Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 8 , Issue 1 , PP: 01-08, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mona Ahmed Yassen 1 , Mohamed Gamal Abdel-Fattah 2 , Islam Ismail 3 , EL-Sayed M. El Kenawy 4 , Hossam El-Din Moustafa 5
Doi: https://doi.org/10.54216/JAIM.080101
Accurate generation forecasting of Renewable Energy Sources (RES) is becoming more and more crucial for effective grid operation and energy management as RES are incorporated into the electrical grid. Because Machine Learning (ML) and Deep Learning (DL) algorithms can learn complicated relationships from data and provide accurate forecasts, they have become more popular than traditional forecasting approaches, which have limits. This article examines the state of the art and future directions in the field of ML and DL-based forecasting of renewable energy generation. This paper reviews the several approaches and models that have been used to project renewable energy. It also highlights the challenges, such as managing the uncertainty and unpredictability of renewable energy output, data accessibility, and model interpret ability. To sum up, this study emphasizes how important it is to develop accurate and dependable renewable energy forecasting models to facilitate the future transition to sustainable energy sources and enable the integration of RES into the electrical grid.
Renewable Energy , Machine Learning , Deep Learning
[1] Wadim Strielkowski, Lubom´ır Civ´ın, Elena Tarkhanova, Manuela Tvaronaviˇciene˙, and Yelena Petrenko. Renew- able energy in the sustainable development of electrical power sector: A review. Energies, 14(24):8240, 2021.
[2] Iman Ghalehkhondabi, Ehsan Ardjmand, Gary R Weckman, and William A Young. An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 8:411–447, 2017.
[3] Adam Krechowicz, Maria Krechowicz, and Katarzyna Poczeta. Machine learning approaches to predict electricity production from renewable energy sources. Energies, 15(23):9146, 2022.
[4] Ying-Yi Hong and Thursy Rienda Aulia Satriani. Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network. Energy, 209:118441, 2020.
[5] Javier Huertas-Tato, Ricardo Aler, In´es M Galvan, Francisco J Rodr´ıguez-Ben´ıtez, Clara Arbizu-Barrena, and David Pozo-Vazquez. A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning. Solar Energy, 195:685–696, 2020.
[6] Francisco J Rodr´ıguez-Ben´ıtez, Clara Arbizu-Barrena, Javier Huertas-Tato, Ricardo Aler-Mur, In´es Galv´an- Le´on, and David Pozo-Vazquez. A short-term solar radiation forecasting system for the iberian peninsula. Part 1: Models description and performance assessment. Solar Energy, 195:396–412, 2020.
[7] Alireza Zendehboudi, M Abdul Baseer, and R Saidur. Application of support vector machine models for fore- casting solar and wind energy resources: A review. Journal of cleaner production, 199:272–285, 2018.
[8] Jianzhou Wang and Jianming Hu. A robust combination approach for short-term wind speed forecasting and analysis–combination of the arima (autoregressive integrated moving average), elm (extreme learning machine), svm (support vector machine) and lssvm (least square svm) forecasts using a gpr (gaussian process regression) model. Energy, 93:41–56, 2015.
[9] Liye Xiao, Wei Shao, Fulong Jin, and Zhuochun Wu. A self-adaptive kernel extreme learning machine for short- term wind speed forecasting. Applied Soft Computing, 99:106917, 2021.
[10] Erasmo Cadenas, Wilfrido Rivera, Rafael Campos-Amezcua, and Christopher Heard. Wind speed prediction using a univariate arima model and a multivariate narx model. Energies, 9(2):109, 2016.
[11] Zhongda Tian. Short-term wind speed prediction based on lmd and improved fa optimized combined kernel function lssvm. Engineering Applications of Artificial Intelligence, 91:103573, 2020.
[12] Ghadah Alkhayat and Rashid Mehmood. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI, 4:100060, 2021.
[13] T Rajasundrapandiyanleebanon, K Kumaresan, Sakthivel Murugan, MSP Subathra, and Mahima Sivakumar. Solar energy forecasting using machine learning and deep learning techniques. Archives of Computational Methods in Engineering, 30(5):3059–3079, 2023.
[14] Hanin Alkabbani, Ali Ahmadian, Qinqin Zhu, and Ali Elkamel. Machine learning and metaheuristic methods for renewable power forecasting: a recent review. Frontiers in Chemical Engineering, 3:665415, 2021.
[15] Xiao Zhou, Chengxi Liu, Yongjian Luo, Baoying Wu, Nan Dong, Tianying Xiao, and Haojun Zhu. Wind power forecast based on variational mode decomposition and long short term memory attention network. Energy Reports, 8:922–931, 2022.
[16] Ling-Ling Li, Xue Zhao, Ming-Lang Tseng, and Raymond R Tan. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. Journal of Cleaner Production, 242:118447, 2020.
[17] Jiandong Duan, Peng Wang, Wentao Ma, Xuan Tian, Shuai Fang, Yulin Cheng, Ying Chang, and Haofan Liu. Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and correntropy long short-term memory neural network. Energy, 214:118980, 2021.
[18] Amir Rafati, Mahmood Joorabian, Elaheh Mashhour, and Hamid Reza Shaker. High dimensional very short-term solar power forecasting based on a data-driven heuristic method. Energy, 219:119647, 2021.
[19] Rui Yin, Dengxuan Li, Yifeng Wang, and Weidong Chen. Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network. Global Energy Interconnection, 3(6):571– 576, 2020.
[20] Germ´anico Lopez and Pablo Arboleya. Short-term wind speed forecasting over complex terrain using linear regression models and multivariable lstm and narx networks in the andes mountains, ecuador. Renewable Energy, 183:351–368, 2022.
[21] Khizir Mahmud, Sami Azam, Asif Karim, Sm Zobaed, Bharanidharan Shanmugam, and Deepika Mathur. Ma- chine learning based pv power generation forecasting in alice springs. IEEE Access, 9:46117–46128, 2021.
[22] Sanjay Kumar and Tarlochan Kaur. Development of ann based model for solar potential assessment using various meteorological parameters. Energy Procedia, 90:587–592, 2016.
[23] Su-Chang Lim, Jun-Ho Huh, Seok-Hoon Hong, Chul-Young Park, and Jong-Chan Kim. Solar power forecasting using cnn-lstm hybrid model. Energies, 15(21):8233, 2022.
[24] Bixuan GAO, Xiaoqiao Huang, Junsheng Shi, Yonghang Tai, and Jun Zhang. Hourly forecasting of solar irradiance based on ceemdan and multi-strategy cnn-lstm neural networks. Renewable Energy, 162:1665–1683, 2020.
[25] Davide Cannizzaro, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Andrea Acquaviva, and Edoardo Patti. Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications, 181:115167, 2021.
[26] Tebogo Bokaba, Wesley Doorsamy, and Babu Sena Paul. A comparative study of ensemble models for predicting road traffic congestion. Applied Sciences, 12(3):1337, 2022.
[27] Yueqi Wu and Xiandong Ma. A hybrid lstm-kld approach to condition monitoring of operational wind turbines. Renewable Energy, 181:554–566, 2022.
[28] Borja Fernandez-Gauna, Manuel Gran˜a, Juan-Luis Osa-Amilibia, and Xabier Larrucea. Actor-critic continuous state reinforcement learning for wind-turbine control robust optimization. Information Sciences, 591:365–380, 2022.
[29] Jan Lorenz Svensen, Hans Henrik Niemann, Anne Katrine V Falk, and Niels K Poulsen. Sewer orientated framework for ensemble-based chance-constrained model predictive control. Advanced Control for Applications: Engineering and Industrial Systems, 3(4):e68, 2021.
[30] Ibomoiye Domor Mienye and Yanxia Sun. A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access, 10:99129–99149, 2022.
