# Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

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## Abstract

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## 1. Introduction

#### 1.1. Smart Grid

#### 1.2. Problem Statement and Motivation

## 2. Background and Related Work

#### 2.1. Forecasting Electricity Load

#### 2.2. Forecasting Electricity Price

## 3. System Models

#### 3.1. Model for Predicting Electricity Load and Price

- Data input (i.e., dataset).
- Feature extraction using RFE.
- Feature selection using RF and XG-Boost.
- Splitting of data into training and testing.
- Load the CNN layers and parameters.
- Tuning the CNN parameters using CHIO and then model compiling.
- Predicted price and load.
- Performance evaluation.
- Statistical analysis.

#### 3.2. Data Collection

#### 3.3. Feature Extraction Using (RFE)

#### 3.4. Feature Selection

#### XG-Boost

#### 3.5. Convolutional Neural Network

#### 3.6. Coronavirus Herd Immunity Optimization

Algorithm 1: Proposed Work Algorithm |

Result: Electricity price and load forecasting X: data features; Y: data with a purpose; /* Separate the data into two categories: preparation and testing. */ ; split (x, y) = x train, x test, y train, y test; RFE (5, x train, y train); Selected_ function; /* Selection of hybrid features */ ; Incorporate _{imp} = RF_{imp} + XG_{imp}; /* Using RF and XG-boost, measure value */ ; RF imp = RF calculates importance; /* RFE is a technique for extracting features. */ ; CNN-CHIO predicting the future with fine-tuned; Performance evaluation test, compare predictions; |

#### 3.7. Performance Evaluation

## 4. Simulation Results and Discussions

#### 4.1. Electricity Load Forecasting

#### 4.2. Electricity Price Forecasting

#### 4.3. Performance Evaluation of Electricity Price and Load Forecasting

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Methodology (s) | Aims and Objectives | Source(s) of Information/Achievement(s) | Drawback(s) |
---|---|---|---|

[10] NN with several layers. | Forecasting prices | Price forecasting with reasonable accuracy. | The loss rate is high, as is the computational time. |

[13] HSDNN (LSTM and CNN combined). | Forecasting electricity costs | PJM (half-hour). | The amount of time required for computation is considerable. |

[14] Recurrent units with gates (GRU). | Estimating prices | Turkish power sector forecast for the day. | The problem of over-fitting has gotten worse. |

[15] Neural networks with back propagation (BPNN). | Load forecasting for the short term | Texas Electric Reliability Council, USA, a day ahead. | The level of complexity has risen. |

[16] CS-SSA-SVM is a combination of CS-SSA and SVM. | Forecasting Loads | New South Wales: half-hourly, hourly, regular day, and non-working day results (ten weeks). | The calculation takes a very long time. |

[18] LSTM and RNN. | Predictions Loads | Hourly and monthly payments are accepted. France Metropolitan. | Over-fitting is a risk that cannot be avoided. |

[20] DNN, CNN, and LSTM are all examples of deep neural networks. | Forecasting the price of electricity | Estimated price. | The effect of dataset size is not measured, and redundancy is not eliminated. |

[22] The UC-DADR and CC-DADR algorithms. | Decrease high growth and increase consumer benefits by reducing generation capacity. | Interconnection of the states of New Jersey, Maryland (PJM), and Pennsylvania, | The degree of defect detection is smaller. |

[23] Storage device with batteries. | Estimating prices | Ontario’s power market data is updated hourly. | The model isn’t stable or accurate. |

[24] Clustering validity indices (CVIs) are a form of validity index that is used to group together similar items. | The use of electricity | The upcoming day. The University of Seoul has eight buildings. | The amount of time it takes to compute something is enormous. |

[26] SVM and ANN (are two different types of artificial neural networks). | Forecasting loads | The day ahead. PJM and Tunisian electricity industry. | The calculation takes a very long time. |

[27] DCA, KPCA, SVM (are all types of simulation models). | Forecasting prices | Estimated cost and hybrid feature range. | Irrelevant features in the dataset add to the processing time. |

[28] SVM/DNN | Forecasting short-term power costs | Different models are compared, and short-term price predictions are made. | Only for a particular situation. |

[29] DNN | Forecasting prices | By using Bayesian optimization, you can improve accuracy and finish feature selection. | There was no thought given to redundancy or dimensionality reduction. |

[30] Multivariate model | Price forecast on an hourly basis | Reduced the probability of overfitting by using a multivariate model instead of a univariate model. | Except for the unit-variate approach, the model’s output is not comparable to that of other techniques. |

[31] LSTM, DNN | Predictions of cost and load | Predict both the price and the volume of a product. | Price forecasting is unreliable. |

[32] GELM | Price prediction on an hourly basis | Using bootstrapping techniques, predict hourly price and increase model speed. | For large datasets, this method does not function well. |

[33] IG/MI | Hybrid algorithm for feature selection | Accuracy has improved as a result of a better choice of features. | The classifier’s optimization was not taken into consideration. |

[34,35] LSSVM, QOABC | Forecasting prices with loads | Artificial bee colony forecasting of price and load, as well as conditional feature selection and modification. | Their established scenario was the only one that works for them. |

[36] ANN | ANN parameters: finding the best | Parameters for ANN and price estimation have been optimized. | Problems of overfitting were not taken into account. |

[37] DCANN | Price prediction for the next day | Price prediction and development architecture that uses a neural network to automate scenarios. | The computational time is extremely long. |

Techniques | Accuracy (%) | F1-Score (%) | Recall (%) | Precision (%) | RMSE (%) | MAPE (%) | MSE (%) | MAE (%) |
---|---|---|---|---|---|---|---|---|

SVM | 90.89 | 90.32 | 94.456 | 88.21 | 8.43 | 7.23 | 12.34 | 10.77 |

RF | 84.54 | 72.98 | 89.33 | 82.22 | 24.27 | 24.56 | 27.65 | 25.78 |

LR | 81.22 | 75 | 71.555 | 84.94 | 24 | 22.78 | 27 | 21 |

LDA | 76.21 | 74.12 | 82.22 | 65.22 | 29 | 28.78 | 35.22 | 31.56 |

CNN-CHIO | 95.789 | 96.22 | 98.55 | 94.639 | 6.23 | 5.67 | 10.82 | 7.22 |

Techniques | Accuracy (%) | Precision (%) | F1-Score (%) | Recall (%) | RMSE (%) | MSE (%) | MAPE (%) | MAE (%) |
---|---|---|---|---|---|---|---|---|

LR | 75.22 | 78.94 | 69 | 65.545 | 24 | 27 | 22.78 | 21 |

RF | 79.54 | 77.22 | 67.98 | 84.33 | 24.27 | 27.65 | 24.56 | 25.78 |

SVM | 88.89 | 85.21 | 87.32 | 91.466 | 8.34 | 11.34 | 7.23 | 10.77 |

LDA | 71.21 | 60.22 | 69.12 | 77.22 | 29 | 35.22 | 28.78 | 31.56 |

CNN-CHIO | 90.789 | 89.639 | 91.22 | 93.55 | 6.23 | 9.82 | 5.67 | 7.22 |

Techniques | Kendallas | Spearmans | ANOVA | Mann-Whitney | Kruskal | Chi-Squared |
---|---|---|---|---|---|---|

SVM F-statistic | −0.128 | −0.149 | 99.775 | 13,344.500 | 194.502 | 168.491 |

SVM p-Value | 0.014 | 0.014 | 0.000 | 0.000 | 0.000 | 0.000 |

RF F-statistic | 0.785 | 0.856 | 28.779 | 39,227.000 | 35.686 | 107.540 |

RF p-Value | 0.911 | 0.995 | 0.000 | 0.785 | 0.000 | 0.042 |

CNN-CHIO F-stat | 1.000 | 1.000 | 0.000 | 37,538.000 | 0.000 | 6028.000 |

CNN-CHIO p-Val | 1.000 | 0.000 | 1.000 | 0.500 | 1.000 | 0.000 |

LDA F-statistic | 0.801 | 0.867 | 3.100 | 41,232.000 | 70.847 | 109.440 |

LDA p-Value | 0.849 | 0.839 | 0.079 | 0.811 | 0.000 | 0.053 |

LG F-statistic | 1.000 | 1.000 | 0.000 | 37,538.000 | 0.000 | 6028.000 |

LG p-Value | 0.000 | 0.000 | 1.000 | 0.500 | 1.000 | 0.000 |

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## Share and Cite

**MDPI and ACS Style**

Aslam, S.; Ayub, N.; Farooq, U.; Alvi, M.J.; Albogamy, F.R.; Rukh, G.; Haider, S.I.; Azar, A.T.; Bukhsh, R.
Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid. *Sustainability* **2021**, *13*, 12653.
https://doi.org/10.3390/su132212653

**AMA Style**

Aslam S, Ayub N, Farooq U, Alvi MJ, Albogamy FR, Rukh G, Haider SI, Azar AT, Bukhsh R.
Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid. *Sustainability*. 2021; 13(22):12653.
https://doi.org/10.3390/su132212653

**Chicago/Turabian Style**

Aslam, Shahzad, Nasir Ayub, Umer Farooq, Muhammad Junaid Alvi, Fahad R. Albogamy, Gul Rukh, Syed Irtaza Haider, Ahmad Taher Azar, and Rasool Bukhsh.
2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid" *Sustainability* 13, no. 22: 12653.
https://doi.org/10.3390/su132212653