# An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Proposed Method

**Data collection:**The first step is to gather relevant data on wind and solar energy production. This data can be obtained from various sources, including meteorological data, weather forecasts, and actual wind and solar power-production data from existing wind and solar farms. The data used for our model was collected for first 200 days of 2022.**Data preprocessing:**The collected data must be preprocessed to remove any outliers, inconsistencies, and missing values. This step is crucial for ensuring the accuracy of any machine-learning model. The training dataset was normalized to fall between [0] and [1].**Feature selection:**The next step is to select the relevant features that are most indicative of wind and solar energy production. This can be performed by using various feature-selection techniques, such as correlation analysis and mutual information. Weibull distribution was utilized in our model to assess product dependability, model failure rates, and life data.**Model selection:**Based on the selected features, a suitable machine-learning model must be chosen. This can be a regression model, decision tree, random forest, or neural network, among others. The choice of the model depends on the complexity of the data, the number of features, and the desired prediction accuracy. The model chosen in this study was artificial neural network (ANN) due to its capacity to analyze and handle non-linearity in data pertaining to wind–solar power generation.**Model training**: The selected model must be trained on the preprocessed data using an appropriate training algorithm, such as gradient descent or backpropagation. The model must be trained until the prediction accuracy is satisfactory. The proposed model utilizes a feedforward back propagation neural network (FFBPN-net) [45]. The primary reason for the selection of a feed-forward back propagation network (FFBPN) is to discover and map the connections between inputs and outputs. In order to attain the least amount of error, a system’s weight values and threshold values are also adjusted using the FFBPN learning rule. The model was trained on FFBPN Equation (1) until the required criteria were met.$${x}_{k}=\sum _{i}^{n}{w}_{ki}{x}_{i}$$_{ki}is the weight link value between the neuron and the variable, ${x}_{i}$ is the variable’s initial value, and x_{k}is its updated value. The logsig activation function in Equation (2) was used between the input layer and the hidden layer.$$f\left(x\right)=\frac{1}{1+{e}^{-x}}$$The linear function was used as an activation function between the hidden and output layer, as it just returns the value without making any changes to the weighted sum of the input.**Model validation:**The trained model must be validated using a validation dataset to ensure that the model is not overfitting the training data. This step is crucial to ensure that the model can generalize well to the unseen data. In our model, the training set and testing set were split at a ratio of 0.7 to 0.3.**Model deployment:**Once the model has been trained and validated, it can be deployed in the wind–solar hybrid system. The model can be used to predict wind and solar energy production for a given set of weather conditions and to optimize the operation of the hybrid system.**Model monitoring and maintenance:**The performance of the deployed model must be monitored regularly, and any necessary updates or improvements must be made to ensure that the model continues to perform well over time. Mean absolute percentage error (MAPE) is the most widely used performance measure for energy-generation prediction models, which is also used in this study.

#### 3.1. Levenberg–Marquardt vs. Gauss Newton

#### 3.2. Data Used for Training and Validation

#### 3.2.1. Temperature

#### 3.2.2. Air Pressure

#### 3.2.3. Relative Humidity

#### 3.3. Weibull Distribution

#### 3.4. Wind-Speed Prediction Using ANN

## 4. Wind–Solar Hybrid System Modeling

- An uninterrupted power supply is ensured by the combination of two or more energy-generating technologies and the integration of energy storage technology.
- Enhanced dependability: installing two or more energy systems improves the system’s dependability.
- Reduced carbon footprint by using fewer fossil fuels; hybrid systems have a smaller carbon impact.

#### 4.1. Solar-Panel-Array Modeling

#### 4.2. Solar Radiance

#### 4.3. Power Produced by Wind Turbines

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Kumar, R.; Bansal, H.O. Shunt active power filter: Current status of control techniques and its integration to renewable energy sources. Sustain. Cities Soc.
**2018**, 42, 574–592. [Google Scholar] [CrossRef] - Agency, I.E. Energy, Climate Change and Environment: 2016 Insights; International Energy Agency: Paris, France, 2016. [Google Scholar]
- Chang, T.P.; Liu, F.J.; Ko, H.H.; Cheng, S.P.; Sun, L.C.; Kuo, S.C. Comparative analysis on power curve models of wind turbine generator in estimating capacity factor. Energy
**2014**, 73, 88–95. [Google Scholar] [CrossRef] - Fath, H.E. Solar distillation: A promising alternative for water provision with free energy, simple technology and a clean environment. Desalination
**1998**, 116, 45–56. [Google Scholar] [CrossRef] - Kumar, K.V.; Bai, R.K. Performance study on solar still with enhanced condensation. Desalination
**2008**, 230, 51–61. [Google Scholar] [CrossRef] - Maalej, A. Solar still performance. Desalination
**1991**, 82, 197–205. [Google Scholar] [CrossRef] - Huang, Q.; Shi, Y.; Wang, Y.; Lu, L.; Cui, Y. Multi-turbine wind-solar hybrid system. Renew. Energy
**2015**, 76, 401–407. [Google Scholar] [CrossRef] - Celik, A.; Muneer, T.; Clarke, P. An investigation into micro wind energy systems for their utilization in urban areas and their life cycle assessment. Proc. Inst. Mech. Eng. Part A J. Power Energy
**2007**, 221, 1107–1117. [Google Scholar] [CrossRef] - Chen, H.C. Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability. Appl. Energy
**2013**, 103, 155–164. [Google Scholar] [CrossRef] - International Renewable Energy Agency. Renewable Capacity Statistics 2022. Available online: https://www.irena.org/publications/2022/Apr/Renewable-Capacity-Statistics-2022 (accessed on 21 December 2022).
- Cameron, L.; Van Der Zwaan, B. Employment factors for wind and solar energy technologies: A literature review. Renew. Sustain. Energy Rev.
**2015**, 45, 160–172. [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] - Pombo, D.V.; Bindner, H.W.; Spataru, S.V.; Sørensen, P.E.; Rygaard, M. Machine learning-driven energy management of a hybrid nuclear-wind-solar-desalination plant. Desalination
**2022**, 537, 115871. [Google Scholar] [CrossRef] - Abdmouleh, Z.; Alammari, R.A.; Gastli, A. Review of policies encouraging renewable energy integration & best practices. Renew. Sustain. Energy Rev.
**2015**, 45, 249–262. [Google Scholar] - Hong, T.; Wang, P. Fuzzy interaction regression for short term load forecasting. Fuzzy Optim. Decis. Mak.
**2014**, 13, 91–103. [Google Scholar] [CrossRef] - Heydari, A.; Garcia, D.A.; Keynia, F.; Bisegna, F.; De Santoli, L. A novel composite neural network based method for wind and solar power forecasting in microgrids. Appl. Energy
**2019**, 251, 113353. [Google Scholar] [CrossRef] - Han, S.; Qiao, Y.H.; Yan, J.; Liu, Y.Q.; Li, L.; Wang, Z. Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network. Appl. Energy
**2019**, 239, 181–191. [Google Scholar] [CrossRef] - Ali, S.; Jang, C.M. Evaluation of PV-wind hybrid energy system for a small island. In Wind Solar Hybrid Renewable Energy System; IntechOpen: London, UK, 2019. [Google Scholar]
- Abualigah, L.; Zitar, R.A.; Almotairi, K.H.; Hussein, A.M.; Abd Elaziz, M.; Nikoo, M.R.; Gandomi, A.H. Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning techniques. Energies
**2022**, 15, 578. [Google Scholar] [CrossRef] - Mezzai, N.; Belaid, S.; Rekioua, D.; Rekioua, T. Optimization, design and control of a photovoltaic/wind turbine/battery system in Mediterranean climate conditions. Bull. Electr. Eng. Inform.
**2022**, 11, 2938–2948. [Google Scholar] [CrossRef] - Susmitha, P.; Parventhan, K.; Umamaheswari, S. Artificial Neural Network Control for Solar—Wind Based Micro Grid. In Proceedings of the 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 16–17 October 2022; pp. 1–6. [Google Scholar] [CrossRef]
- Sahoo, S.; Amirthalakshmi, T.M.; Ramesh, S.; Ramkumar, G.; Dhanraj, J.A.; Ranjith, A.; Obaid, S.A.; Alfarraj, S.; Kumar, S.S. Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management. Int. J. Photoenergy
**2022**, 2022, 9353470. [Google Scholar] [CrossRef] - Drir, N.; Chekired, F.; Rekioua, D. An integrated neural network for the dynamic domestic energy management of a solar house. Int. Trans. Electr. Energy Syst.
**2021**, 31, e13227. [Google Scholar] [CrossRef] - Reder, M.; Yürüşen, N.Y.; Melero, J.J. Data-driven learning framework for associating weather conditions and wind turbine failures. Reliab. Eng. Syst. Saf.
**2018**, 169, 554–569. [Google Scholar] [CrossRef] - Colone, L.; Reder, M.; Tautz-Weinert, J.; Melero, J.J.; Natarajan, A.; Watson, S.J. Optimisation of data acquisition in wind turbines with data-driven conversion functions for sensor measurements. Energy Procedia
**2017**, 137, 571–578. [Google Scholar] [CrossRef] - Pedersen, M.C.; Sørensen, H.; Swytink-Binnema, N.; Martinez, B.; Condra, T. Measurements from a cold climate site in Canada: Boundary conditions and verification methods for CFD icing models for wind turbines. Cold Reg. Sci. Technol.
**2018**, 147, 11–21. [Google Scholar] [CrossRef] - Reikard, G. Predicting solar radiation at high resolutions: A comparison of time series forecasts. Sol. Energy
**2009**, 83, 342–349. [Google Scholar] [CrossRef] - Palomares-Salas, J.; De La Rosa, J.; Ramiro, J.; Melgar, J.; Aguera, A.; Moreno, A. ARIMA vs. Neural networks for wind speed forecasting. In Proceedings of the 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Hong Kong, China, 11–13 May 2009; pp. 129–133. [Google Scholar]
- Li, H.; Li, R.; Zhao, Y. Wind speed forecasting based on autoregressive moving average- exponential generalized autoregressive conditional heteroscedasticity-generalized error distribution (ARMA-EGARCH-GED) model. Int. J. Phys. Sci.
**2011**, 6, 6867–6871. [Google Scholar] - Hejase, H.A.; Assi, A.H. Time-series regression model for prediction of mean daily global solar radiation in Al-Ain, UAE. Int. Sch. Res. Not.
**2012**, 2012, 412471. [Google Scholar] [CrossRef] - Wang, H.Z.; Li, G.Q.; Wang, G.B.; Peng, J.C.; Jiang, H.; Liu, Y.T. Deep learning based ensemble approach for probabilistic wind power forecasting. Appl. Energy
**2017**, 188, 56–70. [Google Scholar] [CrossRef] - Wang, H.; Wang, G.; Li, G.; Peng, J.; Liu, Y. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl. Energy
**2016**, 182, 80–93. [Google Scholar] [CrossRef] - Hu, Q.; Zhang, R.; Zhou, Y. Transfer learning for short-term wind speed prediction with deep neural networks. Renew. Energy
**2016**, 85, 83–95. [Google Scholar] [CrossRef] - Liu, H.; Mi, X.W.; Li, Y.F. Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers. Manag.
**2018**, 156, 498–514. [Google Scholar] [CrossRef] - Yu, R.; Liu, Z.; Li, X.; Lu, W.; Ma, D.; Yu, M.; Wang, J.; Li, B. Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space. Appl. Energy
**2019**, 238, 249–257. [Google Scholar] [CrossRef] - Hong, Y.Y.; Satriani, T.R.A. Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network. Energy
**2020**, 209, 118441. [Google Scholar] [CrossRef] - Ding, Z.; Hou, H.; Yu, G.; Hu, E.; Duan, L.; Zhao, J. Performance analysis of a wind-solar hybrid power generation system. Energy Convers. Manag.
**2019**, 181, 223–234. [Google Scholar] [CrossRef] - Wind Energy Technologies Office. Wind Market Reports: 2022 Edition. Available online: https://www.energy.gov/eere/wind/wind-market-reports-2022-edition (accessed on 21 December 2022).
- International Energy Agency. Solar PV—Analysis. 2022. Available online: https://www.iea.org/reports/solar-pv (accessed on 21 December 2022).
- International Energy Agency. Next Generation Wind and Solar Power (Full Report). 2022. Available online: https://www.iea.org/reports/next-generation-wind-and-solar-power-full-report (accessed on 21 December 2022).
- Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science
**2015**, 349, 255–260. [Google Scholar] [CrossRef] - Alternative Energy Development Board (AEDB). Current Status of Solar PV Power Projects. 2023. Available online: https://www.aedb.org/ae-technologies/solar-power/solar-current-status (accessed on 4 April 2023).
- Alternative Energy Development Board (AEDB). Current Status of Wind Power Projects. Available online: https://www.aedb.org/ae-technologies/wind-power/wind-current-status (accessed on 4 April 2023).
- Brody, S.D.; Zahran, S.; Vedlitz, A.; Grover, H. Examining the relationship between physical vulnerability and public perceptions of global climate change in the United States. Environ. Behav.
**2008**, 40, 72–95. [Google Scholar] [CrossRef] - Shaik, N.B.; Pedapati, S.R.; Taqvi, S.A.A.; Othman, A.R.; Dzubir, F.A.A. A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline. Processes
**2020**, 8, 661. [Google Scholar] [CrossRef] - Shafi, I.; Ahmad, J.; Shah, S.I.; Kashif, F.M. Techniques to obtain good resolution and concentrated time-frequency distributions: A review. EURASIP J. Adv. Signal Process.
**2009**, 2009, 673539. [Google Scholar] [CrossRef] - Pavlić, B.; Bera, O.; Vidović, S.; Ilić, L.; Zeković, Z. Extraction kinetics and ANN simulation of supercritical fluid extraction of sage herbal dust. J. Supercrit. Fluids
**2017**, 130, 327–336. [Google Scholar] [CrossRef] - Shafi, I.; Noman, M.; Gohar, M.; Ahmad, A.; Khan, M.; Din, S.; Ahmad, S.H.; Ahmad, J. An adaptive hybrid fuzzy-wavelet approach for image steganography using bit reduction and pixel adjustment. Soft Comput.
**2018**, 22, 1555–1567. [Google Scholar] [CrossRef] - Weather Spark. Climate and Average Weather Year Round in Thatta, Pakistan. 2022. Available online: https://weatherspark.com/y/106464/Average-Weather-in-Thatta-Pakistan-Year-Round (accessed on 21 December 2022).
- Gupta, A.; Bansal, A.; Roy, K. Solar energy prediction using decision tree regressor. In Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 6–8 May 2021; pp. 489–495. [Google Scholar]
- Xia, X.; Wang, X. A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM. Complexity
**2022**, 2022, 4014048. [Google Scholar] [CrossRef] - Kani, S.P.; Ardehali, M. Very short-term wind speed prediction: A new artificial neural network–Markov chain model. Energy Convers. Manag.
**2011**, 52, 738–745. [Google Scholar] [CrossRef] - Grassi, G.; Vecchio, P. Wind energy prediction using a two-hidden layer neural network. Commun. Nonlinear Sci. Numer. Simul.
**2010**, 15, 2262–2266. [Google Scholar] [CrossRef] - Khatib, T.; Mohamed, A.; Sopian, K.; Mahmoud, M. Solar energy prediction for Malaysia using artificial neural networks. Int. J. Photoenergy
**2012**, 2012, 419504. [Google Scholar] [CrossRef] - Barua, P.; Barua, R. Machine learning based solar energy forecasting and wind-solar based hybrid grid arrangement at Patenga coastal area, Bangladesh. In Proceedings of the 2021 IEEE International Conference on Power, Electrical, Electronic and Industrial Applications (PEEIACON), Dhaka, Bangladesh, 3–4 December 2021; pp. 53–57. [Google Scholar]
- Sanjari, M.J.; Gooi, H.B.; Nair, N.K.C. Power generation forecast of hybrid PV–wind system. IEEE Trans. Sustain. Energy
**2019**, 11, 703–712. [Google Scholar] [CrossRef] - Lei, M.; Shiyan, L.; Chuanwen, J.; Hongling, L.; Yan, Z. A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev.
**2009**, 13, 915–920. [Google Scholar] [CrossRef] - Bauer, L.; Matysk, S. Compare Power Curves of Wind Turbines. 2023. Available online: https://en.wind-turbine-models.com/powercurves (accessed on 4 April 2023).

**Figure 5.**(

**a**) Comparison of predicted wind speeds (at 12-h intervals) using different hidden neurons, and (

**b**) predicted wind speeds (daily basis) using optimized hidden neurons and layers.

Ref. | MAPE | Inputs | Model | Wind/Solar |
---|---|---|---|---|

[50] | 1.57% | Solar energy, grid availability, equipment availability, solar irradiation | Decision tree | Solar only |

[51] | 6.68% | Wind speed | Twice decomposition, phase space reconstruction (PSR), and improved multiverse optimizer-extreme learning machine (IMVO-ELM) | Wind only |

[52] | 3.1439 | Wind speed | Artificial neural network (ANN)–Markov chain (MC) | Wind only |

[53] | 1.56% | Wind speed, relative humidity, generation hours, temperature, maintenance hours | 2-hidden layers ANN | Wind only |

[54] | 9.8% | Solar irradiation, clearness index, latitude, longitude, day number, sunshine ratio | 1-hidden layer ANN | Solar only |

[55] | 2.19% | Solar radiation and wind speed | Random forest, Catboost | Wind–solar |

[56] | 4.0% | Solar radiation, wind speed, ambient temperature and relative humidity | Higher order multivariate Markov chain | Wind–solar |

Proposed model | 2.08% | Wind speed, absolute temperature, relative humidity, air pressure, solar irradiance, optimal angle | Learn GDM, Weibull distribution, 1-hidden Layer ANN | Wind–solar |

Month | $\mathit{\theta}$ | Monthly kW/m${}^{2}$ |
---|---|---|

January | 57 | 209.6 |

February | 48 | 198 |

March | 43 | 229.9 |

April | 18 | 232 |

May | 6 | 250.8 |

June | 0 | 248 |

July | 5 | 251 |

August | 13 | 238 |

September | 28 | 218.6 |

October | 43 | 216 |

November | 54 | 200 |

December | 59 | 202 |

Yearly radiation kW/m${}^{2}$ | 2694 |

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**

Shafi, I.; Khan, H.; Farooq, M.S.; Diez, I.d.l.T.; Miró, Y.; Galán, J.C.; Ashraf, I.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation. *Energies* **2023**, *16*, 4171.
https://doi.org/10.3390/en16104171

**AMA Style**

Shafi I, Khan H, Farooq MS, Diez IdlT, Miró Y, Galán JC, Ashraf I.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation. *Energies*. 2023; 16(10):4171.
https://doi.org/10.3390/en16104171

**Chicago/Turabian Style**

Shafi, Imran, Harris Khan, Muhammad Siddique Farooq, Isabel de la Torre Diez, Yini Miró, Juan Castanedo Galán, and Imran Ashraf.
2023. "An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation" *Energies* 16, no. 10: 4171.
https://doi.org/10.3390/en16104171