# Applications of Artificial Intelligence Algorithms in the Energy Sector

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

- machine learning,
- metaheuristic algorithms,
- fuzzy inference systems.

- cybersecurity of intelligent energy management systems,
- energy saving,
- Smart Grid management,
- fault diagnosis in energy systems,
- electricity load forecasting,
- renewable energy.

## 3. Overview of AI Algorithms

#### 3.1. Machine Learning

- supervised learning,
- unsupervised learning,
- semi-supervised learning,
- reinforcement learning.

#### 3.1.1. Decision Trees

#### 3.1.2. Artificial Neural Networks

#### 3.1.3. Deep Learning

#### 3.1.4. Support Vector Machines

#### 3.1.5. K-Means Clustering Algorithms

#### 3.1.6. Regression Algorithms

#### 3.1.7. Self-Organizing Maps

#### 3.1.8. Hidden Markov Model

#### 3.2. Metaheuristics

#### 3.2.1. Evolutionary Algorithms

- evolutionary programming (EP),
- genetic algorithms (GA),
- evolutionary strategies (ES),
- genetic programming (GP).

#### 3.2.2. Swarm Intelligence

- particle swarm optimization (PSO),
- bee colony optimization (BCO),
- ant colony optimization (ACO),
- bat algorithm (BA).

#### 3.2.3. Gray Wolf Optimization

#### 3.2.4. Simulated Annealing Algorithm

#### 3.3. Fuzzy Inference Systems

- fuzzification—based on the membership function, the degree of truth for each premise of the rule is determined;
- aggregation—using the appropriate operators, the degrees of all the premises of the rule are combined if there is more than one premise of the rule “anded” together;
- inference—based on the assignment of one fuzzy subset to the output variables for the defined rules, the inference process is carried out using appropriate operators;
- composition—a single fuzzy subset is created for each output variable by combining all fuzzy subsets assigned to that variable;
- defuzzification—optional step if it is reasonable to convert the fuzzy output to a crisp number.

## 4. AI Algorithms for Engineering Problems in the Energy Sector

#### 4.1. Cybersecurity

#### 4.2. Energy Saving and Power Loss Reduction

#### 4.3. Smart Grid Management

#### 4.4. Forecasting Electricity Loads

#### 4.5. Fault Diagnosis in Energy Systems

#### 4.6. Renewable Energy

## 5. Open Research Challenges

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

AbB | Adaptive boosting |

ACO | Ant colony optimization |

AHP | Analytic hierarchy process |

AI | Artificial intelligence |

ANFIS | Adaptive neuro-fuzzy inference system |

ANN | Artificial neural network |

BA | Bat algorithm |

BBA | Binary bat algorithm |

BCO | Bee colony optimization |

CBA | Chaotic bat algorithm |

CEV | Connected electric vehicle |

CNN | Convolutional neural network |

CPS | Cyber-physical systems |

dBA | Directional bat algorithm |

DBN | Deep belief network |

DDPG | Deep deterministic policy gradient |

DG | Distributed generation |

DL | Deep learning |

DLR | Deep-learning regression |

DNN | Deep neural network |

DQN | Deep Q networks |

DRL | Deep reinforcement learning |

DT | Decision tree |

EA | Evolutionary algorithm |

eIoT | Energy Internet of Things |

ELM | Extreme learning machines |

EP | Evolutionary programming |

ES | Evolutionary strategies |

ETC | Extra-tree classifier |

GA | Genetic algorithms |

GAN | Generative adversarial network |

GRNN | General regression neural network |

GRU | Gate recurrent unit |

GTB | Gradient tree boosting |

GWO | Gray wolf optimization |

HMM | Hidden Markov model |

HVAC | Heating, ventilation, air conditioning |

iBA | Island bat algorithm |

k-NN | k-Nearest neighbor |

KRR | Kernel ridge regression |

LSTM | Long short-term memory network |

MDP | Markov decision process |

ML | Machine learning |

MLP | Multi-layer perceptron |

MVEW-DNN | Multi-view ensemble width-depth neural network |

MWPCA | Moving window principal component analysis method |

NB | Naive Bayes |

PCA | Principal component analysis |

PSO | Particle swarm optimization |

RF | Random forest |

RL | Reinforcement learning |

RNN | Recurrent neural network |

RQ | Research question |

SA | Simulated annealing |

SOM | Self-organizing map |

SVM | Support vector machine |

SVR | Support vector regression |

TVAC | Time-varying acceleration coefficient |

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Cybersecurity Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Detection of cyber-physical anomalies | Data propagation between generators within one Balancing Authority and behavior correlation | k-means clustering | Wang and Govindarasu [93] |

Detection of cyberattacks and disturbances in power grids | Prediction based on historical data and logs collected by phasor measurement units | RF, weighted voting method | Wang et al. [94] |

eIoT cybersecurity | Modeling a theft attack on an intelligent energy management system | RF, XGBoost | Li et al. [96] |

Energy theft detection | Modeling energy theft in a Smart Grid | ensemble ML | Guntury and Sarkar [97] |

Cybersecurity of energy systems | Analyzing the impact of cybersecurity on monitoring and control systems | fuzzy-based method of AHP and TOPSIS | Alghassab [98] |

Cybersecurity of connected electric vehicles | False data injection detection | SVM | Said et al. [99] |

Energy Saving Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Industrial robotics | Optimization of the palletizing robot’s trajectory | differential EA | Deng et al. [100] |

Manufacturing industry | Permutation flow planning for batch machines | hybrid ACO | Zheng et al. [101] |

Reduction in power losses in electricity distribution | Location optimization for reactive power compensators | BA | Yuvaraj et al. [102] |

Energy path planning | Power consumption problem for UAV transmission towers | simulated expression algorithm | Wu et al. [103] |

Smart homes | Home automation systems | DTs | Machorro-Cano et al. [104] |

Energy efficiency in 5G networks | Packet arrival time prediction | LSTM | Memon et al. [105] |

Responding to demand in energy networks | HVAC controller integration with ML engine for activity recognition | RF | Zhang et al. [106] |

Smart Grid Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Power flow management | Implementing an intelligent agent controlling the power grid | RL, DQN | Damjanović et al. [107] |

Ensuring Smart Grid stability | Predicting Smart Grid stability | DT, NB, SVM, logistic regression, k-NN, ANN | Bashir et al. [108] |

Transformer management | Architecture of multi-agent systems | multi-layer solution, various algorithms | Laayati et al. [109] |

Multi-energy systems management | Real-time energy management automation | LSTM, DDPG, safety-guided network | Qiu et al. [110] |

Smart energy community | Decision problems in peer-to-peer energy trading | MDP, fuzz Q-learning | Zhou et al. [111] |

Prediction Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Load estimation for microgrid planning | Generating load profiles and dimensioning generating units | SOM | Llanos et al. [112] |

Forecasting regional electric load | Adopting an adaptive network-based fuzzy inference system | ANFIS | Ying and Pan [113] |

Smart Grid | Short-term load forecasting | SVR, XGBoost, AdaBoost, random forest, LightGBM, DLR, Bi-LSTM, GRU | Ibrahim et al. [114] |

Building load forecasting | Load time series prediction | multi-variable LSTM | Xu et al. [115] |

Fault Detection Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Power transformers | Fault diagnosis based on dissolved gas data | SVM, GA | Fei and Zhang [116] |

MEPSO-TVAC, ANN | Illias et al. [117] | ||

Electric power lines | Detection and classification of faults in a three-phase system of industrial lines | feed-forward neural network, back propagation algorithm | Jamil et al. [118] |

Thermal power plants | Estimating sensors for fault diagnosis in boilers and turbines | ETC, SVM, k-NN, NB | Khalid et al. [119] |

Hydropower plants | Diagnosing and detecting damage in turbines of a hydroelectric generating set | framework based on Bayesian network and MWPCA | Michalski et al. [120] |

Wind power plants | Diagnosing a variable-speed wind turbine failure | HMM | Kouadri et al. [121] |

Photovoltaic power plants | Monitoring of a photovoltaic installation based on data | regression and classification based on XGBoost | Livera et al. [122] |

Renewable Energy Domains | Engineering Problems | AI Algorithms | References |
---|---|---|---|

Solar energy | Short-term forecasting of photovoltaic energy production | linear techniques, tree-oriented algorithms, ANN | Cabezón et al. [123] |

GRNN, GWO | Tu et al. [124] | ||

Wind energy | Short-term forecasting of wind energy | MVEW-DNN | Wan et al. [125] |

Water energy | Forecasting production capacity of hydropower plants | ML regression techniques | Condemi et al. [126] |

Geothermal energy | Modeling subsurface performance of a geothermal reservoir | ML for timeseries prediction | Duplyakin et al. [127] |

Biomass energy | Estimator for above-ground biomass of fast-growing trees | DT, RF, GTB, AdB, KRR, SVM, k-NN | Wongchai et al. [128] |

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**MDPI and ACS Style**

Szczepaniuk, H.; Szczepaniuk, E.K.
Applications of Artificial Intelligence Algorithms in the Energy Sector. *Energies* **2023**, *16*, 347.
https://doi.org/10.3390/en16010347

**AMA Style**

Szczepaniuk H, Szczepaniuk EK.
Applications of Artificial Intelligence Algorithms in the Energy Sector. *Energies*. 2023; 16(1):347.
https://doi.org/10.3390/en16010347

**Chicago/Turabian Style**

Szczepaniuk, Hubert, and Edyta Karolina Szczepaniuk.
2023. "Applications of Artificial Intelligence Algorithms in the Energy Sector" *Energies* 16, no. 1: 347.
https://doi.org/10.3390/en16010347