# State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

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## Abstract

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## 1. Introduction

## 2. Methodology of Survey

## 3. State of the Art of ML Models in Energy Systems

_{ti}represents the target value, x

_{pi}represents the predicted value, and n is the number of data points.

#### 3.1. ANN

_{2}emissions (million Tons). Based on Figure 4, the use of clean energy nearly doubled, which led to a reduction in CO

_{2}emissions from about 109 million tons to 38 million tons. Therefore the proposed case has an effective role compared with the base case.

#### 3.2. MLP

#### 3.3. ELM and Other Advanced ANNs

#### 3.4. SVM

#### 3.5. WNN

#### 3.6. ANFIS

#### 3.7. Decision Trees

#### 3.8. Deep Learning

#### 3.9. Ensemble Methods

#### 3.10. Hybrid ML Models

^{2}), which led to higher accuracy of predictions compared with other machine learning techniques. This claim is evident in Figure 29, which presents the average values of RMSE for the four developed techniques.

#### 3.11. Comparative Analysis of ML Models

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

## Acronyms

AR | Autoregressive |

ARMA | Autoregressive Moving Average |

ANNs | Artificial Neural Networks |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

BANN | Back Propagation Neural Network |

BP | Back Propagation |

CFS | Correlation based Feature Selection |

CGP | Cartesian Genetic Programming |

CRBM | Conditional Restricted Boltzmann Machines |

CRO | Coral Reefs Optimization |

CWNN | Convolutional-Wavelet Neural Networks |

DL | Deep Learning |

DOPH | Direct Optimum Parallel Hybrid |

DT | Decision trees |

EANN | Evolutionary Artificial neural networks |

EEMD | Ensemble Empirical Mode Decomposition |

ELM | Extreme Learning Machine |

EMD | Empirical Mode Decomposition |

FCRBM | Factored Conditional Restricted Boltzmann Machine |

FFNN | Feed Forward Neural Network |

FIS | Fuzzy inference system |

FOARBF | Fruit Fly Optimization Algorithm Radial Basis Function |

FOAGRNN | Fruit Fly Optimization Algorithm Generalized Regression Neural Networks |

FOASVR | Fruit Fly Optimization Algorithm Support Vector Regression |

GA | Genetic Algorithm |

GANN | Neural Networks by Genetic Algorithm |

GARCH | Generalized Autoregressive Conditional Heteroskedasticity |

GHG | Greenhouse Gas |

GFF | General Factorization Framework |

GP | Gaussian Processes |

GPR | Gaussian Processes Regression |

GRNN | Generalized Regression Neural Networks |

IDSS | Intelligent Decision Support System |

IoT | Internet of Things |

KELM | kernel-based extreme learning machine |

KFCM | Kernel Fuzzy C Means |

KNN | K-Nearest Neighbors |

LM | Levenberg-Marquardt |

MARS | Multivariate Adaptive Regression Splines |

ML | Machine Learning |

MLP | Multilayer Perceptron |

MR | Multi-Resolution |

MLR | Multiple linear regression |

MTL | Method of transmission lines |

MRWNN | Multi-Resolution Wavelet Neural Network |

NARX | Nonlinear Auto-Regressive with external input |

NDP | Neuro-Dynamic Programming |

NN | Neural Networks |

NWP | Numerical Weather Prediction |

OLR | Outgoing long-wave radiation |

PR | Persistence model |

PSO | Particle Swarm Optimization |

PV | Photovoltaic |

r | Correlation coefficient |

RBF | Radial Basis Function |

RBFNN | Radial Basis Function Neural Networks |

RF | Random Forests |

RMSE | Root Mean Squared Error |

RNN | Recurrent Neural Network |

SANN | Subsequent Artificial Neural Networks |

SAPSO | Self-Adaptive Particle Swarm Optimization |

SARIMA | Seasonal Autoregressive Integrated Moving Average |

SCADA | Supervisory Control and Data Acquisition |

SCG | Scaled Conjugate Gradient |

SE | Solar radiation |

SEAT | A car manufacturing plant in Spain |

SOC | State of Charge |

SOFM | Self-Organizing Feature Map |

SVM | Support Vector Machine |

SP | Coral Reefs Optimization algorithm with species |

SVR | Support vector regression |

WPRE | Wind Power Ramp Events |

WNN | Wavelet Neural Network |

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**Figure 4.**Results for study by Abbas et al. (2018). Reproduced from [29], Elsevier: 2018.

**Figure 7.**RMSE values for study by Chahkoutahi and Khashei (2017). Reproduced from [38], Elsevier 2017.

**Figure 11.**Correlation coefficient values for the study by Li et al. (2018). Reproduced from [44], Elsevier: 2018.

**Figure 16.**(

**a**) RMSE and (

**b**) correlation coefficient values for study by Bassam et al. (2017). Reproduced from [59], Elsevier: 2017.

**Figure 18.**(

**a**) RMSE and (

**b**) correlation coefficient values for study by Mocanu et al. (2016). Reproduced from [72], Elsevier: 2016.

**Figure 26.**(

**a**) RMSE and (

**b**) r values for study by Feng et al. (2017). Reproduced from [91], Elsevier: 2017.

**Figure 27.**(

**a**) RMSE and (

**b**) r values for study by Hassan et al. (2017). Reproduced from [92], Elsevier: 2017.

**Figure 28.**(

**a**) RMSE and (

**b**) r values for study by Salcedo-Sanz et al. (2018). Reproduced from [93], Elsevier: 2018.

**Figure 29.**RMSE values for study by Salcedo-Sanz et al. (2017). Reproduced from [94], Elsevier: 2017.

**Figure 30.**Correlation coefficient values for study by Touati et al. (2017). Reproduced from [95], the Elsevier: 2017.

**Figure 31.**RMSE values for study by Cornejo-Bueno et al. (2017). Reproduced from [98], Elsevier: 2017.

**Figure 32.**(

**a**) RMSE and (

**b**) r values for study by Khosravi et al. (2018). Reproduced from [99], Elsevier: 2018.

**Figure 33.**Correlation coefficient values for study by Pandit and Infield (2018). Reproduced from [101], Elsevier: 2018.

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Abbas et al. [29] | Electronics (Switzerland) | Optimization of renewable energy generation capacities |

2017 | Anwar et al. [30] | IEEE Transactions on Power Systems | Mitigation of wind power fluctuation and scheduling strategies for power generation |

2017 | Boukelia et al. [31] | Renewable Energy | Prediction of levelized cost of electricity |

2016 | Chatziagorakis et al. [32] | Neural Computing and Applications | A forecasting model for wind speed and hourly and daily solar radiation |

2018 | Gallagher et al. [33] | Energy and Buildings | Measurement and verification of energy savings in industrial buildings |

Year | Reference | Journal | Application |
---|---|---|---|

2015 | Ahmad et al. [37] | Solar Energy | A day ahead prediction of hourly global solar irradiation |

2017 | Chahkoutahi et al. [38] | Energy | Electricity load forecasting |

2017 | Kazem et al. [39] | Energy Conversion and Management | Prediction of solar system power output |

2017 | Loutfi et al. [40] | International Journal of Renewable Energy Research | Hourly global solar radiation prediction |

2017 | Shimray et al. [41] | Computational Intelligence and Neuroscience | Ranking of different potential power plant projects |

**Table 3.**Results of evaluations of models by Loutfi et al. (2017). Reproduced from [40], Elsevier: 2017.

ML Model | The Best Structure | r | f |
---|---|---|---|

MLP | 5-30-1 | 0.938 | 23.31 |

NARX | 1 5-10-1 | 0.974 | 15.1 |

Year | Reference | Journal | Application |
---|---|---|---|

2017 | Arat and Arslan [42] | Applied Thermal Engineering | Optimization of the district heating system aided with geothermal heat pump |

2015 | Bagnasco et al. [43] | Energy and Buildings | Electrical consumption forecasting model of a building |

2018 | Li, Q et al. [44] | Applied Energy | Forecasting of PV power generation |

2016 | Premalatha and Valan Arasu [45] | Journal of Applied Research and Technology | Monthly average global radiation prediction |

2014 | Yaïci and Entchev [46] | Applied Thermal Engineering | Performance forecasting of a solar thermal energy system |

Parameter | Model | RMSE | Parameter | Model | RMSE | Parameter | Model | RMSE |
---|---|---|---|---|---|---|---|---|

COP | LM-28 | 0.07506 | Cinst | LM-28 | 229,835 | WP2 | LM-50 | 58.3957 |

SCG-28 | 0.79114 | SCG-28 | 335,283 | SCG-50 | 606.962 | |||

CGP-28 | 0.77328 | CGP-28 | 345,108.6 | CGP-50 | 862.943 | |||

εsys | LM-28 | 0.00335 | Cop | LM-28 | 32,357.74 | WP3 | LM-50 | 1.14269 |

SCG-28 | 0.01616 | SCG-28 | 183,385.5 | SCG-50 | 13.3365 | |||

CGP-28 | 0.01963 | CGP-28 | 384,134.8 | CGP-50 | 22.8781 | |||

Wc | LM-50 | 98.07454 | NPV | LM-20 | 45,456.24 | Ηr | LM-50 | 25.2462 |

SCG-50 | 1044.513 | SCG-20 | 3,104,498 | SCG-50 | 294.616 | |||

CGP-50 | 1939.327 | CGP-20 | 20,565,006 | CGP-50 | 498.103 | |||

Qcon | LM-50 | 87.0961 | COPsys | LM-28 | 0.03824 | Qevp | LM-50 | 2.208349 |

SCG-50 | 1027.196 | SCG-28 | 0.31434 | SCG-50 | 465.1691 | |||

CGP-50 | 1754.37 | CGP-28 | 0.29927 | CGP-50 | 575.9355 |

Year | Authors | Journal | Application |
---|---|---|---|

2015 | Arabloo et al. [47] | Fuel | Estimation of optimum oxygen-steam ratios |

2013 | Arikan et al. [48] | International Review of Electrical Engineering | Classification of power quality disturbances |

2017 | Ma et al. [49] | IEEE Transactions on Circuits and Systems I: Regular Papers | Estimation of irradiance levels from photovoltaic electrical characteristics |

2016 | Özdemir et al. [50] | Neural Network World | Harmonic estimation of power quality in electrical energy systems |

2016 | Pinto et al. [51] | Neurocomputing | An electricity market price prediction in a fast execution time |

Year | Authors | Journal | Application |
---|---|---|---|

2016 | Doucoure et al. [53] | Renewable Energy | Prediction of time series for renewable energy sources |

2018 | Gu et al. [54] | Energy | Heat load perdition in district heating systems |

2018 | He et al. [55] | Applied Energy | Wind speed forecasting (reduction of the influence of noise in the raw data series) |

2018 | Qin et al. [56] | Algorithms | Simultaneous optimization of fuel economy and battery state of charge |

2017 | Sarshar et al. [57] | Energy | Uncertainty reduction in wind power prediction |

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Abdulwahid et al. [58] | Sustainability (Switzerland) | A protection device for a reverse power protection system |

2017 | Bassam et al. [59] | Sustainability (Switzerland) | Module temperature estimation of PV systems |

2018 | Kampouropoulos et al. [60] | IEEE Transactions on Smart Grid | Prediction of the power demand of a plant and optimization of energy flow |

2016 | Mohammadi et al. [61] | Renewable and Sustainable Energy Reviews | Identification of the most relevant parameters for forecasting of daily global solar radiation |

2016 | Sajjadi et al. [62] | Journal of the Taiwan Institute of Chemical Engineers | Transesterification yield estimation and prediction of biodiesel synthesis |

Year | Authors | Journal | Application |
---|---|---|---|

2018 | Aguado et al. [64] | IEEE Transactions on Smart Grid | Railway electric energy systems optimal operation |

2016 | Costa et al. [65] | Electric Power Systems Research | Security dispatch method for coupled natural gas and electric power networks |

2017 | Kamali et al. [66] | Applied Energy | Prediction of the risk of a blackout in electric energy systems |

2016 | Moutis et al. [67] | Applied Energy | energy storage planning and energy controlling |

2016 | Ottesen [68] | Energy | Total cost minimization in energy systems for the prosumers’ buildings |

Year | Authors | Journal | Application |
---|---|---|---|

2018 | Chemali et al. [69] | Journal of Power Sources | Battery State-of-charge estimation |

2017 | Coelho et al. [70] | Applied Energy | Household electricity demand forecasting |

2017 | Kim et al. [71] | Computational Intelligence and Neuroscience | Estimation of the power consumption of individual appliances in the distribution system |

2016 | Mocanu et al. [72] | Sustainable Energy, Grids, and Networks | Prediction of building energy consumption |

2017 | Wang et al. [73] | Energy Conversion and Management | PV power forecasting |

Year | Authors | Journal | Application |
---|---|---|---|

2015 | Burger and Moura [75] | Energy and Buildings | forecasting of building electricity demand |

2017 | Changfeng et al. | International Journal of Control and Automation | Non-linear fault features extraction |

2018 | Fu, G. [76] | Energy | Cooling load forecasting in buildings |

2015 | Gjoreski et al. [77] | Applied Soft Computing Journal | Human energy expenditure estimation |

2016 | Hasan and Twala [78] | International Journal of Innovative Computing, Information, and Control | Prediction of the underground water dam level |

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Deng et al. [80] | Journal of Renewable and Sustainable Energy | Short-term load forecasting in microgrids |

2016 | Dou et al. [81] | Electric Power Components and Systems | prediction of renewable energy loads in microgrids |

2016 | Peng et al. [82] | Energies | Electric load forecasting |

2016 | Qu et al. [83] | Advances in Meteorology | Reliable wind speed prediction |

2017 | Yang and Lian [84] | Applied Energy | Electricity price prediction |

2019 | Dehghani et al. [85] | Energies | Hydropower generation forecasting |

2017 | Mosavi et al. [86] | Intelligent Systems | General energy sectors |

Year | Reference | Journal | ML Model | Application |
---|---|---|---|---|

2016 | David et al. [90] | Solar Energy | Hybrid ARMA-GARCH model | Forecasting of the solar irradiance |

2017 | Feng et al. [91] | International Journal of Hydrogen Energy | GRNN, RF, ELM and optimized back propagation GANN | Estimating daily Hd |

2017 | Hassan et al. [92] | Applied Energy | Gradient boosting, RF and bagging | Modeling solar radiation |

2018 | Salcedo-Sanz et al. [93] | Applied Energy | A hybrid CRO-ELM model. | Estimation of daily global solar radiation in Queensland, Australia. |

2017 | Salcedo-Sanz et al. [94] | Renewable Energy | A hybrid CCRO-ELM model. | Global solar radiation prediction at a given point |

2017 | Touati et al. [95] | Renewable Energy | Hybrid of MA, AR and ARMA modeling | Forecasting the output power of PV panels in environmental conditions. |

2017 | Voyant et al. [96] | Energy | linear quadratic estimation | Prediction of solar yields |

2017 | Voyant et al. [97] | Energy | Hybrid of multilayer perceptron | Forecasting of global radiation time series |

**Table 14.**Results related to the study by David et al. (2016). Reproduced from [90], Elsevier 2016.

Method | RMSE |
---|---|

Recursive ARMA | 20.8% |

SVR | 20.8% |

NN | 20.6% |

AR | 21.3% |

**Table 15.**Results related to the study by Feng et al. (2017). Reproduced from [91], Elsevier: 2017.

Station | ML Model | RMSE | r |
---|---|---|---|

Beijing | ELM | 17.3 | 0.9196 |

GANN | 17.1 | 0.9209 | |

RF | 18.3 | 0.9102 | |

GRNN | 19.2 | 0.8902 | |

Iqbal | 32.9 | 0.8865 | |

Zhengzhou | ELM | 13.8 | 0.947 |

GANN | 13.4 | 0.9515 | |

RE | 15 | 0.9408 | |

GRNN | 16.5 | 0.9072 | |

Iqbal | 35.8 | 0.929 |

Year | Reference | Journal | ML model | Application |
---|---|---|---|---|

2017 | Cornejo-Bueno et al. [98] | Energies | SVR, MLP and ELM, GPs, ERA-Interim reanalysis | Accurate prediction of Wind Power Ramp Events |

2018 | Khosravi et al. [99] | Applied Energy | MLP, SVR, fuzzy inference system, ANFIS, and group model of data handling | Prediction of wind speed data for Osorio wind farm |

2017 | Burlando et al. [100] | International Journal of Renewable Energy Research | Hybrids of ANNs | Accurate wind power forecast |

2018 | Pandit et al. [101] | Energies | Hybrid of GP | Predictive condition monitoring |

2018 | Sharifian et al. [102] | Renewable Energy | PSO, the Type-2 fuzzy NN | The wind power accurate forecasting |

Year | Reference | Journal | ML Model | Application |
---|---|---|---|---|

2016 | Albert and Maasoumy [104] | Applied Energy | predictive segmentation technique | Predictive segmentation technique for energy companies |

2018 | Alobaidi et al. [105] | Applied Energy | Hybrids | Predicting the average daily energy consumption on a household level |

2016 | Benedetti et al. [106] | Applied Energy | Hybrids of ANN | Control of energy consumption in energy-intensive industries |

2018 | Chen et al. [107] | Energy | ensemble learning technique (feedforward deep networks and extreme gradient boosting forest) | Prediction of the household electricity consumption |

2018 | Kuroha et al. [108] | Energy and Buildings | Support Vector Regression, Particle Swarm Optimization, Predicted Mean Vote | Improving thermal comfort and reduction of electricity costs |

2018 | Torabi et al. [7,88] | Sustainable Energy | Hybrid ML models | Solar radiation forecasting |

ML Model | Complexity | User-Friendliness | Accuracy | Speed | Dataset Type |
---|---|---|---|---|---|

ANN | Reasonably high | Low | High | Reasonable | Historical |

MLP | Reasonable | Reasonable | Reasonably high | High | Historical |

ELM | Reasonable | Reasonably high | Reasonable | Reasonably high | Historical |

SVM | Reasonably high | Low | High | Low | Historical |

DT | Reasonable | Low | Reasonable | Reasonable | Historical |

DL | High | Reasonable | High | Reasonable | Historical |

Ensemble | High | Low | Reasonable | High | Historical |

WNN | Reasonable | Low | High | Low | Historical |

ANFIS | Reasonable | Reasonable | Reasonable | High | Historical |

Hybrids | Reasonable | High | High | High | Historical |

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**MDPI and ACS Style**

Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R.
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. *Energies* **2019**, *12*, 1301.
https://doi.org/10.3390/en12071301

**AMA Style**

Mosavi A, Salimi M, Faizollahzadeh Ardabili S, Rabczuk T, Shamshirband S, Varkonyi-Koczy AR.
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. *Energies*. 2019; 12(7):1301.
https://doi.org/10.3390/en12071301

**Chicago/Turabian Style**

Mosavi, Amir, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, and Annamaria R. Varkonyi-Koczy.
2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review" *Energies* 12, no. 7: 1301.
https://doi.org/10.3390/en12071301