# Utilization of Artificial Neural Networks for Precise Electrical Load Prediction

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## Abstract

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## 1. Introduction

- Long-term forecasting (LTF): 1–20 years. The LTF is crucial for the inclusion of new-generation units in the system and the development of the transmission system.
- Medium-term forecasting (MTF): 1 week–12 months. The MTF is most helpful for the setting of tariffs, the planning of the system maintenance, financial planning, and the scheduling of fuel supply.
- Short-term forecasting (STF): 1 h–1 week. The STF is necessary for the data supply to the generation units to schedule their start-up and shutdown time, to prepare the spinning reserves, and to conduct an in-depth analysis of the restrictions in the transmission system. STF is also crucial for the evaluation of power system security.

## 2. Theoretical Background

#### 2.1. RNN for Variable Inputs/Outputs

- One to Many, applied in fields of image captioning, text generation
- Many to One, applied in fields of sentiment analysis, text classification
- Many to Many, applied in fields of machine translation, voice recognition

#### 2.2. Vanilla RNN

- Inputs and outputs are of variable size
- In each stage the hidden state from the previous stage as well as the current input is utilized to compute the current hidden state that feeds the next stage. Consequently, knowledge from past data is transmitted through the hidden states to the next stages. Hence, the hidden state is a means of connecting the past with the present as well as input with output.
- The set of parameters U, V, and W as well as the activation function are common to all RNN cells.

#### 2.3. Long Short-Term Memory

#### 2.4. Convolutional Neural Network

#### 2.5. Gated Recurrent Unit

## 3. Materials and Methods

#### 3.1. Dataset

#### 3.2. Proposed Methodology

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AGC | Automatic Generation Control |

ANN | Artificial Neural Network |

CNN | Convolutional Neural Network |

DSO | Distribution System Operators |

ELD | Economic Load Dispatch |

GRU | Gated Recurrent Unit |

HETS | Hellenic Electricity Transmission System |

IPTO | Independent Power Transmission Operator |

LD | Linear dichroism |

LSTM | Long Short-Term Memory |

LTF | Long-term forecasting |

MAE | Mean Absolute Error |

MTF | Medium-term forecasting |

RMSE | Root Mean Square Error |

RNN | Recurrent Neural Network |

STF | Short-term forecasting |

TSO | Transmission System Operator |

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**Figure 4.**Actual ground truth values against the predicted ones during the

**training process**for a parameter time step equal to

**24**for the proposed RNN.

**Figure 5.**Actual ground truth values against the predicted ones during the

**testing process**for a parameter time step equal to

**24**for the proposed RNN.

**Figure 6.**RMSE and MSE in training and test set for GRU, LSTM and RNN for time steps 12 (

**a**), 24 (

**b**), 48 (

**c**) and 72 (

**d**).

**Figure 7.**Actual ground truth values against the predicted ones during the

**training process**for a parameter time step equal to

**12**for the proposed RNN.

**Figure 8.**Actual ground truth values against the predicted ones during the

**testing process**for a parameter time step equal to

**12**for the proposed RNN.

**Figure 9.**Actual ground truth values against the predicted ones during the

**training process**for a parameter time step equal to

**48**for the proposed RNN.

**Figure 10.**Actual ground truth values against the predicted ones during the

**testing process**for a parameter time step equal to

**48**for the proposed RNN.

**Figure 11.**Actual ground truth values against the predicted ones during the

**training process**for a parameter time step equal to

**72**for the proposed RNN.

**Figure 12.**Actual ground truth values against the predicted ones during the

**training process**for a parameter time step equal to

**72**for the proposed RNN.

**Figure 13.**Loss function evolution throughout the training epochs for RNN (

**a**), LSTM (

**b**), GRU (

**c**) for time steps 12, 24, 48, and 72.

Time Horizon | Area of Application |
---|---|

12 months–20 months | Planning of the Power System |

1 week–12 months | Scheduling the maintenance of the power system elements |

1 min–1 week | Commitment analysis of the power units |

Automatic Generation Control (AGC) | |

Economic load dispatch (ELD) | |

ms–s | Power system dynamic analysis |

ns–ms | Power system transient analysis |

Time Step | 12 | 24 | 48 | 72 | |||||
---|---|---|---|---|---|---|---|---|---|

RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | ||

RNN | Training set | 0.032 | 0.161 | 0.038 | 0.171 | 0.051 | 0.210 | 0.032 | 0.163 |

Testing set | 0.048 | 0.165 | 0.033 | 0.163 | 0.041 | 0.187 | 0.070 | 0.244 | |

LSTM | Training set | 0.024 | 0.129 | 0.030 | 0.145 | 0.030 | 0.153 | 0.054 | 0.219 |

Testing set | 0.050 | 0.162 | 0.055 | 0.228 | 0.051 | 0.207 | 0.115 | 0.324 | |

GRU | Training set | 0.026 | 0.137 | 0.032 | 0.143 | 0.029 | 0.148 | 0.031 | 0.141 |

Testing set | 0.057 | 0.188 | 0.033 | 0.165 | 0.040 | 0.182 | 0.040 | 0.186 |

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**MDPI and ACS Style**

Pavlatos, C.; Makris, E.; Fotis, G.; Vita, V.; Mladenov, V.
Utilization of Artificial Neural Networks for Precise Electrical Load Prediction. *Technologies* **2023**, *11*, 70.
https://doi.org/10.3390/technologies11030070

**AMA Style**

Pavlatos C, Makris E, Fotis G, Vita V, Mladenov V.
Utilization of Artificial Neural Networks for Precise Electrical Load Prediction. *Technologies*. 2023; 11(3):70.
https://doi.org/10.3390/technologies11030070

**Chicago/Turabian Style**

Pavlatos, Christos, Evangelos Makris, Georgios Fotis, Vasiliki Vita, and Valeri Mladenov.
2023. "Utilization of Artificial Neural Networks for Precise Electrical Load Prediction" *Technologies* 11, no. 3: 70.
https://doi.org/10.3390/technologies11030070