# Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

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## Abstract

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## 1. Introduction

- a machine learning and deep learning-based model is proposed, i.e., Extreme Learning Machine based Genetic Algorithm (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS),
- the hyperparameter values are tuned using an optimization algorithm to obtain maximum accuracy,
- DT, XGboost and RFE are used in the feature engineering process for removing the redundancy and cleaning the data,
- the GA and GS optimization algorithms are applied to the ELM and SVM to calculate the optimum hyperparameter values.

## 2. Related Work

## 3. Problem Statement and Motivation

## 4. Proposed Model

#### 4.1. Dataset

#### 4.2. Feature Engineering

#### 4.3. Classification and Forecasting

## 5. Simulation Setup

#### Performance Evaluation

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

TGs | Traditional Grids |

SG | Smart Grid |

SMs | Smart Meters |

SVM | Support Vector Machine |

RBM | Restricted Boltzmann Machine |

ReLU | Rectified Linear Unit |

DLSTM | Deep Long Short-Term Memory |

DAEs | Deep Auto Encoders |

GRU | Gated Recurrent Units |

CNN | Convolutional Neural Network |

ISO NE | Independent System Operator New England |

SOTA | State Of The Art |

ELM | Extreme Learning Machine |

GS | Grid Search |

NN | Neural Network |

MAPE | Mean Average Percentage Error |

RFE | Redundancy removal using Feature Extraction |

RMSE | Root Mean Square Error |

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**Table 1.**Features Overview and Dimensions calculated by Redundacny removal using Feature Extraction (RFE).

Target Feature | Features | Short Name | Dimension |
---|---|---|---|

System Load | |||

Day-Ahead Cleared Demand | DA_Demand | TRUE | |

Regulation Market Service clearing price | Reg_Capacity_Price | TRUE | |

Real-Time Demand | RT_Demand | TRUE | |

The dewpoint temperature | Dew_Point | FALSE | |

Day-Ahead Locational Marginal Price | DA_LMP | FALSE | |

The dry-bulb temperature | Dry_Bulb | FALSE | |

Energy Component of Day-Ahead | DA_EC | FALSE | |

Marginal Loss Component of Real-Time | RT_MLC | FALSE | |

Congestion Component of Day-Ahead | DA_CC | FALSE | |

Congestion Component of Real-Time | RT_CC | FALSE | |

Marginal Loss Component of Day-Ahead | DA_MLC | FALSE | |

Energy Component of Real-Time | RT_EC | TRUE | |

Real-Time Locational Marginal Price | RT_LMP | TRUE | |

Regulation Market Capacity clearing | Reg_Service_Price | FALSE |

Techniques | Accuracy | Performance Metrics | |||
---|---|---|---|---|---|

MAPE | RMSE | MSE | MAE | ||

ELM-GA | 96.42% | 2.58 | 737.35 | 5.44 | 4.39 |

SVM-GS | 93.25% | 6.75 | 1811.95 | 32.83 | 10.95 |

LG | 86.14% | 13.86 | 2918.49 | 85.18 | 22.1 |

LM | 84.88% | 15.12 | 3030.06 | 91.81 | 24.46 |

LDA | 84.9% | 15.1 | 3028.09 | 91.69 | 24.44 |

ELM | 89.23% | 10.77 | 2014.66 | 40.59 | 16.12 |

SVM | 85.31% | 14.69 | 3034.03 | 92.05 | 22.75 |

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## Share and Cite

**MDPI and ACS Style**

Ahmad, W.; Ayub, N.; Ali, T.; Irfan, M.; Awais, M.; Shiraz, M.; Glowacz, A.
Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. *Energies* **2020**, *13*, 2907.
https://doi.org/10.3390/en13112907

**AMA Style**

Ahmad W, Ayub N, Ali T, Irfan M, Awais M, Shiraz M, Glowacz A.
Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. *Energies*. 2020; 13(11):2907.
https://doi.org/10.3390/en13112907

**Chicago/Turabian Style**

Ahmad, Waqas, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, and Adam Glowacz.
2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine" *Energies* 13, no. 11: 2907.
https://doi.org/10.3390/en13112907