# Machine Learning and Deep Learning in Energy Systems: A Review

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## Abstract

**:**

## 1. Introduction

## 2. The Main Applications of ML and DL in Energy Systems

#### 2.1. Energy Consumption and Demand Forecast

#### 2.2. Predicting the Output Power of Solar Systems

#### 2.3. Predicting the Output Power of Wind Systems

#### 2.4. Optimization

#### 2.5. Fault and Defect Detection

#### 2.6. Other Applications and Algorithms Comparison

_{2}emission in power grids [99], ranking of different potential power plant projects [100], crack detection in wind turbine blades [101], module temperature estimation of PV systems [102], and so on.

## 3. Machine Learning (ML)

#### 3.1. Types of ML

#### 3.1.1. Supervised Learning (SL)

#### 3.1.2. Unsupervised Learning (USL)

#### 3.1.3. Reinforcement Learning (RL)

#### 3.1.4. Semi-Supervised Learning (SSL)

#### 3.2. ML Algorithms

#### 3.2.1. Linear Regression (LR)

#### Simple Linear Regression (SLR)

#### Multiple Linear Regression (MLR)

#### 3.2.2. Logistic Regression (LOR)

#### 3.2.3. k Nearest Neighbor (kNN)

#### 3.2.4. Decision Tree (DT)

#### 3.2.5. Random Forest (RF)

#### 3.2.6. SVM/SVR

#### 3.2.7. Naive Bayes Classifier (NB)

#### 3.2.8. K-Means

## 4. Deep Learning (DL)

#### 4.1. DL Algorithms

#### 4.1.1. Artificial Neural Network (ANN)

Name of the Activation Function | Formula | Graphical Representation | Number of Equations |
---|---|---|---|

Linear | $f\left(w\right)=w$ | (3) | |

Sigmoid | $f\left(w\right)=\frac{1}{1+{e}^{-w}}$ | (4) | |

Hyperbolic tangent sigmoid (tanh-sig) | $f\left(w\right)=\mathrm{tan}\mathrm{h}\left(w\right)=\frac{{e}^{w}-{e}^{-w}}{{e}^{w}+{e}^{-w}}$ | (5) | |

Binary step | $f\left(w\right)=\{\begin{array}{c}1w0\\ 0w\le 0\end{array}$ | (6) | |

Rectified Linear Units (ReLU) | $f\left(w\right)=\{\begin{array}{c}wforw\ge 0\\ 0forw0\end{array}$ | (7) | |

Leaky ReLU | $f\left(w\right)=\{\begin{array}{c}wforw0\\ awforw0\end{array}$ | (8) | |

Exponential Linear Unit (ELU) | $f\left(w\right)=\{\begin{array}{c}wforw0\\ a\left({e}^{w}-1\right)forw0\end{array}$ | (9) | |

Gaussian Radial Basis | $f\left(w\right)=exp\left(\frac{-\Vert w-\omega \Vert}{2{\sigma}^{2}}\right)$ | (10) | |

Softmax | $\sigma {(\overrightarrow{z})}_{i}=\frac{{e}^{{z}_{i}}}{{{\displaystyle \sum}}_{j=1}^{k}{e}^{{z}_{j}}}$ | (11) |

#### 4.1.2. Convolutional Neural Network (CNN)

#### Input Layer

#### Convolutional Layer

#### Pooling Layer

#### Activation Function

#### Fully Connected Layer (FCL)

#### Loss Function

#### Back Propagation and Feedforward

#### 4.1.3. Recurrent Neural Network (RNN)

#### 4.1.4. Restricted Boltzmann Machine (RBM)

#### 4.1.5. Auto Encoder (AE)

#### 4.1.6. Deep Belief Neural Networks (DBN)

#### 4.1.7. Generative Adversarial Network (GAN)

#### 4.1.8. Adaptive Neuro-Fuzzy Inference System (ANFIS)

#### 4.1.9. Wavelet Neural Network (WNN)

#### 4.1.10. Radial Basis Neural Network (RBNN)

#### 4.1.11. General Regression Neural Network (GRNN)

#### 4.1.12. Extreme Learning Machine (ELM)

#### 4.1.13. Ensemble Learning (EL)

#### Boosting

#### Adaptive Boosting (AdaBoost)

#### Extreme Gradient Boost (XGBoost)

#### AdaBoost.MRT

#### Bagging

#### Stacking

#### 4.1.14. Hybrid Model (HM)

#### 4.1.15. Transfer Learning (TL)

## 5. Time Series (TS)

#### 5.1. TS Algorithms

#### 5.1.1. Moving Average (MA) & Exponential Smoothing (ES)

#### 5.1.2. Autoregressive Moving Average (ARMA)

#### 5.1.3. Autoregressive Integrated Moving Average (ARIMA)

#### 5.1.4. Case-Based Reasoning (CBR)

#### 5.1.5. Fuzzy Time SERIES (FTS)

#### 5.1.6. Grey Prediction Model (GPM)

#### 5.1.7. Prophet Model

## 6. Performance Evaluation Metrics

^{2}[272], MSE [106,113], MAE [3,106], RMSE [273,274], nRMSE

_{%}[275], MAPE [3,113], MBE [272], t-stat [43], CV-RMSE [276].

#### 6.1. Mean Squared Error (MSE)

#### 6.2. R-Squared (R^{2})

#### 6.3. Mean Absolute Error (MAE)

#### 6.4. Root Mean Square Error (RMSE)

#### 6.5. Normalised Root Mean Square Error (nRMSE)

#### 6.6. Mean Absolute Percentage Error (MAPE)

#### 6.7. Mean Bias Error (MBE)

#### 6.8. t-Statistics

#### 6.9. Coefficient of Variation of the Root Mean Square Error (CV-RMSE)

## 7. Conclusions

_{2}emission in power grids, crack detection in wind turbine blades, energy efficiency, and more have been mentioned. The results of this study indicate the further use of certain algorithms in particular fields. For example, in the optimization section, most of the articles written use the ANN algorithm. In the predictions section, SVM, ANN, and MLP algorithms are mainly used. In the fault detection section, the SVM algorithm is mainly used to perform part of the problem development process.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ML | Machine Learning |

DL | Deep Learning |

SL | Supervised Learning |

SSL | Semi-Supervised Learning |

ANN | Artificial Neural Network |

R2 | Coefficient of Determination |

DNN | Deep Neural Network |

CNN | Convolutional Neural Network |

CL | Convolutional Layer |

nRMSE% | Normalized Root Mean Square Error |

GAN | Generative Adversarial Network |

RNN | Recurrent Neural Network |

LSTM | Long-Short term Memory |

RBM | Restricted Boltzmann Machine |

RE | Reconstruction Error |

AE | Auto Encoder |

DBN | Deep Belief Networks |

GAN | Generative Adversarial Network |

ARMA | Autoregressive Moving Average |

ARIMA | Autoregressive Integrated Moving Average |

CBR | Case-Based Reasoning |

HM | Hybrid Model |

FCL | Fully Connected Layer |

GRNN | General Regression Neural Network |

TL | Transfer Learning |

LASSO | Least Absolute Shrinkage Selector Operator |

kNN | k Nearest Neighbor |

SVR | Support Vector Regression |

KELM | Kernel Extreme Learning Machine |

NARX | Nonlinear Autoregressive Exogenous |

NN | Neural Networks |

DNI | Direct Normal Irradiance |

GHI | Global Horizontal Irradiance |

ANFIS-FCM | ANFIS based on Fuzzy C-means Clustering |

ANFIS-SC | ANFIS based on Subtractive Clustering |

SP | Smart Persistence |

BT | Boosted-Tree |

MMI | Modified Mutual Information |

FCRBM | Factored Conditional Restricted Boltzmann Machine |

GWDO | Genetic Wind-Driven Optimization |

APSONN | Accelerated Particle Swarm Optimization Neural Network |

GANN | Genetic Algorithm Neural Network |

ABCNN | Artificial Bee Colony Neural Network |

MLR | Multiple Linear Regression |

GRU | Gated Recurrent Unit |

AEM | Actual Engineering Model |

GSA | Gravitational Search Algorithm |

ICBR | Improved Case Based Reasoning |

FDD | Fault Detection and Diagnosis |

IDA | Improved Dragonfly Algorithm |

MSSM | Mahalanobis Semi-Supervised Mapping |

SM | Surrogate Model |

HOA | Hybrid Optimization Algorithm |

LSSVM | Least Square SVM |

FA | Firefly Algorithm |

HVAC | Heating Ventilating and Air Conditioning |

PV | Photovoltaic |

EM | Energy Management |

LR | Linear Regression |

DT | Decision Tree |

WNN | Wavelet Neural Network |

BP | Back Propagation |

AIC | Akaike Information Criterion |

ReLU | Rectified Linear Unit |

GB | Gradient Boosting |

DA | Dragonfly algorithm |

ELU | Exponential Linear Unit |

DM | Discriminator Model |

GM | Generative Model |

USL | Unsupervised Learning |

RL | Reinforcement Learning |

EORV | Expectation of a Random Variable(Expected Value) |

UE | Uncorrelated Error |

NB | Naive Bayes |

WT | Wavelet Transform |

ELM | Extreme Learning Machine |

PCA | Principal Component Analysis |

SLFN | Single Hidden Layer Feed-Forward Neural Networks |

MAE | Mean Absolute Error |

MSE | Mean Squared Error |

MRE | Mean Relative Error |

MBE | Mean Bias Error |

MAPE | Mean Absolute Percentage Error |

RMSE | Root Mean Square Error |

MA | Moving Average |

AR | Autoregressive |

ES | Exponential Smoothing |

FTS | Fuzzy Time Series |

MLP | Multilayer Perceptron Network |

GPM | Gray Prediction Model |

TS | Time Series |

SVM | Support Vector Machine |

XGBoost | eXtreme Gradient Boost |

RF | Random Forest |

NWP | Numerical Weather Prediction |

WRF | Weather Research and Forecasting |

CIADCast | Cloud Index Advection and Diffusion |

GBT | Gradient Boosting Tree |

MLPNN | Multi Layer Perceptron Neural Network |

ANFIS | Adaptive Neuro-Fuzzy Inference Systems |

MARS | Multivariate Adaptive Regression Spline |

CART | classification and regression tree |

MI-ANN | Mutual Information-Based Artificial Neural Network |

AFC-ANN | Accurate and Fast Converging based on ANN |

CSNN | Cuckoo Search Neural Network |

CS | Cuckoo Search |

OPEC | Organization of Petroleum Exporting Countries |

GARCH | Generalized Autoregressive Conditional Heteroscedasticity |

EMD | Empirical Mode Decomposition |

PSO | Particle Swarm Optimization |

GA | Genetic Algorithm |

DRNN | Deep Recurrent Neural Network |

SMTL | Surrogate Model trained using Transfer Learning |

VPSO | Vibration Particle Swarm Optimization |

DST | Decision Support Tool |

PNN | Probabilistic Neural Networks |

LMD | Local Mean Decomposition |

BAS-SVM | Beetle Antennae Search based Support Vector Machine |

SMANN | Surrogate Model trained using ANN |

EM | Energy Management |

GPR | Gaussian Process Regression |

RBNN | Radial Basis Neural Network |

AI | Artificial Intelligence |

EWT | Empirical Wavelet Transform |

EL | Ensemble Learning |

SLR | Simple Linear Regression |

LOR | Logistic Regression |

RBF | Radial Basis Function |

AdaBoost | Adaptive Boosting |

IoT | Internet of Things |

CA | Cluster Analysis |

GP | Gaussian Processes |

FDA | Fischer Discriminant Analysis |

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**Figure 1.**Frequency chart of the number of articles related to ML and DL in the field of energy systems [12].

**Figure 4.**A flowchart example of a DT [130].

**Figure 5.**An ANN type with a hidden layer [151].

**Figure 6.**A CNN has several CLs and PLs with ANN at the end [164].

**Figure 7.**The structure of RNN [169].

**Figure 8.**The structure of LSTM [169].

**Figure 9.**The structure of RBM [174].

**Figure 10.**The structure of a deep AE [179].

**Figure 12.**The structure of DBN [186].

**Figure 13.**The process of data processing in GAN [190].

**Figure 14.**The structure of ANFIS [197].

**Figure 15.**The structure of RBNN [203].

**Figure 19.**The Process of CBR [259].

Year | Reference | The Algorithms Investigated in This Study | Application |
---|---|---|---|

2017 | Deb et al. [22] | SVM, MA & ES, CBR, NN, ARIMA, Grey, HM, ANN, Fuzzy | Energy consumption and demand forecast |

2018 | Amasyali et al. [21] | SVM, ANN, LSSVM, DT, GLR, MLR, FFNN, LASSO, NARIX, PENN, GRNN, ARIMA, AR, BN, CBR, RBF, MARS, ELM | |

2019 | Beyca et al. [34] | MLR, SVR, ANN | |

2020 | Walker et al. [23] | ANN, SVM, RF, BT | |

Grimaldo et al. [24] | kNN | ||

Haq et al. [25] | SVM, ANN, K-mean | ||

Hafeez et al. [26] | FCRBM | ||

Khan et al. [27] | CSNNN | ||

Kazemzadeh et al. [28] | PSO-SVR, ANN, ARIMA, HM | ||

Fathi et al. [29] | MLR, ANN, SVR, GA, RF, CA, BN, GP, GB, PCA, DL, RL, ARIMA, ENS | ||

Liu et al. [30] | SVM | ||

Kaytez et al. [31] | LSSVM, ARIMA, HM, MLR | ||

Fan et al. [32] | EMD-SVR-PSO-AR-GARCH, EMD-SVR-AR, SVR-GA, AR-GARCH, ARMA | ||

Wen et al. [35] | DRNN-GRU, DRNN-LSTM, DRNN, MLP, ARIMA, SVM, MLR | ||

Jamil [33] | ARIMA | ||

2017 | Voyant et al. [6] | LR, GLM, ANN, SVR/SVM, DT, kNN, Markov Chain, HM, ARIMA | Predicting the output power of solar systems |

2019 | Srivastava et al. [45] | RF, CART, MARS, M5 | |

Benali et al. [46] | ANN, RF, SP | ||

2020 | Huertas-Tato et al. [41] | SVR-HM | |

Gürel et al. [43] | ANN | ||

Alizamir et al. [44] | GBT, MLPNN, ANFIS-FCM, ANFIS-SC, MARS, CART | ||

2021 | Govindasamy et al. [42] | ANN, SVR, GRNN, RF | |

Khosravi et al. [47] | SVM, ANN, DL, kNN | ||

2015 | Wang et al. [52] | ARIMA, SVM, ELM, EWT, LSSVM, GPR, HM | Predicting the output power of wind systems |

2016 | Cadenas et al. [55] | ARIMA, NARX | |

2018 | Zendehboudi et al. [51] | SVM-HM, ANN, SVM | |

2019 | Demolli et al. [53] | LASSO, kNN, RF, XGBoost, SVR | |

2020 | Li et al. [56] | IDA-SVM, DA-SVM, GA-SVM, Grid-SVM, GPR, BPNN | |

Tian et al. [57] | LSSVM, HM, LMD | ||

Hong et al. [58] | CNN | ||

2021 | Xiao et al. [54] | ANN, KELM | |

2018 | Abbas et al. [71] | ANN-GA | Optimization |

2019 | Perera et al. [66] | TL, HM | |

Wen et al. [74] | ANN, GABP-ANN | ||

Zhou et al. [68] | ANN, PSO | ||

2020 | Ilbeigi et al. [69] | ANN, MLP, GA, HM | |

Naserbegi et al. [70] | GSA-ANN | ||

Xu et al. [73] | VPSO, ANN, ANFIS, ANFIS-VPSO, ICBR | ||

2021 | Li et al. [72] | ANN-GA, CFD-GA | |

Ikeda et al. [67] | DNN, HM | ||

2018 | Zhao et al. [8] | AE, MLP, CNN, DBN | Fault and defect detection |

2019 | Wang et al. [83] | LSSVM, SVM, PNN | |

Han et al. [84] | ANN, SVM, PCA, BN, SVR, Fuzzy | ||

Helbinget al. [85] | SV-PSO, BPNN, ANFIS | ||

Sarwar et al. [87] | SVM, kNN, NB | ||

Wang et al. [86] | SVM, PCA, FDA | ||

2020 | Yang et al. [81] | RF, DT, kNN | |

Choi et al. [82] | BAS-SVM, SVM, PSO-SVM, GA-SVM, ABS-SVM | ||

Rivas et al. [80] | SVM | ||

Eskandari et al. [88] | kNN, SVM, RF, EL | ||

Han et al. [89] | ANFIS-BWOA, AR |

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Forootan, M.M.; Larki, I.; Zahedi, R.; Ahmadi, A.
Machine Learning and Deep Learning in Energy Systems: A Review. *Sustainability* **2022**, *14*, 4832.
https://doi.org/10.3390/su14084832

**AMA Style**

Forootan MM, Larki I, Zahedi R, Ahmadi A.
Machine Learning and Deep Learning in Energy Systems: A Review. *Sustainability*. 2022; 14(8):4832.
https://doi.org/10.3390/su14084832

**Chicago/Turabian Style**

Forootan, Mohammad Mahdi, Iman Larki, Rahim Zahedi, and Abolfazl Ahmadi.
2022. "Machine Learning and Deep Learning in Energy Systems: A Review" *Sustainability* 14, no. 8: 4832.
https://doi.org/10.3390/su14084832