Abstract

Solar power forecasting is important in smart cities to balance the energy demand with the energy supply. As solar energy is an inexhaustible clean energy source, it can provide sustainability and bulk energy generation economically. The rapid transition of urban cities into smart cities is increasing power demand in many countries. Solar power is a dominant renewable energy source for the success of smart cities. Solar power generation is purely depends on the photovoltaic (PV) panels and sunlight. Hence, the solar panels can also be installed easily on the rooftop. The reliable power is guaranteed by installing solar panels on rooftop in smart cities. The dependability of smart city functions relies on a steady power supply, making accurate solar power forecasting essential. The paper focuses on exploring the research work done in solar power forecasting. It discusses the functioning of smart cities, describes the importance of solar power for the efficient functioning of smart cities, addresses the challenges of solar power forecasting, and presents the applications of deep learning methodologies such as recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU) and hybrid models in solar power forecasting.

Keywords

Electricity demand, Energy forecasting, Photovoltaic panels, Renewable energy, Sustainable Energy,

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References

  1. A.S. Syed, D. Sierra-Sosa, A. Kumar, A. Elmaghraby, IoT in smart cities: A survey of technologies, practices and challenges. Smart Cities, 4(2), (2021) 429-475. https://doi.org/10.3390/smartcities4020024
  2. M.G.M. Almihat, M.T.E. Kahn, K. Aboalez, A.M. Almaktoof, Energy and Sustainable Development in Smart Cities: An Overview. Smart Cities, 5(4), (2022) 1389-1408. https://doi.org/10.3390/smartcities5040071
  3. N. A. Vukovic, D.E. Nekhorosheva, Renewable Energy in Smart Cities: Challenges and Opportunities by the Case Study of Russia. Smart Cities, 5(4), (2022) 1208-1228. https://doi.org/10.3390/smartcities5040061
  4. A.J. Calderón Godoy, I. González Pérez, Integration of sensor and actuator networks and the SCADA system to promote the migration of the legacy flexible manufacturing system towards the industry 4.0 concept. Journal of Sensor and Actuator Networks, 7(2), (2018) 23. https://doi.org/10.3390/jsan7020023
  5. H. Zhu, L. Shen, Y. Ren, How can smart city shape a happier life? The mechanism for developing a Happiness Driven Smart City. Sustainable cities and society, 80, (2022) 103791. https://doi.org/10.1016/j.scs.2022.103791
  6. B. Asare-Bediako, (2014) SMART energy homes and the smart grid: a framework for intelligent energy management systems for residential customers. Technische Universiteit Eindhoven. https://doi.org/10.6100/IR781632
  7. F. Moura, J.D.A. Silva, Smart cities: Definitions, evolution of the concept and examples of initiatives. Industry, innovation and infrastructure, (2019) 1-9. https://doi.org/10.1007/978-3-319-71059-4_6-1
  8. S.S. Sankari, P.S. Kumar, A Review of Deep Transfer Learning Strategy for Energy Forecasting. Nature Environment and Pollution Technology, 22(4), (2023) 1781-1793. https://doi.org/10.46488/NEPT.2023.v22i04.007
  9. M.A. El-Rashidy, An efficient and portable solar cell defect detection system. Neural Computing and Applications, 34(21), (2022) 18497-18509. https://doi.org/10.1007/s00521-022-07464-2
  10. J. Wang, L. Bi, P. Sun, X. Jiao, X. Ma, X. Lei, Y. Luo, Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images. Sensors, 23(1), (2022) 297. https://doi.org/10.3390/s23010297
  11. K.J. Iheanetu, Solar Photovoltaic Power Forecasting: A Review. Sustainability, 14(24), (2022), 17005. https://doi.org/10.3390/su142417005
  12. J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F.J. Martinez-de-Pison, F. Antonanzas-Torres, Review of photovoltaic power forecasting. Solar energy, 136, (2016) 78-111. http://dx.doi.org/10.1016/j.solener.2016.06.069
  13. S. Pelland, J. Remund, J. Kleissl, T. Oozeki, K. De Brabandere, Photovoltaic and solar forecasting: state of the art, IEA PVPS Task, 14(355), (2013) 1-36.
  14. S. Theocharides, G. Makrides, A. Livera, M. Theristis, P. Kaimakis, G.E. Georghiou, Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post- processing. Applied Energy, 268, (2020) 115023. https://doi.org/10.1016/j.apenergy.2020.115023
  15. J. Ascencio-Vásquez, J. Bevc, K. Reba, K. Brecl, M. Jankovec, M. Topič, Advanced PV performance modelling based on different levels of irradiance data accuracy. Energies, 13(9), (2020) 2166. https://doi.org/10.3390/en13092166
  16. S.S. Subbiah, J. Chinnappan, A review of bio-inspired computational intelligence algorithms in electricity load forecasting. Smart Buildings Digitalization, (2022) 169-192. https://doi.org/10.1201/9781003201069-11
  17. V. Raja, V. Rakesh, A. Kumar, S.S. Sankari, (2023) Comparative Analysis on Solar Panel Defect Detection Using Deep Learning Approaches. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), IEEE, India. https://doi.org/10.1109/ICDSAAI59313.2023.10452666
  18. S.S. Subbiah, J. Chinnappan, Short-term load forecasting using random forest with entropy-based feature selection. In Artificial Intelligence and Technologies: Select Proceedings of ICRTAC-AIT 2020, Springer Singapore, (2021) 73-80. http://dx.doi.org/10.1007/978-981-16-6448-9_8
  19. A. Almadhor, K. Mallikarjuna, R. Rahul, G. Chandra Shekara, R. Bhatia, W. Shishah, V. Mohanavel, S. Suresh Kumar, S.P. Thimothy, Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods. International Journal of Photoenergy, (2022). https://doi.org/10.1155/2022/5442304
  20. D. Chakraborty, J. Mondal, H.B.Barua, A. Bhattacharjee, Computational solar energy–Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India. Renewable Energy Focus, 44, (2023) 277-294. https://doi.org/10.1016/j.ref.2023.01.006
  21. H. Mubarak, A. Abdellatif, S. Ahmad, A. Hammoudeh, S. Mekhilef, H. Mokhlis, (2022) Prediction of Solar Photovoltaic Energy Output Based on Thin-Film Technology Utilizing Various Machine Learning Techniques. In 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), IEEE, India. http://dx.doi.org/10.1109/GlobConPT57482.2022.9938166
  22. V. Mantri, N. Sharma, R. Jayaraman, (2022) Solar Power Generation Prediction for Better Energy Efficiency using Machine Learning. In 2022 7th International Conference on Communication and Electronics Systems (ICCES), IEEE, India. https://doi.org/10.1109/ICCES54183.2022.9835749
  23. X. Sun, D. Wu, M. Jia, Y. Xiao, B. Boulet, Forecasting of Solar Energy Generation via Dynamic Model Ensemble, In 2022 IEEE Electrical Power and Energy Conference (EPEC), IEEE, Canada. https://doi.org/10.1109/EPEC56903.2022.10000238
  24. F. Wang, Z. Zhen, B. Wang, Z. Mi, Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Applied Sciences, 8(1), (2017) 28. http://dx.doi.org/10.3390/app8010028
  25. J. Huertas Tato, M. Centeno Brito, Using smart persistence and random forests to predict photovoltaic energy production. Energies, 12(1), (2018) 100. https://doi.org/10.3390/en12010100
  26. G. Li, H. Wang, S. Zhang, J. Xin, H. Liu, Recurrent neural networks based photovoltaic power forecasting approach. Energies, 12(13), (2019) 2538.
  27. S. Khan, F. Shaikh, M.M. Siddiqui, T. Hussain, L. Kumar, A. Nahar, Hourly forecasting of solar photovoltaic power in Pakistan using recurrent neural networks. International Journal of Photoenergy, (2022) 1-11. https://doi.org/10.1155/2022/7015818
  28. A. Alzahrani, P. Shamsi, C. Dagli, M. Ferdowsi, Solar irradiance forecasting using deep neural networks, Procedia Computer Science, 114, (2017) 304-313. https://doi.org/10.1016/j.procs.2017.09.045
  29. S. Mishra, P. Palanisamy, (2018) Multi-time-horizon solar forecasting using recurrent neural network, In 2018 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, USA. https://doi.org/10.1109/ECCE.2018.8558187
  30. M. Jaihuni, J.K. Basak, F. Khan, F.G. Okyere, T. Sihalath, A. Bhujel, J. Park, D.H. Lee, H.T. Kim, A novel recurrent neural network approach in forecasting short term solar irradiance. ISA transactions, 121, (2022) 63-74. https://doi.org/10.1016/j.isatra.2021.03.043
  31. S.S. Subbiah, S.K. Paramasivan, K. Arockiasamy, S. Senthivel, M. Thangavel, Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features. Intelligent Automation & Soft Computing, 35(3), (2023) 3829-3844. https://doi.org/10.32604/iasc.2023.030480
  32. P. Singh, N. K. Singh, A.K. Singh, (2022) Solar Photovoltaic Energy Forecasting Using Machine Learning and Deep Learning Technique. In 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, India. http://dx.doi.org/10.1109/UPCON56432.2022.9986446
  33. S.J. Sultanuddin, A. Suganya, M. Ahmed, V. Shanmugasundaram, P. Adhikary, S. Sajith, (2022) Hybrid Solar Energy Forecasting with Supervised Deep Learning in IoT Environment, In 2022 International Conference on Innovative Computing. Intelligent Communication and Smart Electrical Systems (ICSES), IEEE, India. https://doi.org/10.1109/ICSES55317.2022.9914113
  34. L. Hamberg, (2021). Photovoltaic System Performance Forecasting Using LSTM Neural Networks.
  35. J. Sharma, S. Soni, P. Paliwal, S. Saboor, P.K. Chaurasiya, M. Sharifpur, N. Khalilpoor, A. Afzal, A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India, Energy Science & Engineering, 10(8), (2022) 2909-2929. https://doi.org/10.1002/ese3.1178
  36. H. Chen, X. Chang, Photovoltaic power prediction of LSTM model based on Pearson feature selection. Energy Reports, 7, (2021) 1047-1054. https://doi.org/10.1016/j.egyr.2021.09.167
  37. M. Leelavathi, V. Suresh Kumar, Real-time solar power forecasting using LSTM algorithms. European chemical bulletin, 12, (2023) 2498-2503.
  38. R. Zafar, B.H. Vu, M. Husein, I.Y. Chung, Day-Ahead solar irradiance forecasting using hybrid recurrent neural network with weather classification for power system scheduling. Applied Sciences, 11(15), (2021) 6738. https://doi.org/10.3390/app11156738
  39. N.L.M. Jailani, J.K. Dhanasegaran, G. Alkawsi, A.A. Alkahtani, C.C. Phing, Y. Baashar, L.F. Capretz, A.Q. Al-Shetwi, S.K. Tiong, Investigating the Power of LSTM-Based Models in Solar Energy Forecasting. Processes, 11(5), (2023) 1382. http://dx.doi.org/10.3390/pr11051382
  40. B. Chen, P. Lin, Y. Lin, Y. Lai, S. Cheng, Z. Chen, L. Wu, Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets, In IOP Conference Series: Earth and Environmental Science, IOP Publishing, 431(1) (2020) 012059. http://dx.doi.org/10.1088/1755-1315/431/1/012059
  41. S.S. Subbiah, S.K. Paramasivan, M. Thangave, Prediction of particulate matter PM2. 5 using bidirectional gated recurrent unit with feature selection, 26(3), (2024) 1-12. https://doi.org/10.30955/gnj.005631
  42. S.S. Subbiah, P.S. Kumar, Deep learning based load forecasting with decomposition and feature selection techniques, Journal of Scientific & Industrial Research, 81(5), (2022) 505-517. https://doi.org/10.56042/jsir.v81i05.56794
  43. T. Khatib, A. Gharaba, Z. Haj Hamad, A. Masri, Novel models for photovoltaic output current prediction based on short and uncertain dataset by using deep learning machines. Energy Exploration & Exploitation, 40(2), (2022) 724-748. https://doi.org/10.1177/01445987211068119
  44. A.F. Faisal, A. Rahman, M.T.M. Habib, A.H. Siddique, M. Hasan, M. M. Khan, Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh. Results in Engineering, 13, (2022) 100365. http://dx.doi.org/10.1016/j.rineng.2022.100365
  45. W. Bendali, I. Saber, B. Bourachdi, O. Amri, M. Boussetta and Y. Mourad, Multi time horizon ahead solar irradiation prediction using GRU, PCA, and GRID SEARCH based on multivariate datasets. Journal Européen des Systèmes Automatisés, 55(1), (2022) 11. http://dx.doi.org/10.18280/jesa.550102
  46. Y. Wang, W. Liao, Y. Chang, Gated recurrent unit network-based short-term photovoltaic forecasting. Energies, 11(8), (2018) 2163. https://doi.org/10.3390/en11082163
  47. Z.A. Khan, T. Hussain, S.W. Baik, Dual stream network with attention mechanism for photovoltaic power forecasting, Applied Energy, 338, (2023) 120916. http://dx.doi.org/10.1016/j.apenergy.2023.120916