# Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

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## Abstract

**:**

## 1. Introduction

- AC microgrids,
- DC microgrids,
- Hybrid AC/DC microgrids.

- Integration of renewable energy resources,
- Efficiency,
- Control.

- Grid-connected mode,
- Isolated or islanded mode.

- Shipboard power systems or marine power systems, which can include power resources to support the power system for satisfying the loads and energy storage systems to store and release the energy at the planed or proper times, where this type of microgrids has been studied in previous works, e.g., [15,16,17,18].
- Lunar habitat microgrids, which can be considered as a new concept and the type of microgrids and has been studied previously by [22].
- Etc.

- This paper addresses and focuses on forecasting-based strategies in cyber–physical microgrids.
- Electrical loads and renewable energy resources can have direct and significant effects on a cyber–physical microgrid. In addition, the weather condition can affect the output power of renewable energy resources and the patterns of the load consumption. Therefore, in this paper, recent advances related to prediction of electrical loads, forecasting the output power of renewable energy resources, and anticipation of weather conditions are considered.
- In this paper, PVs and wind turbines are discussed as the popular renewable energy resources.
- In the case of the prediction of the meteorological data, this paper discusses wind speed and solar irradiance.
- Additionally, this paper discusses the advantages of deep learning for the implementation in cyber–physical microgrids.
- In addition, in this paper, the challenges, which can be faced by the deployment of deep learning in cyber–physical microgrids are discussed.
- In addition, this paper discusses future directions and works for this research line to address the current challenges.

## 2. Introduction to Deep Learning

- Feed-forward neural network,
- Radial basis function neural network,
- Cascaded neural network,
- Non-linear autoregressive exogenous (NARX) neural network,
- Long short-term memory (LSTM) neural network,
- Convolutional neural network (CNN),
- Etc.

- Particle swarm optimization,
- Firefly algorithm,
- Teaching learning-based optimization,
- Genetic algorithm,
- Etc.

## 3. Deep Learning-Based Load Forecasting

## 4. Forecasting the Renewable Energy Resource Production

#### 4.1. PV Output Prediction

#### 4.2. Wind Power Prediction

## 5. Deep Learning-Based Weather Prediction

#### 5.1. Wind Speed Prediction

#### 5.2. Solar Irradiance Prediction

## 6. A Brief Overview and a Few Additional Considerations

## 7. Advantages, Current Challenges, and Future Perspectives

#### 7.1. Advantages

- Clear implementation,
- Easy to understand,
- No need or a very slight need to have a knowledge about the entire studied application.

#### 7.2. Challenges

- Requirement of a high volume of data,
- Difficult access to some data,
- Time consuming process for the training of the deep learning-based application,
- Data availability,
- Scalability of ANN-based strategies in cyber–physical microgrids,
- Unclean data.

#### 7.3. Solutions and Future Perspectives

- Generating data in the case of small dataset,
- Considering the studied system as a gray-box,
- Quantum computing,
- Data cleaning.

## 8. Discussion

## 9. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Two different modes of operation for a cyber–physical microgrid, i.e., connected to an upper grid that can be a cluster of microgrids or an upper power system, and isolated mode that works independently.

**Figure 2.**A graph of a cluster of microgrids, which shows the connection between the microgrids in the cluster with k microgrids. Each node is a microgrid and the edges represent the physical connections between microgrids. In addition, Figure 2 illustrates the issues, which should be considered in a cluster of microgrids to have a well-planned and proper operation.

**Figure 4.**The implementation of deep learning for load forecasting in a cyber–physical microgrid. A dataset can be created to train the deep learning-based application (e.g., the ANN) to forecast the values of the loads. Then, based on Figure 4, the current values and the historical values of the desired inputs can be used to predict the future values of power consumption in a cyber–physical microgrid using the trained ANN.

**Figure 5.**Deep learning-based tool (e.g., ANNs) to anticipate the future values of the power of PVs and wind turbines in a cyber–physical microgrid. Based on Figure 5, different inputs (historical values of the power of PVs and wind turbines, or meteorological data) can be used to be implemented in the deep learning-based strategy to predict the output power of PVs and wind turbines.

**Figure 6.**The implementation of a deep learning-based strategy (e.g., an ANN) for the forecasting the weather conditions (e.g., solar irradiance or wind speed). The prediction strategy contains two main steps. For the first step, a proper dataset should be gathered to create the input dataset, including desired data, e.g., meteorological data. Then, in the first step, the gathered data can be deployed to obtain the parameters of the ANN. Additionally, for the second step, the obtained parameters, which can be implemented to exploit the trained ANN to predict the desired parameters, e.g., wind speed and solar irradiance.

**Figure 7.**The white-box, gray-box, and black-box representation of a cyber–physical microgrid. In the case of a white-box consideration, there can be access to all possible information of the microgrid. Additionally, in the case of a gray-box application, there can be only access to some or few parameters. In addition, in the case of a black-box-based system, there is no access to the information about the entire of the studied microgrid.

Traditional Microgrid | Transportation-Based Microgrid | Space Microgrid | Building Microgrid | |
---|---|---|---|---|

Small-Scale Power System | X | |||

Shipboard Power system | X | |||

Satellite | X | |||

Aircraft | X | |||

Data Center | X | |||

Lunar Habitat | X | |||

Residential Building | X | |||

Commercial Building | X |

Protection Strategy | Secure Control | Communication-Based Strategies | Well-Designed Controllers | Optimization-Based Methods | Forecasting-Based Approaches | |
---|---|---|---|---|---|---|

Physical Fault | X | |||||

Cyber Issue | X | X | ||||

Stability | X | |||||

Economical Aspect | X | |||||

Size of the Area | X | |||||

Uncertainty | X | |||||

Environmental Issue | X |

**Table 3.**The summarizing of some advantages, issues, and also future directions related to the deployment of deep learning in a cyber–physical microgrid.

Advantages | Issues | Future Directions and Solutions | |
---|---|---|---|

1 | Clear deployment | Data availability | Generation of Data |

2 | Easily understandable | Unavailability to some parameters or data | Considering the system as a gray-box. |

3 | The mathematical-based information of the entire system are not required. | Training to tune the parameters of the deep learning-based network can be a time consuming process. | Quantum computing |

4 | Typically just data are required | Unclean data | Data cleaning |

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## Share and Cite

**MDPI and ACS Style**

Habibi, M.R.; Golestan, S.; Guerrero, J.M.; Vasquez, J.C.
Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions. *Electronics* **2023**, *12*, 1685.
https://doi.org/10.3390/electronics12071685

**AMA Style**

Habibi MR, Golestan S, Guerrero JM, Vasquez JC.
Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions. *Electronics*. 2023; 12(7):1685.
https://doi.org/10.3390/electronics12071685

**Chicago/Turabian Style**

Habibi, Mohammad Reza, Saeed Golestan, Josep M. Guerrero, and Juan C. Vasquez.
2023. "Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions" *Electronics* 12, no. 7: 1685.
https://doi.org/10.3390/electronics12071685