# Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler

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## Abstract

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## 1. Introduction

- Hybrid of feature selection techniques; Extreme Gradient Boosting (XGB), Random Forest RF and Recursive Feature Elimination (RFE) techniques are applied to clean the huge amount of data.
- Two enhanced classifier techniques, Support Vector Machine with Grey Wolf Optimization (SVM-GWO) and Convolutional Neural Network Gated Recurrent Unit with Earth Worm Optimization (CNN-GRU-EWO) are proposed to forecast the electricity load.
- Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) algorithms are used to tune the parameters of SVM and CNN-GRU, respectively.
- The parameters of classifiers are tuned to reduce the computational time efficiently.
- To overcome the overfitting problem, enhanced classifiers are used.
- Our proposed techniques are compared with some State Of The Art (SOTA) to prove the better performance of our enhanced techniques.

## 2. Related Work

## 3. Proposed System Model

#### 3.1. Dataset Description

#### 3.2. Feature Engineering

#### 3.3. Classification and Forecasting

#### 3.3.1. CNN-GRU-EWO

#### 3.3.2. Gated Recurrent Unit (GRU)

#### 3.3.3. SVM-GWO

## 4. Simulation Results

#### 4.1. Average Feature Selection Based on RF and XGB

#### 4.2. Classification and Forecasting Using SVM-GWO and CNN-GRU-EWO

## 5. Performance Metrics

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

CMI | Conditional Mutual Information |

NLSSVM | Nonlinear Least Square Support Vector Machine |

ABC | Artificial Bee Colony |

ARIMA | Autoregressive Integrated Moving Average |

IWNN | Wavelet Neural Network |

ELM | Extreme Learning Machine |

CNN | Convolutional Neural Network |

LSTM | Long Short Term Memory |

ASF | Auto Correlation Function |

IITK | India Institution of Technology Kanpoor |

ELM | Extreme Learning Machine |

XGB | Extreme Gradient Boosting |

DTC | Decision Tree Classifier |

MAE | Mean Absolute Error |

RMSE | Root Mean Square Error |

MSE | Mean Square Error |

MAPE | Mean Absolute Percentage Error |

PJM | Pennsylvania New Jersey Maryland |

CA | Correlation Analysis |

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Proposed Techniques | Objective | Dataset | Limitations |
---|---|---|---|

DA [13] | Reduce peak load | PJM | Issue in managing big data |

DLSTM [14] | Price and Load forecasting | ISO-NE | Cannot fulfill the requirement of real time data. |

CMI, NLSSVM [15,16] | Forecasting with important feature selection method | PJM | Less amount of data is taken into consideration |

GELM, IWNN [17] | Hourly price forecasting | PJM | Model complexity is considered |

CNN, LSTM [18,19] | Price forecasting | PJM | Redundancy in features are not considered |

DNN [20] | Load forecasting | Irish | Overfitting problem needed to improve |

DCNN [21] | Load forecasting of one day | Victoria | Limited use of dataset |

ESVM [22,23] | Short term load forecasting | ISO-NE | SVM is not good to deal big dataset because overfitting problem |

ANN [24] | Half hourly load forecasting | Tanzanian | Accuracy rates of their work are not satisfactory. |

MI, NN [25] | Short term forecasting | PJM | Maximize the penetration of renewable energy |

NARX, ARMAX [26] | Residential based short term load forecasting | IESCO | Model complexity increased |

GRU [27] | Load forecasting | PJM | Redundancy of features did not considered |

SVM, ANN [28] | Short term forecasting | IITK | Very small dataset is used for experiment |

ELM-K [29] | Short term forecasting | Southern China | Only one error metrics used for evaluation. |

CNN [20] | Short term forecasting | ISO-NE | Manually tuned the hyper parameters of proposed technique |

GRU-CNN [31] | Short term forecasting | Wuwei, Gansu province | Manually tuned the hyper parameters of proposed technique |

MI, ANN [32] | Day ahead load forecasting | DAYTOWN, AKPC | Feature selection need more improvement |

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 | Performance Metrics | |||||||
---|---|---|---|---|---|---|---|---|

F1-Score | Accuracy | Precision | Recall | MAPE | RMSE | MAE | MSE | |

CNN_GRU_EWA | 95.23 | 96.33 | 94.00 | 94.62 | 6.00 | 7.00 | 10.00 | 13.00 |

LR | 75.88 | 78.35 | 76.56 | 76.98 | 20.00 | 23.00 | 27.00 | 26.00 |

ELM | 75.00 | 78.98 | 76.45 | 22.78 | 13.00 | 12.00 | 15.00 | 18.00 |

SVM | 87.88 | 87.99 | 86.91 | 85.99 | 1.79 | 12.30 | 10.50 | 12.00 |

SVM_GWO | 90.67 | 93.99 | 91.87 | 90.99 | 1.33 | 9.12 | 10.31 | 9.75 |

CNN | 88.66 | 89.00 | 90.00 | 88.76 | 10.00 | 12.00 | 15.00 | 18.00 |

Techniques and Tests | Correlation Tests | Parametric Statistical Hypothesis Tests | Non-Parametric Statistical Hypothesis Tests | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

Pearson’s Test | Spearman’s Test | Kendalla’s Test | Chi- Squared Test | Student’s Test | Paired Student’s Test | ANOVA Test | Mann- Whitney Test | Wilcoxon Test | Kruskal Test | ||

SVM | F-stastistic | −0.0404 | −0.0549 | −0.0362 | 157,449.28 | −5.5019 | −5.3941 | 30 | 225,955 | 104,549 | 26.0883 |

p-value | 0.2753 | 0.1379 | 0.1429 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |

SVM-GWO | F-stastistic | −0.0376 | −0.0553 | −0.0362 | 164,404.40 | 0.2530 | 0.2484 | 0.0640 | 262,798 | 132,003 | 0.2949 |

p-value | 0.3106 | 0.1349 | 0.1436 | 0.0000 | 0.8003 | 0.8039 | 0.8003 | 0.2936 | 0.8054 | 0.5871 | |

CNN | F-stastistic | 0.9964 | 0.9963 | 0.9499 | 575.09 | 1.1820 | 19.2812 | 1.3971 | 257,449 | 37,953 | 1.4537 |

p-value | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.2374 | 0.0000 | 0.2374 | 0.1140 | 0.0000 | 0.2279 | |

CNN-GRU-EWA | F-stastistic | 0.7367 | 0.7208 | 0.5321 | 37,815.93 | −0.8087 | −1.4750 | 0.6539 | 267,085 | 131,225 | 0.0001 |

p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.4188 | 0.1406 | 0.4188 | 0.4953 | 0.6555 | 0.9906 | |

ELM | F-stastistic | 0.9887 | 0.9856 | 0.9143 | 1865.32 | −0.1100 | −1.0303 | 0.0121 | 26,4803 | 124,235 | 0.0868 |

p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.9124 | 0.3032 | 0.9124 | 0.3842 | 0.1538 | 0.7683 | |

LG | F-stastistic | 0.2411 | 0.2033 | 0.1415 | 89,538.00 | −6.0994 | −6.9077 | 37 | 218,561 | 94,749 | 36.3238 |

p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

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## Share and Cite

**MDPI and ACS Style**

Ayub, N.; Irfan, M.; Awais, M.; Ali, U.; Ali, T.; Hamdi, M.; Alghamdi, A.; Muhammad, F.
Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. *Energies* **2020**, *13*, 5193.
https://doi.org/10.3390/en13195193

**AMA Style**

Ayub N, Irfan M, Awais M, Ali U, Ali T, Hamdi M, Alghamdi A, Muhammad F.
Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. *Energies*. 2020; 13(19):5193.
https://doi.org/10.3390/en13195193

**Chicago/Turabian Style**

Ayub, Nasir, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, and Fazal Muhammad.
2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler" *Energies* 13, no. 19: 5193.
https://doi.org/10.3390/en13195193