# An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Gap Analysis

## 4. Dataset Description and System Setup

## 5. Methodology

## 6. Results

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**General framework of system training and testing [24].

Ref | Year | Proposed Work | Limitations |
---|---|---|---|

[25] | 2018 | In this literature, the large data is called Big Data for energy management. | The availability of the referenced massive datasets is limited. |

[26] | 2019 | Deep learning approaches have outdone themselves in dealing with big data. | There is the challenge of managing the large data package. |

[27] | 2019 | The authors have strongly argued for the usefulness of Deep Learning frameworks to design the electrical energy efficiency management system. | Huge standard reference limits set of data for electric energy. |

[28] | 2016 | ||

[29] | 2016 | This is a comparative consideration of two varieties of the Deep Learning network: (LSTM) and sequence architecture (S2 S). | In this study, coverage is limited to a single residential customer. |

[30] | 2016 | They then compared the proposed model with the regression of existing support vector and Deep Learning frameworks. The result of the simulation shows that the local RVS surpassed the RVS and H2 O in-Deep Learning. | The authors stated that they used data on submissions; however, the details of the data were not included in this document. |

[31] | 2016 | This paper presented the Factored Conditional Limited Boltzmann Machine (FCRBM) to forecast energy demands. The model has been tested on the EcoGrid EU data set. | The author of this paper needed to compare his research study with other variations of Deep Learning architecture and currently performed systems. |

[32] | 2017 | The authors have compared the convolutional neural network (CNN/ConvNet) with the study presented in 2016. | No new CNN/ConvNet architecture was presented in this study, and neither was the pre-trained network described. |

[33] | 2017 | Initially formed the Recurrent neural networks (RNN) using a data-driven approach. | This model-less, evidence-based approach has surpassed the approach of model-based studies in management of energy. |

[34] | 2017 | This study provided a comprehensive comparison of conventional machine learning algorithms, including vector support machines, Gaussian processes, regression trees, overall amplification and linear regression, and the Deep Learning method. | Validation of the claim related to the energy management system is observed to be unclear. |

[35] | 2018 | This method is optimized for building energy management, and explores two DL algorithms, namely, Deep Q-learning (DQN) and Deep Policy Gradient (DPG), at the same time. | The parametric fringe of the proposed technique proved insufficient. Moreover, the cognitive scope of the gadget seems to be very trendy. |

[36] | 2018 | The authors have used Recurrent neural networks (RNN) to forecast time series data of energy consumption for a university campus. | The robustness of this work could be enhanced if the master data set had been chosen. |

[37] | 2019 | The authors have suggested using the methods of alternating direction of multipliers (ADMM) and accelerated alternating direction of multipliers (AADM) to find the optimum value of operation of the micro network distribution. | This study did not include the data-based approach to energy forecasting. Moreover, it was felt that the parametric comparison was missing in this work. |

[38] | 2019 | This work submitted a data-driven, Deep Learning approach to district-wide energy demand forecasting. | This study appears deficient because of the absence of a baseline data set and extensive comparison with pre-existing models. |

[39] | 2020 | The FS-FCRBM-GWDO hybrid model is superior to the existing models presented in this study. | The gap between the existing real-world reference data set and the pre-established model is deficient. |

[40] | 2020 | Major contributions include device-based real-time power management via a common cloud data monitoring server. | The actual application was outside the scope of study. |

[41] | 2020 | The authors have used a convolutional neural network (CNN) and a multilayer bi-directional synchronized recurrent unit (MB-GRU) to predict the consumption load of a residential building. | The degree of contribution to research could increase if the updated architecture of Deep Learning could be introduced. Moreover, the systematic evaluation of the novel model may be established in comparison to the existing pre-trained Deep Learning architectures. |

[42] | 2021 | A machine (computer) based vision approach, You Only Look Once (YOLO v3), was utilized to calculate the number of individuals within the region. It is more in correlation with the temperature range of the air conditioning units. | The author’s study would be made more effective by implementing new variants of Deep Learning. |

[43] | 2021 | A new machine learning model for forecasting the energy usage on an hourly basis in a residential building is proposed. | The performance of the machine learning algorithm is compromised as a result of the performance plateau highlighted with big data. |

[44] | 2021 | Deep Learning is the best candidate for power prediction based on time series. The concern in this study is increasingly associated with the fact that the performance of ML and DL is found. | Three small data sets were used to validate the study. the authors could not pursue the novel Deep Learning architecture of a machine learning model for data-driven energy efficiency forecasting. |

[45] | 2020 | Comparison of Machine Learning and DL algorithms with the residential building dataset. The comparison was based on competition between ML and DL. | These works also highlight the urgent need for a master data set for domain-specific applications such as hospitals, schools, universities, residential buildings, etc. |

[46] | 2020 | ||

[47] | 2020 | ||

[48] | 2019 | The work continued to use the RNN with the LSTM approach to forecast energy demand. This work also suggested a gap in the development of any new DL architecture and a pre-formed network of master datasets. | This work also suggests a gap in the development of new ML on master datasets. |

[49] | 2019 | Smart Energy Informatics Lab (SEIL) offered data-driven reinforcement learning for predicting demand. | |

[50] | 2020 | SEIL-IIT introduced the database medium for adaptive data visualization of large sensors. | |

[51] | 2020 | Smart Energy Informatics Lab (SEIL), the same group, studied the hybrid model for predicting energy consumption in buildings through LSTM networks. | |

[52] | 2017 | There has been a push for a data-driven approach to the intelligent energy management system. | |

[53] | 2019 | In this work reference is made to the data set generated by their research. Another area that this group has targeted is solar photovoltaic optimisation and building thermal modelling [53,54,55]. This is beyond the scope of our study. | The Pre-Trained model is deficient. |

[54] | 2019 | ||

[55] | 2018 |

Attribute Number | Attribute Symbol | Attribute Number | Attribute Symbol | Attribute Number | Attribute Symbol | Attribute Number | Attribute Symbol |
---|---|---|---|---|---|---|---|

1. | V1 | 8. | VA1 | 15. | W | 22. | PF3 |

2. | V2 | 9. | VA2 | 16. | VAR1 | 23. | PF |

3. | V3 | 10. | VA3 | 17. | VAR2 | 24. | FwdWh |

4. | A1 | 11. | VA | 18. | VAR3 | 25. | FwdVAh |

5. | A2 | 12. | W1 | 19. | VAR | 26. | FwdVARh, FwdVARhC |

6. | A3 | 13. | W2 | 20. | PF1 | ||

7. | A | 14. | W3 | 21. | PF2 |

**Table 3.**Performance evaluation of machine learning algorithm for energy prediction on SEIL dataset.

Algorithm | Training | Testing | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

RMSE | R-Squared | MSE | MAE | Prediction Speed (Obs/s) | Training Time (s) | RMSE | R-Squared | MSE | MAE | |

Linear | 1.35 × 10^{8} | 0.54 | 1.83 × 10^{16} | 8.42 × 10^{7} | 790,000 | 22.065 | 1.36 × 10^{8} | 0.53 | 1.84 × 10^{16} | 8.45 × 10^{7} |

Interactions Linear | 1.22 × 10^{8} | 0.62 | 1.48 × 10^{16} | 8.02 × 10^{7} | 110,000 | 65.108 | 1.23 × 10^{8} | 0.62 | 1.50 × 10^{16} | 8.05 × 10^{7} |

Robust Linear | 1.64 × 10^{8} | 0.32 | 2.68 × 10^{16} | 5.25 × 10^{7} | 790,000 | 19.44 | 1.65 × 10^{8} | 0.31 | 2.71 × 10^{16} | 5.30 × 10^{7} |

Stepwise Linear | 1.18 × 10^{8} | 0.65 | 1.39 × 10^{16} | 7.63 × 10^{7} | 600,000 | 24,417 | 1.18 × 10^{8} | 0.64 | 1.40 × 10^{16} | 7.66 × 10^{7} |

Fine Trees | 2.11 × 10^{6} | 1 | 4.44 × 10^{12} | 1.40 × 10^{6} | 3,300,000 | 8.3244 | 2.38 × 10^{6} | 1 | 5.64 × 10^{12} | 1.41 × 10^{6} |

Medium Trees | 2.81 × 10^{6} | 1 | 7.87 × 10^{12} | 1.42 × 10^{6} | 3,700,000 | 8.0721 | 3.20 × 10^{6} | 1 | 1.02 × 10^{13} | 1.43 × 10^{6} |

Coarse Tree | 4.26 × 10^{6} | 1 | 1.81 × 10^{13} | 1.49 × 10^{6} | 4,000,000 | 7.5477 | 4.63 × 10^{6} | 1 | 2.15 × 10^{13} | 1.50 × 10^{6} |

Linear SVM | 8.67 × 10^{8} | −18.13 | 7.52 × 10^{17} | 6.69 × 10^{8} | 1,400,000 | 8892.6 | 8.68 × 10^{8} | −18.13 | 7.54 × 10^{17} | 6.69 × 10^{8} |

Quadratic SVM | 3.46 × 10^{8} | −2.05 | 1.20 × 10^{17} | 2.98 × 10^{8} | 240,000 | 18,985 | 3.45 × 10^{8} | −2.02 | 1.19 × 10^{17} | 2.98 × 10^{8} |

Cubic SVM | 6.38 × 10^{8} | −9.35 | 4.07 × 10^{17} | 5.50 × 10^{8} | 260,000 | 5761.7 | 6.37 × 10^{8} | −9.31 | 4.06 × 10^{17} | 5.50 × 10^{8} |

Fine Gaussian SVM | 1.04 × 10^{8} | 0.72 | 1.08 × 10^{16} | 8.83 × 10^{7} | 270,000 | 10,099 | 1.04 × 10^{8} | 0.72 | 1.08 × 10^{16} | 8.85 × 10^{7} |

Medium Gaussian SVM | 2.10 × 10^{8} | −0.13 | 4.43 × 10^{16} | 1.80 × 10^{8} | 1,200,000 | 17,835 | 2.11 × 10^{8} | −0.13 | 4.44 × 10^{16} | 1.80 × 10^{8} |

Coarse Gaussian SVM | 2.17 × 10^{8} | −0.19 | 4.69 × 10^{16} | 1.82 × 10^{8} | 1,300,000 | 18,127 | 2.17 × 10^{8} | −0.2 | 4.71 × 10^{16} | 1.82 × 10^{8} |

Boosted Trees | 2.33 × 10^{7} | 0.99 | 5.42 × 10^{14} | 1.66 × 10^{7} | 180,000 | 62.082 | 2.31 × 10^{7} | 0.99 | 5.34 × 10^{14} | 1.66 × 10^{7} |

Bagged Trees | 1.58 × 10^{6} | 1 | 2.48 × 10^{12} | 1.06 × 10^{6} | 120,000 | 119.87 | 1.78 × 10^{6} | 1 | 3.17 × 10^{12} | 1.09 × 10^{6} |

Squared E × ponential GPR | 7.68 × 10^{7} | 0.85 | 5.89 × 10^{15} | 4.62 × 10^{7} | 200 | 9102.8 | 7.66 × 10^{7} | 0.85 | 5.87 × 10^{15} | 4.62 × 10^{7} |

Matern 5/2 GPR | 6.40 × 10^{7} | 0.9 | 4.10 × 10^{15} | 3.87 × 10^{7} | 110 | 15,589 | 6.40 × 10^{7} | 0.9 | 4.10 × 10^{15} | 3.88 × 10^{7} |

E × ponential GPR | 6.86 × 10^{7} | 0.88 | 4.70 × 10^{15} | 3.73 × 10^{7} | 130 | 14,212 | 6.87 × 10^{7} | 0.88 | 4.72 × 10^{15} | 3.73 × 10^{7} |

Rational Quadratic GPR | 7.30 × 10^{7} | 0.86 | 5.33 × 10^{15} | 4.11 × 10^{7} | 110 | 15,637 | 7.28 × 10^{7} | 0.87 | 5.29 × 10^{15} | 4.11 × 10^{7} |

Narrow Neural Network | 3.18 × 10^{7} | 0.97 | 1.01 × 10^{15} | 1.04 × 10^{7} | 1,000,000 | 254.45 | 3.12 × 10^{7} | 0.98 | 9.76 × 10^{14} | 1.03 × 10^{7} |

Medium Neural Network | 2.54 × 10^{7} | 0.98 | 6.46 × 10^{14} | 1.46 × 10^{7} | 1,100,000 | 396.17 | 2.51 × 10^{7} | 0.98 | 6.32 × 10^{14} | 1.46 × 10^{7} |

Wide Neural Network | 1.81 × 10^{7} | 0.99 | 3.27 × 10^{14} | 1.14 × 10^{7} | 630,000 | 1238.8 | 1.79 × 10^{7} | 0.99 | 3.19 × 10^{14} | 1.14 × 10^{7} |

Bi-layered Neural Network | 3.77 × 10^{8} | −2.62 | 1.42 × 10^{17} | 3.21 × 10^{8} | 1,000,000 | 35.635 | 3.77 × 10^{8} | −2.6 | 1.42 × 10^{17} | 3.20 × 10^{8} |

Tri-layered Neural Network | 3.77 × 10^{8} | −2.62 | 1.42 × 10^{17} | 3.21 × 10^{8} | 870,000 | 48.817 | 3.77 × 10^{8} | −2.6 | 1.42 × 10^{17} | 3.20 × 10^{8} |

Algorithm | Efficacy Ranking | Efficiency Ranking |
---|---|---|

Bagged Trees | 1st | 3rd |

Fine Trees | 2nd | 2nd |

Medium Trees | 3rd | 1st |

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**MDPI and ACS Style**

Akhtar, S.; Sujod, M.Z.B.; Rizvi, S.S.H.
An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms. *Energies* **2022**, *15*, 5742.
https://doi.org/10.3390/en15155742

**AMA Style**

Akhtar S, Sujod MZB, Rizvi SSH.
An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms. *Energies*. 2022; 15(15):5742.
https://doi.org/10.3390/en15155742

**Chicago/Turabian Style**

Akhtar, Shamim, Muhamad Zahim Bin Sujod, and Syed Sajjad Hussain Rizvi.
2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms" *Energies* 15, no. 15: 5742.
https://doi.org/10.3390/en15155742