# A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

#### Objectives

- The recent machine learning and deep learning approaches based on popular renewable energy sources such as wind, solar, tidal, and hydropower are reviewed for their usage in techniques like power prediction, energy conversion, and forecasting.
- A summary is included to exhibit the main contributions and the ideologies of researchers who worked with various sources of renewable energy.
- The power prediction, energy conversion, and forecasting based on wind, solar, tidal, and hydropower energy sources using ML and DL techniques are examined thoroughly with their advantages and drawbacks.
- Thus, this survey acts as a key tool for future researchers to overcome the challenges in existing research in order to build a more robust model with advanced technologies.

## 2. Literature Survey

#### 2.1. Applications of Solar Energy Using Machine Learning and Deep Learning Approaches

#### 2.2. Applications of Wind Energy Based on Machine Learning and Deep Learning Approaches

#### 2.3. Applications of Hydro and Tidal Energies Based on Machine Learning and Deep Learning Approaches

## 3. Methodology

#### 3.1. Renewable Energy Sources

#### 3.1.1. Wind Energy

#### 3.1.2. Solar Energy

- (i)
- The radiation from the sun is absorbed by the solar cells which is the primary step involved in converting the solar energy into electrical energy.
- (ii)
- Every individual solar cell is comprising a thin layer of semiconductor material with two silicon layers. The usage of silicon-based semiconductors can act as both conductors and insulators.
- (iii)
- Among the two silicon layers, one layer is positively charged and another one is negatively charged. The positively charged material is represented as P-type and the negatively charged material is represented as N-type.
- (iv)
- Generally, the energy from the sun strikes the ground surface in the form of smaller packets known as photons. When these photons strike the photovoltaic material, it creates an electric current.

#### 3.1.3. Hydro Energy and Tidal Energy

## 4. Summary

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Rahman, A.; Aziz, T.; Nahid-Al-Masood; Deeba, S.R. A time of use tariff scheme for demand side management of residential energy consumers in Bangladesh. Energy Rep.
**2021**, 7, 3189–3198. [Google Scholar] [CrossRef] - Eid, A.; Kamel, S.; Abualigah, L. Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Comput. Appl.
**2021**, 33, 14327–14355. [Google Scholar] [CrossRef] - Biswas, S.; Roy, P.K.; Chatterjee, K. Development of MADB of P-I controller using LMI technique in a renewable energy based AGC system and study its application in a deregulated environment including energy storage device. Optim. Control Appl. Methods
**2023**, 44, 426–451. [Google Scholar] [CrossRef] - Connolly, K. The regional economic impacts of offshore wind energy developments in Scotland. Renew. Energy
**2020**, 160, 148–159. [Google Scholar] [CrossRef] - Alkawsi, G.; Baashar, Y.; Alkahtani, A.A.; Lim, C.W.; Tiong, S.K.; Khudari, M. Viability Assessment of Small-Scale On-Grid Wind Energy Generator for Households in Malaysia. Energies
**2021**, 14, 3391. [Google Scholar] [CrossRef] - Hassan, M.H.; Kamel, S.; Abualigah, L.; Eid, A. Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Syst. Appl.
**2021**, 182, 115205. [Google Scholar] [CrossRef] - Yang, Y.; Campana, P.E.; Yan, J. Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities. Renew. Sustain. Energy Rev.
**2020**, 131, 109967. [Google Scholar] [CrossRef] - Maamoun, N.; Kennedy, R.; Jin, X.; Urpelainen, J. Identifying coal-fired power plants for early retirement. Renew. Sustain. Energy Rev.
**2020**, 126, 109833. [Google Scholar] [CrossRef] - Ganiyu, S.O.; Martínez-Huitle, C.A.; Rodrigo, M.A. Renewable energies driven electrochemical wastewater/soil decontamination technologies: A critical review of fundamental concepts and applications. Appl. Catal. B
**2020**, 270, 118857. [Google Scholar] [CrossRef] - Gasa, G.; Lopez-Roman, A.; Prieto, C.; Cabeza, L.F. Life cycle assessment (LCA) of a concentrating solar power (CSP) plant in tower configuration with and without thermal energy storage (TES). Sustainability
**2021**, 13, 3672. [Google Scholar] [CrossRef] - Nazir, M.S.; Ali, N.; Bilal, M.; Iqbal, H.M.N. Potential environmental impacts of wind energy development: A global perspective. Curr. Opin. Environ. Sci. Health
**2020**, 13, 85–90. [Google Scholar] [CrossRef] - Goh, H.H.; He, R.; Zhang, D.; Lui, H.; Dai, W.; Lim, C.S.; Kurniawan, T.A.; Teo, K.T.K.; Goh, K.C. A multimodal approach to chaotic renewable energy prediction using meteorological and historical information. Appl. Soft Comput.
**2022**, 118, 108487. [Google Scholar] [CrossRef] - Rosas, M.A.T.; Pérez, M.R.; Pérez, E.R.M. Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico. Renew. Energy
**2022**, 188, 1141–1165. [Google Scholar] [CrossRef] - Sasaki, K.; Aki, H.; Ikegami, T. Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty. Energy
**2022**, 239, 122183. [Google Scholar] [CrossRef] - Zhang, S.; Chen, Y.; Xiao, J.; Zhang, W.; Feng, R. Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. Renew. Energy
**2021**, 174, 688–704. [Google Scholar] [CrossRef] - Wang, L.; He, Y.; Li, L.; Liu, X.; Zhao, Y. A novel approach to ultra-short-term multi-step wind power predictions based on encoder–decoder architecture in natural language processing. J. Clean. Prod.
**2022**, 354, 131723. [Google Scholar] [CrossRef] - Yan, J.; Möhrlen, C.; Göçmen, T.; Kelly, M.; Wessel, A.; Giebel, G. Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain. Renew. Sustain. Energy Rev.
**2022**, 165, 112519. [Google Scholar] [CrossRef] - Chen, W.-H.; You, F. Sustainable building climate control with renewable energy sources using nonlinear model predictive control. Renew. Sustain. Energy Rev.
**2022**, 168, 112830. [Google Scholar] [CrossRef] - Ser, J.D.; Casillas-Perez, D.; Cornejo-Bueno, L.; Prieto-Godino, L.; Sanz-Justo, J.; Casanova-Mateo, C.; Salcedo-Sanz, S. Randomization-based machine learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives. Appl. Soft Comput.
**2022**, 118, 108526. [Google Scholar] - Hu, G.; You, F. Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management. Renew. Sustain. Energy Rev.
**2022**, 168, 112790. [Google Scholar] [CrossRef] - Xue, J.; Ding, J.; Zhao, L.; Zhu, D.; Li, L. An option pricing model based on a renewable energy price index. Energy
**2022**, 239, 122117. [Google Scholar] [CrossRef] - May, R.; Nygård, T.; Falkdalen, U.; Åstrom, J.; Hamre, Ø.; Stokke, B.G. Paint it black: Efficacy of increased wind turbine rotor blade visibility to reduce avian fatalities. Ecol. Evol.
**2020**, 10, 8927–8935. [Google Scholar] [CrossRef] [PubMed] - Akdas, S.B.; Onur, M. Analytical solutions for predicting and optimizing geothermal energy extraction from an enhanced geothermal system with a multiple hydraulically fractured horizontal-well doublet. Renew. Energy
**2022**, 181, 567–580. [Google Scholar] [CrossRef] - Gonz’alez-Longatt, F.; Wall, P.; Terzija, V. Wake effect in wind farm performance: Steady-state and dynamic behavior. Renew. Energy
**2012**, 39, 329–338. [Google Scholar] [CrossRef] - Coro, G.; Trumpy, E. Predicting geographical suitability of geothermal power plants. J. Clean. Prod.
**2020**, 267, 121874. [Google Scholar] [CrossRef] - Ikram, R.M.A.; Dai, H.-L.; Ewees, A.A.; Shiri, J.; Kisi, O.; Zounemat-Kermani, M. Application of improved version of multi verse optimizer algorithm for modeling solar radiation. Energy Rep.
**2022**, 8, 12063–12080. [Google Scholar] [CrossRef] - Abdelghany, R.Y.; Kamel, S.; Sultan, H.M.; Khorasy, A.; Elsayed, S.K.; Ahmed, M. Development of an improved bonobo optimizer and its application for solar cell parameter estimation. Sustainability
**2021**, 13, 3863. [Google Scholar] [CrossRef] - Liu, Z.; Fan, G.; Sun, D.; Wu, D.; Guo, J.; Zhang, S.; Yang, X.; Lin, X.; Ai, L. A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings. Energy
**2022**, 239, 122577. [Google Scholar] [CrossRef] - Tercan, S.M.; Demirci, A.; Gokalp, E.; Cali, U. Maximizing self-consumption rates and power quality towards two-stage evaluation for solar energy and shared energy storage empowered microgrids. J. Energy Storage
**2022**, 51, 104561. [Google Scholar] [CrossRef] - Mostafa, N.; Ramadan, H.S.M.; Elfarouk, O. Renewable energy management in smart grids by using big data analytics and machine learning. Mach. Learn. Appl.
**2022**, 9, 100363. [Google Scholar] [CrossRef] - Mehrpooya, M.; Raeesi, M.; Pourfayaz, F.; Delpisheh, M. Investigation of a hybrid solar thermochemical water-splitting hydrogen production cycle and coal-fueled molten carbonate fuel cell power plant. Sustain. Energy Technol. Assess.
**2021**, 47, 101458. [Google Scholar] [CrossRef] - Almeshaiei, E.; Al-Habaibeh, A.; Shakmak, B. Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers. J. Clean. Prod.
**2020**, 244, 118788. [Google Scholar] [CrossRef] - Koo, C.; Li, W.; Cha, S.H.; Zhang, S. A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques. Renew. Energy
**2019**, 133, 575–592. [Google Scholar] [CrossRef] - Munawar, U.; Wang, Z. A framework of using machine learning approaches for short-term solar power forecasting. J. Electr. Eng. Technol.
**2020**, 15, 561–569. [Google Scholar] [CrossRef] - AlKandari, M.; Ahmad, I. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Appl. Comput. Inf. 2020; ahead-of-print. [Google Scholar]
- Nejati, M.; Amjady, N. A New Solar Power Prediction Method Based on Feature Clustering and Hybrid-Classification-Regression Forecasting. IEEE Trans. Sustain. Energy
**2022**, 13, 1188–1198. [Google Scholar] [CrossRef] - Zhang, Y.; Chen, Y. Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction. Environ. Sci. Pollut. Res.
**2022**, 29, 22661–22674. [Google Scholar] [CrossRef] - Zhao, N.; You, F. Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization. Renew. Sustain. Energy Rev.
**2022**, 161, 112428. [Google Scholar] [CrossRef] - Fathy, A.; Alharbi, A.G.; Alshammari, S.; Hasanien, H.M. Archimedes optimization algorithm based maximum power point tracker for wind energy generation system. Ain Shams Eng. J.
**2022**, 13, 101548. [Google Scholar] [CrossRef] - Wang, Y.; Li, X.; Yu, X.; Zhu, J.; Shen, P.; Wang, Z.L.; Cheng, T. Driving-torque self-adjusted triboelectric nanogenerator for effective harvesting of random wind energy. Nano Energy
**2022**, 99, 107389. [Google Scholar] [CrossRef] - Angadi, S.; Yargatti, U.R.; Suresh, Y.; Raju, A.B. Speed sensorless maximum power point tracking technique for SEIG-based wind energy conversion system feeding induction motor pump. Electr. Eng.
**2022**, 104, 2935–2948. [Google Scholar] [CrossRef] - Anisa, E.; Berrada, A.; Bakhouya, M. Optimal sizing and deployment of gravity energy storage system in hybrid PV-Wind power plant. Renew. Energy
**2022**, 183, 12–27. [Google Scholar] - Yang, J.J.; Yang, M.; Wang, M.X.; Du, P.J.; Yu, Y.X. A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing. Int. J. Electr. Power Energy Syst.
**2020**, 119, 105928. [Google Scholar] [CrossRef] - Rushdi, M.A.; Rushdi, A.A.; Dief, T.N.; Halawa, A.M.; Yoshida, S.; Schmehl, R. Power prediction of airborne wind energy systems using multivariate machine learning. Energies
**2020**, 13, 2367. [Google Scholar] [CrossRef] - Aksoy, B.; Selbaş, R. Estimation of wind turbine energy production value by using machine learning algorithms and development of implementation program. Energy Sources Part A
**2021**, 43, 692–704. [Google Scholar] [CrossRef] - Wang, L.; Tao, R.; Hu, H.; Zeng, Y.-R. Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder. Renew. Energy
**2021**, 164, 642–655. [Google Scholar] [CrossRef] - Meka, R.; Alaeddini, A.; Bhaganagar, K. A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables. Energy
**2021**, 221, 119759. [Google Scholar] [CrossRef] - El-Aziz, R.M.A. Renewable power source energy consumption by hybrid machine learning model. Alex. Eng. J.
**2022**, 61, 9447–9455. [Google Scholar] [CrossRef] - Zhao, Y.; Foong, L.K. Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm. Measurement
**2022**, 198, 111405. [Google Scholar] [CrossRef] - Naik, K.R.; Rajpathak, B.; Mitra, A.; Sadanala, C.; Kolhe, M.L. Power management scheme of DC micro-grid integrated with Photovoltaic-Battery-Micro hydro power plant. J. Power Sources
**2022**, 525, 230988. [Google Scholar] [CrossRef] - Fonseca, J.D.; Commenge, J.-M.; Camargo, M.; Falk, L.; Gil, I.D. Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions. Energy
**2021**, 214, 118989. [Google Scholar] [CrossRef] - Ma, Z.; Li, M.-J.; Zhang, K.M.; Yuan, F. Novel designs of hybrid thermal energy storage system and operation strategies for concentrated solar power plant. Energy
**2021**, 216, 119281. [Google Scholar] [CrossRef] - Sahu, P.C.; Baliarsingh, R.; Prusty, R.C.; Panda, S. Novel DQN optimised tilt fuzzy cascade controller for frequency stability of a tidal energy-based AC microgrid. Int. J. Ambient Energy
**2022**, 43, 3587–3599. [Google Scholar] [CrossRef] - Chen, C.; Hu, Y.; Karuppiah, M.; Kumar, P.M. Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustain. Energy Technol. Assess.
**2021**, 47, 101358. [Google Scholar] [CrossRef] - Mostafa, M.H.; Aleem, S.H.E.A.; Ali, S.G.; Ali, Z.M.; Abdelaziz, A.Y. Techno-economic assessment of energy storage systems using annualized life cycle cost of storage (LCCOS) and levelized cost of energy (LCOE) metrics. J. Energy Storage
**2020**, 29, 101345. [Google Scholar] [CrossRef] - Sayed, E.T.; Olabi, A.G.; Alami, A.H.; Radwan, A.; Mdallal, A.; Rezk, A.; Abdelkareem, M.A. Renewable Energy and Energy Storage Systems. Energies
**2023**, 16, 1415. [Google Scholar] [CrossRef] - Mujtaba, A.; Jena, P.K.; Bekun, F.V.; Sahu, P.K. Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries. Renew. Sustain. Energy Rev.
**2022**, 160, 112300. [Google Scholar] [CrossRef] - Roga, S.; Bardhan, S.; Kumar, Y.; Dubey, S.K. Recent technology and challenges of wind energy generation: A review. Sustain. Energy Technol. Assess.
**2022**, 52, 102239. [Google Scholar] [CrossRef] - Rajasingam, N.; Rasi, D.; Deepa, S.N. Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems. Soft Comput.
**2019**, 23, 8453–8470. [Google Scholar] [CrossRef] - Huang, Y.; Li, J.; Hou, W.; Zhang, B.; Zhang, Y.; Li, Y.; Sun, L. Improved clustering and deep learning based short-term wind energy forecasting in large-scale wind farms. J. Renew. Sustain. Energy
**2020**, 12, 066101. [Google Scholar] [CrossRef] - Shirzadi, N.; Nasiri, F.; El-Bayeh, C.; Eicker, U. Optimal dispatching of renewable energy-based urban microgrids using a deep learning approach for electrical load and wind power forecasting. Int. J. Energy Res.
**2022**, 46, 3173–3188. [Google Scholar] [CrossRef] - Zhang, S.; Li, X. Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method. Energy
**2021**, 217, 119321. [Google Scholar] [CrossRef] - Zhang, G.; Hu, W.; Cao, D.; Huang, Q.; Chen, Z.; Blaabjerg, F. A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration. Renew. Energy
**2021**, 178, 363–376. [Google Scholar] [CrossRef] - He, B.; Ye, L.; Pei, M.; Lu, P.; Dai, B.; Li, Z.; Wang, K. A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. Energy Rep.
**2022**, 8, 929–939. [Google Scholar] [CrossRef] - Li, Y.; Wang, R.; Li, Y.; Zhang, M.; Long, C. Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach. Appl. Energy
**2023**, 329, 120291. [Google Scholar] [CrossRef] - Rajesh, P.; Muthubalaji, S.; Srinivasan, S.; Shajin, F.H. Leveraging a dynamic differential annealed optimization and recalling enhanced recurrent neural network for maximum power point tracking in wind energy conversion system. Technol. Econ. Smart Grids Sustain. Energy
**2022**, 7, 19. [Google Scholar] [CrossRef] - Lin, G.-Q.; Li, L.-L.; Tseng, M.-L.; Liu, H.-M.; Yuan, D.-D.; Tan, R.R. An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. J. Clean. Prod.
**2020**, 253, 119966. [Google Scholar] [CrossRef] - Bouzgou, H.; Gueymard, C.A. Fast short-term global solar irradiance forecasting with wrapper mutual information. Renew. Energy
**2019**, 133, 1055–1065. [Google Scholar] [CrossRef] - Lee, M.-H. Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches. Sol. Energy
**2022**, 234, 360–367. [Google Scholar] [CrossRef] - Zambrano, A.F.; Giraldo, L.F. Solar irradiance forecasting models without on-site training measurements. Renew. Energy
**2020**, 152, 557–566. [Google Scholar] [CrossRef] - Khan, W.; Walker, S.; Zeiler, W. Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy
**2022**, 240, 122812. [Google Scholar] [CrossRef] - Lim, S.C.; Huh, J.H.; Hong, S.H.; Park, C.Y.; Kim, J.C. Solar Power Forecasting Using CNN-LSTM Hybrid Model. Energies
**2022**, 15, 8233. [Google Scholar] [CrossRef] - Stoean, C.; Zivkovic, M.; Bozovic, A.; Bacanin, N.; Strulak-Wójcikiewicz, R.; Antonijevic, M.; Stoean, R. Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation. Axioms
**2023**, 12, 266. [Google Scholar] [CrossRef] - Liu, Z.; Zhang, W.G.; Helfrich, K.R. Vertical Structure of Barotropic-to-Baroclinic Tidal Energy Conversion on a Continental Slope. J. Geophys. Res. Ocean.
**2022**, 127, e2022JC019130. [Google Scholar] [CrossRef] - Omidvar, S.; Fagundes, M.; Woodson, C.B. Modification of Internal Wave Generation and Energy Conversion in the Nearshore Due to Tide-Tide and Tide-Wind Interactions. J. Geophys. Res. Ocean.
**2022**, 127, e2021JC017986. [Google Scholar] [CrossRef] - Shen, Z.; Yao, Y.; Wang, Q.; Lu, L.; Yang, H. A novel micro power generation system to efficiently harvest hydroelectric energy for power supply to water intelligent networks of urban water pipelines. Energy
**2023**, 268, 126694. [Google Scholar] [CrossRef] - Sari, B.; Sid, M.A. Deception attack monitoring in vulnerable hydroelectric generator system. Asian J. Control
**2022**, 24, 517–525. [Google Scholar] [CrossRef] - Monahan, T.; Tang, T.; Adcock, T.A. A hybrid model for online short-term tidal energy forecasting. Appl. Ocean Res.
**2023**, 137, 103596. [Google Scholar] [CrossRef] - Fujiwara, R.; Fukuhara, R.; Ebiko, T.; Miyatake, M. Forecasting design values of tidal/ocean power generator in the strait with unidirectional flow by deep learning. Intell. Syst. Appl.
**2022**, 14, 200067. [Google Scholar] [CrossRef] - Chen, Y.C.; Yeh, H.C.; Kao, S.P.; Wei, C.; Su, P.Y. Water level forecasting in tidal rivers during typhoon periods through ensemble empirical mode decomposition. Hydrology
**2023**, 10, 47. [Google Scholar] [CrossRef]

**Figure 2.**VAWT and HAWT [58].

**Table 1.**Research on basic approaches for power prediction, energy conversion, and wind energy forecasting.

Author | Proposed Method | Advantage | Limitations |
---|---|---|---|

Rajasingam et al. [59] | A doubly fed induction generator that uses deep learning neural network and a Density-based Grey Artificial Bee Colony (D-GABC) algorithm | The controller with the doubly fed induction generator minimizes the reactive power in a lesser settling time | The increased number of iterations minimizes the efficiency of the induction generator, due to its step response characteristics |

Huang et al. [60] | Power prediction approach that uses Density Peak Clustering (DPC) and a deep learning approach | The suggested power prediction approach has the capability to evaluate the prediction time by considering the relation among the wind turbines | It is difficult to correlate the type of wind turbines in the power prediction approach that uses DPC |

Shirzadi et al. [61] | Mixed-Integer Linear Programming (MILP) method to optimize the system’s power reliability and the cost for operations | The forecasting results from MILP breaks down the cost required for operation and enhances battery life | The results obtained from the MILP approach varies due to unexpected climatic changes and the unexpected climatic change probably diminishes the power generation capability |

Zhang and Li [62] | A downscaling approach on the basis of a Bidirectional Gated Recurrent Unit (BiGRU) to predict the offshore wind energy sources | The downscaling approach using BiGRU effectively detects the spatial patterns of downscaled wind energy and minimizes biasing state of suggested approach | However, the downscaling approach using BiGRU developed uncertainties when it was evaluated in single models |

Zhang et al. [63] | Sparsity promoting adaptive control method to self-tune power system stabilizers along with a Deep Deterministic Policy Gradient (DDPG algorithm) | The suggested DDPG approach has the ability to adjust multi- power system stabilizing parameters and helps in an optimistic parameter setting | Since the suggested approach was a model-based technique, the presence of an accurate model is necessary to train the agent or else the self-tuning capability of the model will be reduced |

Boyu He et al. [64] | A Short-term Wind Power Forecasting (WPF) model referred as IOWA CNN-LSTM to predict the power forecasts using the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) combined with Induced Ordered Weighted Average (IOWA) | The IOWA operator can effectively combine the prediction results to CNN and LSTM which helps to enhance the power prediction accuracy | However, the suggested approach considered the mapping relation among the speed series of the winds and other relevant factors |

Yang Li et al. [65] | A forecasting scheme based on federated learning and deep reinforcement learning (DRL) for short-term wind power forecasting referred as the Federated Deep Reinforcement Learning (FedDRL) with Deep Deterministic Policy Gradient (DDPG) | The FedDRL has the capability to obtain accurate predictability in decentralized manner and ensure better forecasting accuracy | However, the training efficiency of the model does not meet the requirement related to real-time dispatch power systems |

P. Rajesh et al. [66] | A hybrid approach to tract the maximal power of the Wind Energy Conversion System (WECS) which is a combination of the Dynamic Differential Annealed Optimization (DDAO) and Recalling Enhanced Recurrent Neural Network (RERNN) known as ${\mathrm{D}}^{2}{\mathrm{AORERN}}^{2}$ | The suggested approach has better search ability and helps to maintain system ability with desired performic uncertainties | However, imbalance occurred when the huge load was prompted for a longer time duration |

**Table 2.**Research based on machine learning and deep learning approaches for power prediction, energy conversion, and forecasting for solar energy.

Author | Proposed Method | Advantage | Limitations |
---|---|---|---|

Lin et al. [67] | A strategy based on inertia weighting and a Cauchy mutation operator was used to enhance performance of moth flame optimization and the prediction using Support Vector Machine (SVM) | The suggested approach used moth flame optimization aided in improvising the prediction capability of photovoltaic energy and minimized the impact caused due to penetration of PV cells into the grid | However, when excessive input was fed into model, training time of model increased, and the prediction accuracy decreased |

Bouzgou and Gueymard [68] | Wrapper Mutual Information Methodology (WMIM) was devised for solar irradiance forecasting which combined the mutual information and the Extreme Learning Machine (ELM) to forecast the horizons | The suggested WMIM had advanced generalization ability which effectively selected the variables for time series forecasting | However, the WMIM approach was not robust enough for time horizons and cloudy weather conditions |

Lee [69] | The tree-based machine learning model predicted the open circuit voltage of the Non-Fullerene Acceptors on the basis of Organic Solar Cells (NFA-OSC) by applying the electronic features | The suggested approach effectively extracted the non-linear maps among offset and Voc. Moreover, it predicted the linear interactions among the ground-reflected radius | However, the suggested approach lacked in reliable prediction as it did not consider factors such as illumination intensity, temperature, and the interfacial area |

Zambrano and Giraldo [70] | A methodological approach based on exogenous variables which correlated the solar irradiance with a multidimensional space | The suggested methodological approach effectively implemented the various stages of the solar PV power system | However, the prediction capability of the model was minimized due to the improper irradiances at unknown measurement values |

Waqas Khan et al. [71] | An improved generally applicable stacked ensemble algorithm known as DSE-XGB was introduced with the help of Artificial Neural Network (ANN) and Long Short Term Memeory (LSTM). The Extreme Gradient Boosting (XGB) algorithm was used to enhance the accuracy of PV generation | The suggested DSE-XGB approach does not rely on the input features and helps to handle the uncertainties that occur during forecast | However, the suggested approach does not select the trade-off among prediction gain and the evaluation time |

Su-Chang Lim [72] | A hybrid model which comprised a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for stable power generation forecasting | The CNN categorize the weather condition and the LSTM was used to stabilize the power forecast generation in an effective manner. | However, the suggested approach exhibited errors when it was evaluated in a peaky point with inappropriate patterns |

Catalin Stoean et al. [73] | The Long Short-Term Memory and a Bi-directional LSTM were introduced to perform data collection and solar energy generation. Moreover, the Reptile Search Algorithm (RSA) was used in the process of fine tuning the hyperparameters | The RSA algorithm models the time series data of the PV output which is accompanied with the exogenous weather | However, the suggested approach does not suit multi-objective time series forecasting |

**Table 3.**Research on the basis of ML and DL for power prediction, energy conversion, and forecasting for hydro and tidal energy.

Author | Proposed Method | Advantage | Limitations |
---|---|---|---|

Liu et al. [74] | The vertical profile approach for energy conversion in an ocean model based on slope shelf context due to barotropic tidal flow | The energy converted at thermocline plays a significant role in production of onshore energy radiation which is viable than the offshore radiation | However, the vertical approach varies with the bottom slopes and leads to fluctuation at the time of energy conversion |

Omidvar et al. [75] | An approach based on wave generation and conversion of energy near the shore which is caused as a result of tide to tide and wind to wind interaction | The interaction that occurred among wind and tides resulted in high conversion rates and internal wave frequencies | However, the energy conversion system requires extensive calibration and testing to provide stability to the model |

Shen et al. [76] | A micro power generation system which tends to generate hydroelectric energy for supplying water through intelligent networks | The integrated permanent magnet generator used in the micro power generation system minimalized the total operating hours and improved the efficiency | However, the storage capacity of the battery was limited which meant only a minimal amount of energy could be stored and the overload to the battery produced heat and severely affected the efficiency of the power generation system |

Sari and Sid [77] | A vulnerable hydroelectric generator system to monitor the deception attack using a modified Kalman filter | The suggested system effectively addresses the region which leads to packet loss and the Kalman filter leads to conserve the structural properties of the hydroelectric conversion system | The suggested hydroelectric generator system faces oscillations when the output of the power system stabilizer (PSS) is impacted by the attack |

Thomas Monahan et al. [78] | A hybrid model for short-term prediction of tidal currents based on Harmonic Residual Analysis (HRA). The HRA is combined with Linear Recurrent Forecasting (LRF) and High Order Fuzzy Time Series (HOFTS) | The automated LRF was used for automated selection of components with acute forecasting results | However, energy forecasting for more than the specified period using the suggestion results does not provide expected results |

Ryo Fujiwara et al. [79] | The tidal power generating system using Flaring Flanged Diffuser (FFD) to create power at an optimistic condition with increased velocity flow | The suggested approach performs based on the correlation among fluid velocity and enhances the outlet diameter; this helps to generate more power in a minimal time period | However, the installation cost of FFD was higher and needs to be periodically analyzed with human intervention |

Yen-Chang Chen et al. [80] | Ensemble Empirical Mode Decomposition (EEMD) and a stepwise regression model was introduced to forecast the water level of tidal river sources | The EEMD utilized in this research decomposes the signals of water levels from tidal river into several Intrinsic Mode Functions (IMFs) | However, the suggested approach cannot determine the factors that affects the IMF |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bhansali, A.; Narasimhulu, N.; Pérez de Prado, R.; Divakarachari, P.B.; Narayan, D.L.
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models. *Energies* **2023**, *16*, 6236.
https://doi.org/10.3390/en16176236

**AMA Style**

Bhansali A, Narasimhulu N, Pérez de Prado R, Divakarachari PB, Narayan DL.
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models. *Energies*. 2023; 16(17):6236.
https://doi.org/10.3390/en16176236

**Chicago/Turabian Style**

Bhansali, Ashok, Namala Narasimhulu, Rocío Pérez de Prado, Parameshachari Bidare Divakarachari, and Dayanand Lal Narayan.
2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models" *Energies* 16, no. 17: 6236.
https://doi.org/10.3390/en16176236