Analysis of Neural Networks Used by Artificial Intelligence in the Energy Transition with Renewable Energies
Source abstract
Abstract
Artificial neural networks (ANNs) have become key methods for achieving global climate goals. The aim of this review is to carry out a detailed analysis of the applications of ANNs to the energy transition all over the world. Thus, the applications of ANNs to renewable energies such as solar, wind, and tidal energy or for the prediction of greenhouse gas emissions were studied. This review was conducted through keyword searches and research of publishers and research platforms such as Science Direct, Research Gate, Google Scholar, IEEE Xplore, Taylor and Francis, and MDPI. The dates of the most recent research were 2018 for wind energy, 2022 for solar energy, 2021 for tidal energy, and 2021 for the prediction of greenhouse gas emissions. The results obtained were classified according to the type of structure and the architecture used, the inputs/outputs used, the region studied, the activation function used, and the algorithms used as the main methods for synthesizing the results. To carry out the present review, 96 investigations were used, and among them, the predominant structure was that of the multilayer perceptron, with Purelin and Sigmoid as the most used activation functions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Solar Radiation and Photovoltaics · Wind Energy Research and Development
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Energy, University of Oviedo, 33203 Gijon, Spain
Department of Building Physics and Construction, University of Bielefeld, 32427 Minden, Germany
References (100+)
Show source-provided references
1. The role of artificial intelligence in photo-voltaic systems design and control: A review10.1016/j.rser.2017.04.046 ↗
2. Application of artificial neural network for predicting performance of solid desiccant cooling systems—A review10.1016/j.rser.2017.05.169 ↗
3. A logical calculus of the ideas immanent in nervous activity10.1007/BF02478259 ↗
4. The first stage of perception: Growth of the assembly
5. Artificial intelligence in medical practice: The question to the answer?10.1016/j.amjmed.2017.10.035 ↗
6. Widrow, B., and Hoff, M.E. (1960). IRE WESCON Convention Record, Institute of Radio Engineers.
7. Widrow, B. (2022). Cybernetics 2.0: A General Theory of Adaptivity and Homeostasis in the Brain and in the Body, Springer International Publishing.10.1007/978-3-030-98140-2 ↗
8. Minsky, M., and Papert, S. (1969). Perceptrons. An Introduction to Computational Geometry, The MIT Press Ltd.
9. A sociological history of the neural network controversy10.1016/S0065-2458(08)60408-8 ↗
10. Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysts in the Behavioral Sciences. [Ph.D. Thesis, Harvard University].
11. Generalization of backpropagation with application to a recurrent gas market model10.1016/0893-6080(88)90007-X ↗
12. Gholamalinezhad, H., and Khosravi, H. (2020). Pooling methods in deep neural networks, a review. arXiv.
13. Zhu, X., and Goldberg, A.B. (2022). Introduction to Semi-Supervised Learning, Springer Nature.
14. Deep learning, reinforcement learning, and world models10.1016/j.neunet.2022.03.037 ↗
15. A review of conceptual clustering algorithms10.1007/s10462-018-9627-1 ↗
16. Dang, W., Guo, J., Liu, M., Liu, S., Yang, B., Yin, L., and Zheng, W. (2022). A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell. Appl. Sci., 12.10.3390/app12189213 ↗
17. How to measure uncertainty in uncertainty sampling for active learning10.1007/s10994-021-06003-9 ↗
18. Transfer learning in demand response: A review of algorithms for data-efficient modelling and control10.1016/j.egyai.2021.100126 ↗
19. A review on extreme learning machine10.1007/s11042-021-11007-7 ↗
20. Data collection and quality challenges in deep learning: A data-centric AI perspective10.1007/s00778-022-00775-9 ↗
21. Efficient processing of deep neural networks: A tutorial and survey10.1109/JPROC.2017.2761740 ↗
22. A survey on data collection for machine learning: A big data—AI integration perspective10.1109/TKDE.2019.2946162 ↗
23. Su, X., Zhao, Y., and Bethard, S. (2022, January 22–27). A comparison of strategies for source-free domain adaptation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.10.18653/v1/2022.acl-long.572 ↗
24. State-of-the-art in artificial neural network applications: A survey10.1016/j.heliyon.2018.e00938 ↗
25. Evaluating public transport efficiency with neural network models10.1016/S0968-090X(97)00017-X ↗
26. Literature review: Efficient deep neural networks techniques for medical image analysis10.1007/s00521-022-06960-9 ↗
27. Applying Artificial Neural Networks for Face Recognition10.1155/2011/673016 ↗
28. The measurement of technical efficiency: A neural network approach10.1080/0003684042000217661 ↗
29. Predicting flotation efficiency using neural networks10.1016/j.cep.2006.06.011 ↗
30. Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review10.1016/j.rser.2020.110512 ↗
31. Advances of soft computing methods in edge detection
32. Modeling roadway traffic noise in a hot climate using artificial neural networks10.1016/j.trd.2017.04.014 ↗
33. Artificial neural networks for controlling wind–PV power systems: A review10.1016/j.rser.2013.08.070 ↗
34. Rojas, R. (2013). Neural Networks: A Systematic Introduction, Springer Science Y Business Media.
35. Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review10.1016/j.rser.2011.10.015 ↗
36. Methods to improve prediction performance of ANN models10.1016/S1569-190X(03)00044-3 ↗
37. Artificial Neural Networks (ANNs): A New Paradigm for Thermal Science and Engineering10.1115/1.2944238 ↗
38. Poznyak, T.I., Oria, I.C., and Poznyak, A.S. (2019). Ozonation and Biodegradation in Environmental Engineering, Elsevier.10.1016/B978-0-12-812847-3.00023-8 ↗
39. The perceptron: A probabilistic model for information storage and organization in the brain10.1037/h0042519 ↗
40. Poznyak, A.S., Sanchez, E.N., and Yu, W. (2001). Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking, World Scientific.10.1142/4703 ↗
41. Hecht-Nielsen, R. (1992). Neural Networks for Perception, Academic Press.
42. Modeling of solar energy systems using artificial neural network: A comprehensive review10.1016/j.solener.2019.01.037 ↗
43. Forecasting Beijing transportation hub areas’s pedestrian flow using modular neural network10.1155/2015/749181 ↗
44. AWNN-assisted wind power forecasting using feed-forward neural network10.1109/TSTE.2011.2182215 ↗
45. Sensorless illumination control of a networked LED-lighting system using feddforward neural network10.1109/TIE.2013.2266084 ↗
46. Suykens, J., Vandewalle, J., and Moor, B. (1995). Artificial Neural Networks for Modelling and Control of Non-Linear Systems, Springer Science Y Business Media.10.1007/978-1-4757-2493-6 ↗
47. Decision tree based occlusion detection in face recognition and estimation of human age using back propagation neural network10.3844/jcssp.2014.115.127 ↗
48. Metrics and methods for robustness evaluation of neural networks with generative models10.1007/s10994-021-05994-9 ↗
49. Levy, N., and Katz, G. (2022, January 22–26). Roma: A method for neural network robustness measurement and assessment. Proceedings of the International Conference on Neural Information Processing, Virtual.10.1007/978-981-99-1639-9_8 ↗
50. On the Performance Robustness of Artificial Neural Network Approaches and Gumbel Extreme Value Distribution for Prediction of Wind Speed
51. Savva, A.G., Theocharides, T., and Nicopoulos, C. (2023). Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes. Algorithms, 16.10.3390/a16070322 ↗
52. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda10.1016/j.ijinfomgt.2020.102104 ↗
53. Shaping the future of sustainable energy through AI-enabled circular economy policies
54. Camilleri, M.A. (2023). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Syst., e13406.10.1111/exsy.13406 ↗
55. Roberts, H., Zhang, J., Bariach, B., Cowls, J., Gilburt, B., Juneja, P., Tsamados, A., Ziosi, M., Taddeo, M., and Floridi, L. (2022). Artificial intelligence in support of the circular economy: Ethical considerations and a path forward. AI Soc., 1–14.10.1007/s00146-022-01596-8 ↗
56. Crane, A., McWilliams, A., Matten, D., Moon, J., and Siegel, D.S. (2008). The Oxford Handbook of Corporate Social Responsibility, OUP Oxford.10.1093/oxfordhb/9780199211593.001.0001 ↗
57. The impact of artificial intelligence on human society and bioethics10.4103/tcmj.tcmj_71_20 ↗
58. Collaborative intelligence: Humans and AI are joining forces
59. Eurostat (2023, December 27). Eurostat Statics Explained. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Renewable_energy_statistics#Wind_and_water_provide_most_renewable_electricity.3B_solar_is_the_fastest-growing_energy_source.
60. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran10.1016/j.enconman.2016.02.041 ↗
61. Analysis of wind power generation and prediction using ANN: A case study10.1016/j.renene.2007.06.013 ↗
62. A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks10.1016/j.renene.2004.05.008 ↗
63. Application of artificial neural networks for the wind speed prediction of target using reference stations data10.1016/j.renene.2006.12.001 ↗
64. A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural netowork and GSA evolutionary algorithm10.1016/j.rser.2015.07.034 ↗
65. Support vector machines for wind speed prediction10.1016/j.renene.2003.11.009 ↗
66. A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada10.1016/j.rser.2013.06.022 ↗
67. Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Netoworks10.1016/j.renene.2018.02.081 ↗
68. On comparing three artificial neural networks for wind speed forecasting10.1016/j.apenergy.2009.12.013 ↗
69. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning10.1016/j.renene.2010.08.026 ↗
70. A hybrid statistical method to predict wind speed and wind power10.1016/j.renene.2009.12.011 ↗
71. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction10.1016/j.renene.2008.10.017 ↗
72. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks10.1016/j.renene.2008.03.014 ↗
73. Application of a control algorithm for wind speed prediction and active power generation10.1016/j.renene.2004.07.015 ↗
74. A new strategy for wind speed forecasting using artificial intelligent methods10.1016/j.renene.2008.04.017 ↗
75. Wind energy prediction using a two-hidden layer neural network10.1016/j.cnsns.2009.10.005 ↗
76. Wind speed prediction in the mountainous region of India using an artificial neural network model10.1016/j.renene.2015.02.034 ↗
77. The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria10.1016/j.apenergy.2009.09.005 ↗
78. Wind speed prediction using artificial neural networks
79. Kalogirou, S., Neocleous, C., Pashiardis, S., and Schizas, C. (1999, January 3–4). Wind speed prediction using artificial neural networks. Proceedings of the European Symposium on Intelligent Techniques, Crete, Greece.
80. Artificial neural network approach to spatial estimation of wind velocity data10.1016/j.enconman.2005.05.009 ↗
81. Relative importance of parameters affecting wind speed prediction using artificial neural networks10.1007/s00704-012-0821-9 ↗
82. A case study on a hybrid wind speed forecasting method using BP neural network10.1016/j.knosys.2011.04.019 ↗
83. Lodge, A., and Yu, X. (2014, January 26–28). Short term wind speed prediction using artificial neural networks. Proceedings of the 4th IEEE International Conference on Information Science and Technology, Shenzhen, China.10.1109/ICIST.2014.6920535 ↗
84. Le, X.C., Duong, M.Q., and Le, K.H. (2022). Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems. Energies, 16.10.3390/en16010402 ↗
85. Artificial intelligence techniques for sizing photovoltaic systems: A review10.1016/j.rser.2008.01.006 ↗
86. Artificial intelligence techniques for photovoltaic applications: A review10.1016/j.pecs.2008.01.001 ↗
87. Application of ANN technique to predict the performance of solar collector systems—A review10.1016/j.rser.2018.01.001 ↗
88. Solar radiation prediction using Artificial Neural Network techniques: A review10.1016/j.rser.2013.08.055 ↗
89. Online 24-h solar power forecasting based on weather type classification using artificial neural network10.1016/j.solener.2011.08.027 ↗
90. Estimation of the energy of a PV generator using artificial neural network10.1016/j.renene.2009.05.020 ↗
91. Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation10.1016/j.energy.2010.10.032 ↗
92. Forecasting of preprocessed daily solar radiation time series using neural networks10.1016/j.solener.2010.08.011 ↗
93. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy10.1016/j.solener.2010.02.006 ↗
94. Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia10.1016/j.energy.2010.05.024 ↗
95. Use of artificial neural networks for mapping of solar potential in Turkey10.1016/S0306-2619(03)00137-5 ↗
96. Artificial neural network analysis of Moroccan solar potential10.1016/j.rser.2012.03.071 ↗
97. Estimation of Diffuse Fraction of Global Solar Radiation Using Artificial Neural Networks
98. Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey10.1016/j.eswa.2011.01.085 ↗
99. Modelling of solar energy potential in Nigeria using an artificial neural network model10.1016/j.apenergy.2008.12.005 ↗
100. Solar Energy Prediction for Malaysia Using Artificial Neural Networks