# Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

^{3}kiloliters of oil equivalent (KLOE) of energy, or 31.47% of total final consumption, an increase of 0.22% from the previous year. In addition, energy use in this sector is the highest compared to other sectors. Although there was a slowdown due to the pandemic impact, energy consumption in the industrial sector still increased. Examining more specifically with electricity consumption, it is identical: the industrial sector remains the most extensive electricity user in Taiwan. Until 2020, the use of electricity by the industrial sector in Taiwan was 150,742.3 gigawatts per hour (GWh) or equivalent to 59.83% of the total final consumption.

_{2}or around 6.4% of the national GHG emissions. Another study from Lu [41], who investigated the Granger causality between electricity consumption and economic growth in 17 Taiwanese industries, found that a 1% increase in electricity consumption raises the real GDP by 1.72%.

## 2. Methodology and Data

#### 2.1. Methodology

^{2}will be used to compare the performance of all the methods.

#### 2.2. Data

## 3. Results

^{2}values. Table 3 displays that an ANN with one hidden layer and 30 nodes using a tansig activation function has the lowest MAE, RMSE, and MAPE values, as well as the highest adjusted R

^{2}values. As a result, our experiment revealed that BP-FFNN with the hyperbolic tangent-sigmoid activation function produced the best results. In order to evaluate the model’s performance, we plotted the real value against the predicted value generated by the ANN model, as shown in Figure 5. To be confident, we performed the same plotting with MLR.

^{2}) to determine the score for each model, as shown in Table 3. The final score results for each proposed model are shown in this table. Based on the validation criteria comparison results, it is clear that ANN with a sigmoid activation function has the poorest performance. It is primarily indicated in the model with two hidden layers because the model receives the lowest ranking with the highest MAE, RMSE, and MAPE, while the adjusted R

^{2}value is the smallest. MLR produces better results than ANN with a sigmoid activation function in terms of MAE, RMSE, MAPE, and adjusted R

^{2}values. Even for some criteria, such as MAE and MAPE, MLR outperforms ANN with a linear activation function. Overall, it can be seen that the ANN value with linear activation function outperforms MLR for each assessment parameter. If we look at the validation criteria for all of the estimation techniques used in this study, we can see that the ANN with hyperbolic tangent-sigmoid has the best results. When compared to the entire proposed model, the model with one hidden layer has the lowest MAE, RMSE, and MAPE values of 0.003, 0.008, and 0.829, respectively, and the highest adjusted R

^{2}value of 99.9%.

## 4. Discussion

^{2}values, the estimation results show that the hyperbolic tangent-sigmoid model is the best model. As a result, the variable importance is measured using that activation function. To calculate the variable importance, we used the method proposed by Goh [56] and Garson [69], which divides the hidden output connection weights into components associated with each input neuron based on the absolute value of the connection weights [70]. However, one of the model’s drawbacks is its use of absolute values, which do not reflect the actual value. Therefore, we modified it by using the model proposed by Olden and Jackson [71]. Figure 7 depicts the end result.

## 5. Conclusions

^{2}values to determine the best model among three activation functions and MLR.

^{2}values are 0.003, 0.008, 0.829, and 99.9 percent, respectively. The model’s relative importance estimation result shows that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23. Furthermore, the climate change factor as measured by changes in degree days has a positive relationship with Taiwan’s industrial electricity demand. It demonstrates that as global warming worsens, industrial electricity use will increase. In this study, the value-added industry as measured by manufacturing output at the disaggregated level indicates that the manufacturing industry has various important relationships with industrial energy consumption in Taiwan. According to the experimental results of this study, ANN can be used as an alternative to industrial energy demand modeling, as demonstrated by a powerful statistical performance that outperforms conventional techniques.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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Structure | Explanation |
---|---|

Number of layers | 3 and 4 |

Number of hidden layers | 1 and 2 |

Number of nodes | 30, 20:10 |

Activation function | Linear, sigmoid, Tansig |

Preprocessing (data range) | [0 1] for Linear and Sigmoid, [−1 1] for Tansig |

Percentage of training and test set | 70:30 |

Number of inputs | 29 |

Description | Variable | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

Electricity Consumption (GWh) | EC | 18,624.3 | 3268.13 | 10,398.7 | 24,900 |

Price Rate (NTD/KWh) | P | 2.4223 | 0.3163 | 2.052 | 3.070 |

Degree Days | DD | 353.3 | 181.77 | 64.4 | 664.6 |

Manufacture Output (Thousand NTD): | |||||

Basic Metal | BM | 102,000 | 34,700 | 39,900 | 181,000 |

Fabricated Metal Products | FMP | 57,000 | 11,000 | 29,900 | 74,900 |

Electrical Equipment | EE | 29,800 | 4348 | 16,200 | 40,400 |

Machinery and Equipment | ME | 45,500 | 11,200 | 21,400 | 68,100 |

Motor Vehicles and Parts | MVP | 28,900 | 5919 | 12,700 | 42,200 |

Transport Equipment and Parts | TEP | 18,400 | 4626 | 8660 | 29,300 |

Repair and Installation | RI | 46,280 | 2334 | 1158 | 10,600 |

Electronic Parts and Component | EPC | 231,000 | 79,900 | 77,100 | 351,000 |

Computers and Electronic | CEO | 65,600 | 12,200 | 33,900 | 96,700 |

Leather and Fur | LF | 2501 | 1065 | 779 | 5600 |

Paper and Paper Products | PAP | 13,100 | 1751 | 7306 | 16,400 |

Printing and Reproduction | PR | 5855 | 708 | 3634 | 7820 |

Petroleum and Coal Products | PCP | 71,500 | 30,300 | 18,800 | 147,000 |

Chemical Material | MCM | 129,000 | 48,500 | 33,400 | 219,000 |

Other Chemical Products | OCP | 17,800 | 4709 | 8271 | 25,800 |

Pharmaceuticals and Medicinal | PMC | 5002 | 1208 | 2783 | 8272 |

Rubber Products | RP | 6895 | 1411 | 3736 | 9376 |

Plastics Products | PP | 24,200 | 2321 | 14,000 | 28,100 |

Food Products and Animal Feeds | FPAF | 35,000 | 6732 | 22,400 | 51,500 |

Beverages | MB | 7743 | 1111 | 4953 | 10,900 |

Tobacco Products | TP | 3488 | 1584 | 1322 | 9281 |

Textiles | MT | 25,900 | 3957 | 13,900 | 37,000 |

Apparel and Clothing | ACA | 3630 | 2189 | 1115 | 11,900 |

Products of Wood and Bamboo | PWB | 1581 | 2853 | 834 | 2484 |

Nonmetallic Mineral | NMP | 17,100 | 2128 | 10,500 | 22,000 |

Furniture | MF | 3184 | 6492 | 1822 | 5363 |

Other Manufacturing | O | 10,800 | 1915 | 6937 | 16,800 |

Methods | $\mathit{f}\mathbf{\left(}{\mathit{x}}_{\mathit{j}}\mathbf{\right)}$ | Nodes | Data Type | MAE | RMSE | MAPE | Adj-R^{2} | ||||
---|---|---|---|---|---|---|---|---|---|---|---|

Value | Rank | Value | Rank | Value | Rank | Value | Rank | ||||

MLR | - | - | - | 0.028 | 7 | 0.036 | 11 | 2.861 | 6 | 0.956 | 11 |

BP-FFNN | Linear | 30 | Train | 0.014 | 5 | 0.019 | 6 | 2.630 | 5 | 0.990 | 5 |

Test | 0.015 | 6 | 0.016 | 5 | 2.427 | 4 | 0.983 | 6 | |||

Linear | 20,10 | Train | 0.081 | 8 | 0.024 | 8 | 4.440 | 8 | 0.968 | 8 | |

Test | 0.083 | 10 | 0.032 | 10 | 4.294 | 7 | 0.964 | 10 | |||

Sigmoid | 30 | Train | 0.082 | 9 | 0.023 | 7 | 5.198 | 11 | 0.967 | 9 | |

Test | 0.084 | 12 | 0.027 | 9 | 5.046 | 10 | 0.953 | 12 | |||

Sigmoid | 20,10 | Train | 0.083 | 10 | 0.041 | 12 | 6.723 | 12 | 0.973 | 7 | |

Test | 0.088 | 13 | 0.057 | 13 | 7.396 | 13 | 0.919 | 13 | |||

Tansig | 30 | Train | 0.005 | 2 | 0.003 | 1 | 1.066 | 2 | 0.996 | 4 | |

Test | 0.003 | 1 | 0.008 | 2 | 0.829 | 1 | 0.999 | 1 | |||

Tansig | 20,10 | Train | 0.008 | 4 | 0.014 | 4 | 4.499 | 9 | 0.997 | 3 | |

Test | 0.005 | 2 | 0.011 | 3 | 2.211 | 3 | 0.999 | 1 |

Input | ANN | MLR | |
---|---|---|---|

Train | Test | ||

Intercept | 0.787 *** | ||

Price | −0.229 | −0.17 | −0.183 ** |

Degree Days | 0.305 | 0.176 | 0.078 *** |

Basic Metal | −0.290 | 0.316 | −0.074 * |

Fabricated Metal Products | 0.592 | −0.189 | 0.141 |

Electrical Equipment | 0.377 | 0.405 | −0.237 *** |

Machinery and Equipment | 0.103 | −0.375 | −0.012 |

Motor Vehicles and Parts | 0.034 | 0.064 | −0.037 * |

Transport Equipment and Parts | 0.129 | 0.339 | 0.091 * |

Repair and Installation | 0.108 | 0.034 | 0.098 *** |

Electronic Parts and Component | −0.059 | 0.013 | 0.188 *** |

Computers and Electronic | 0.425 | 0.505 | 0.055 * |

Leather and Fur | −0.687 | −0.724 | −0.068 * |

Paper and Paper Products | 0.109 | 0.224 | −0.047 |

Printing and Reproduction | −0.030 | 0.001 | 0.067 * |

Petroleum and Coal Products | −0.205 | −0.168 | 0.041 * |

Chemical Material | −0.015 | −0.257 | 0.026 |

Other Chemical Products | −0.495 | −0.246 | 0.235 ** |

Pharmaceuticals and Medicinal | −0.256 | −0.241 | 0.023 |

Rubber Products | 0.001 | −0.219 | −0.009 |

Plastics Products | −0.735 | −0.999 | 0.044 |

Food Products and Animal Feeds | −0.421 | −0.217 | 0.011 |

Beverages | 0.080 | 0.568 | 0.033 |

Tobacco Products | 0.053 | 0.542 | 0.011 |

Textiles | 1.00 | 0.467 | −0.082 |

Apparel and Clothing | −0.149 | −0.079 | 0.151 *** |

Products of Wood and Bamboo | −0.017 | −0.135 | 0.050 |

Nonmetallic Mineral | 0.022 | −0.055 | 0.001 |

Furniture | −0.482 | −0.573 | −0.173 *** |

Other Manufacturing | 0.423 | 0.306 | 0.068 |

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**MDPI and ACS Style**

Shiau, Y.-H.; Yang, S.-F.; Adha, R.; Muzayyanah, S.
Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights. *Sustainability* **2022**, *14*, 2896.
https://doi.org/10.3390/su14052896

**AMA Style**

Shiau Y-H, Yang S-F, Adha R, Muzayyanah S.
Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights. *Sustainability*. 2022; 14(5):2896.
https://doi.org/10.3390/su14052896

**Chicago/Turabian Style**

Shiau, Yuo-Hsien, Su-Fen Yang, Rishan Adha, and Syamsiyatul Muzayyanah.
2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights" *Sustainability* 14, no. 5: 2896.
https://doi.org/10.3390/su14052896