# Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

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## Abstract

**:**

## 1. Introduction

- Implementing a learning-based approach that, with its high skill, passes the training phase without problems such as missing data and overfitting;
- Forecasting microgrid load without considering meteorological data that are not available in remote areas;
- Modeling of microgrid load consumption for a short-term time horizon (one hour) based on different household and commercial consumption loads;
- Evaluating the performance of the Bi-LSTM technique in the training phase and the results of load forecasting with different performance evaluation indicators, as well as presenting a comparative approach to express the effectiveness of the suggested method.

## 2. Bidirectional Long Short-Term Memory (Bi-LSTM)

## 3. Case Study

## 4. Simulation Results

_{i}and y

_{i}represent the actual values and forecasted values, respectively, and $\overline{x}$ and $\overline{y}$ are the means of the actual values and forecasted values, respectively.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Samples of 24 h microgrid load profiles for (

**a**) a low-population microgrid with the same income percentage for the households, (

**b**) a populous microgrid with high-income households, and (

**c**) a populous microgrid with low-income households.

**Figure 4.**Bi−LSTM training errors in the forms of the mean squared error (MSE) and root mean squared error (RMSE).

**Figure 5.**Load forecasting results by Bi-LSTM in the test stage. (

**a**) All test data. (

**b**) Zoomed image of the forecasting of 100 test data samples.

**Table 1.**Input parameters for calculating each instance of the microgrid load profiles shown in Figure 2.

Input Variable | Figure 2a | Figure 2b | Figure 2c |
---|---|---|---|

NoH | 50 | 100 | 100 |

HI households | 33% | 45% | 20% |

MI households | 33% | 30% | 30% |

LI households | 33% | 25% | 50% |

Number of WP | 5 | 3 | 3 |

Number of GM | 1 | 1 | 1 |

Number of SS | 5 | 3 | 3 |

Number of Schools | 1 | 3 | 2 |

Number of Clinics | 1 | 3 | 3 |

Number of SL | 10 | 15 | 12 |

**Table 2.**Evaluation of the performance of the proposed method in comparison with other solutions presented in similar studies.

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**MDPI and ACS Style**

Moradzadeh, A.; Moayyed, H.; Zakeri, S.; Mohammadi-Ivatloo, B.; Aguiar, A.P.
Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. *Inventions* **2021**, *6*, 15.
https://doi.org/10.3390/inventions6010015

**AMA Style**

Moradzadeh A, Moayyed H, Zakeri S, Mohammadi-Ivatloo B, Aguiar AP.
Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. *Inventions*. 2021; 6(1):15.
https://doi.org/10.3390/inventions6010015

**Chicago/Turabian Style**

Moradzadeh, Arash, Hamed Moayyed, Sahar Zakeri, Behnam Mohammadi-Ivatloo, and A. Pedro Aguiar.
2021. "Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid" *Inventions* 6, no. 1: 15.
https://doi.org/10.3390/inventions6010015