 291 10.1007/s00498-020-00255-x
20. Hyndman R. Athanasopoulos G. Forecasting: Principles Andpractice OTexts Melbourne, Australia 2018
21. Wen J. Zhang S. Yang Q. Learning in the Context of Noisy Time Series Data IEEE Trans. Knowl. Data Eng. 2022 34 2718 2731
22. Malik A. A Comprehensive Review of Adaptive Sampling Methods for Real-Time Data Analytics J. Big Data 2022 9 1
23. Brownlee J. Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict the Future MachineLearning Mastery San Juan, Puerto Rico 2017
24. Sun C. Zhou H. Chen L. Improved differential Evolutional Algorithms IEEE Int. Conf. Comput. Sci. Autom. Eng. 2012 3 142 146
25. Li L. Zhou R. Zhan Z. Zhang J. A primary Theoretical Study on Decomposition Based Multiobjective Evolutionary Algorithm IEEE Trans. Evol. Comput. 2015 20 563 576 10.1109/TEVC.2015.2501315
26. Daneva M. Optimizing the Quality of Education of Students Studying Engineering and Pedagogy Proceedings of the 59th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) Sozopol, Bulgaria 1–3 July 2024 10.1109/ICEST62335.2024.10639686
27. Zeng Y. Sun Y. Comparison of Multiobjective Particle Swarm Optimization and Evolutionary Algorithms for Optimal Reactive Power Dispatch Problem Proceedings of the IEEE Congress on Evolutionary Computation (CEC) Beijing, China 6–11 July 2014
28. Fan H. Zhu Z. Shao L. Multivariate Time Series Forecast-ing with Structured State Space Models IEEE Trans. Neural Netw. Learn. Syst. 2022 33 1891 1903
29. Hort’ua H. Mora-Valencia A. Forecasting VIX Using Bayesian Deep Learning Papers with Code London, UK 2024
30. Ocana E. Souza F. Time Series Forecasting with Deep Learning: A Survey arXiv 2020 2004.13408
31. Salinas J. Flunkert V. Gasthaus J. Januschowski T. DeepAR:Probabilistic Forecasting with Autoregressive Recurrent Networks Int. J. Forecast. 2020 36 1181 1191 10.1016/j.ijforecast.2019.07.001
32. Oreshkin B. Carpov D. Chapados N. Bengio Y. N-BEATS:Neural Basis Expansion Analysis for Time Series Forecasting Proceedings of the International Conference on Learning Representations Addis Ababa, Ethiopia 30 April 2020
33. Song Q. Chissom B.S. Fuzzy time series and its models Fuzzy Sets Syst. 2023 54 269 277 10.1016/0165-0114(93)90372-O
34. Khishe M. Salih Mohammed A. Mohammadi H. Mohammadi M. The optimization of nodes clustering and multi-hop routing protocol using hierarchical chimp optimization for sustainable energy efficient underwater wireless sensor networks Wirel. Netw. 2024 30 233 252
35. Krishna K.P.R. Thirumuru R. Energy Efficient and Multi-Hop Routing for Constrained Wireless Sensor Networks Sustain. Comput. Inform. Syst. 2023 38 100866 10.1016/j.suscom.2023.100866
36. Liu X. Wang W. Deep Time Series Forecasting Models: A Comprehensive Survey Mathematics 2024 12 1504 10.3390/math12101504
37. Zérah M. Das S. Solar rooftop systems and the urban transition: Shall the twain ever meet? interrogations from Rewari, India J. Urban Technol. 2023 30 103 125 10.1080/10630732.2023.2172305
38. Ali M. Alqahtani A. Jones M. Xie X. Clustering and classification for time series data in visual analytics: A survey IEEE Access 2019 7 181314 181338 10.1109/ACCESS.2019.2958551
39. Du M. Ding S. Xue Y. Shi Z. A novel density peaks clustering with sensitivity of local density and density-adaptive metric Knowl. Inf. Syst. 2019 59 285 309 10.1007/s10115-018-1189-7
40. Gupta K. Chatterjee N. Financial time series clustering Inf. Commun. Technol. Intell. Syst. 2017 2 146 156
41. Kim T.-Y. Cho S.-B. Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption Proceedings of the IEEE Congress on Evolutionary Computation Wellington, New Zealand 10–13 June 2019 1510 1516
42. Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption Available online: https://www.researchgate.net/publication/344813395_Time_Series_Forecasting_with_Multi-Headed_Attention-Based_Deep_Learning_for_Residential_Energy_Consumption (accessed on 17 March 2025)
43. Johnpaul C. Prasad M. Nickolas S. Gangadharan G. Trendlets: A novel probabilistic representational structures for clustering the time series data Expert Syst. Appl. 2020 145 113119
44. Karnik N. Mendel J. Type-2 fuzzy logic systems IEEE Trans. Fuzzy Syst. 1999 7 643 658 10.1109/91.811231
45. Karnik N. Mendel J. Centroid of a type-2 fuzzy set systems Inf. Sci. 2001 132 195 220 10.1016/S0020-0255(01)00069-X
46. Mendel J. Type-2 fuzzy sets and systems: An overview IEEE Comput. Intell. Mag. 2007 2 20 29 10.1109/MCI.2007.380672
47. Deb K. Jain H. An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints IEEE Trans. Evol. Comput. 2014 18 577 601 10.1109/TEVC.2013.2281535
48. Deb K. Pratap A. Agarwal S. Meyarivan T. Fast and Elitist Multiobjective Genetic Algorithm: NSGA II KanGAL Report No. 200001 Indian Institute of Technology Kanpur, India 2000 182 197
49. Gath I. Geva A. Unsupervised optimal fuzzy clustering IEEE Trans. Pattern Anal. Mach. Intell. 1989 11 773 781 10.1109/34.192473
50. Bezdek J. Numerical Taxonomy with Fuzzy Sets J. Math. Biol. 1994 1 57 71 10.1007/BF02339490
51. Das M. Sitaram D. Dalwani A. Narang A. Auradkar P. A Measure of Similarity of Time Series Containing Missing Data Using the Mahalanobis Distance Proceedings of the Second International Conference on Advances in Computing and Communication Engineering Dehradun, India 1–2 May 2015 622 627
52. Gannavaram V. Gajula A. Sun tracking solar energy system-an alternate and emerging energy Unit in 21st century Proceedings of the 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) Greater Noida, India 12–13 May 2023 29 33
53. Lughofer E. Zavoianu A. Pollak R. Pratama M. Meyer-Heye P. Zörrer H. Eitzinger C. Radauer T. Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models J. Process Control 2019 76 27 45 10.1016/j.jprocont.2019.02.005

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