Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
Huge emissions of carbon dioxide (CO2) from the utilization of fossil fuel for power generation has significantly contributed to global warming. In view of this, technological pathways have been initiated to mitigate the effect of CO2 emissions through capture, storage, and utilization. Besides, the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
|
| Series: | Energy Conversion and Management: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174521000362 |
| _version_ | 1818701346523578368 |
|---|---|
| author | Ozavize Freida Ayodele Bamidele Victor Ayodele Siti Indati Mustapa Yudi Fernando |
| author_facet | Ozavize Freida Ayodele Bamidele Victor Ayodele Siti Indati Mustapa Yudi Fernando |
| author_sort | Ozavize Freida Ayodele |
| collection | DOAJ |
| description | Huge emissions of carbon dioxide (CO2) from the utilization of fossil fuel for power generation has significantly contributed to global warming. In view of this, technological pathways have been initiated to mitigate the effect of CO2 emissions through capture, storage, and utilization. Besides, there is an increasing acceptance of carbon tax which is levied in the proportion of carbon emissions from the utilization of fossil fuel. In this study, the nexus between carbon tax, equivalent CO2 emissions from the gas-fired power plant, natural gas flow rate, and air-to-fuel ratio was modeled using a perceptron neural network. The effect of various combinations of identity, hyperbolic tangent, and sigmoid activation functions at the hidden and outer layer of the neural network on the performance of the models was investigated. The various network configurations were trained using the Levenberg-Marquardt algorithm with the network errors backpropagated to enhance the performance. The optimized networks consist of three input units, 15 hidden neurons, and one output unit. The network performance in modeling the carbon tax prediction resulted in R2 of 0.999, 0.999, 0.999, 0.998, and 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively which is an indication that the calculated carbon tax was strongly correlated with the predicted values. The prediction errors of 0.019, 0.009, 0.002, 0.016, 0.002 obtained from model 1, model 2, model 3, model 4, and model 5, respectively revealed the robustness of the models in predicting the carbon tax with minimum error. Among the various configurations investigated, the perceptron neural network configured with hyperbolic tangent and sigmoid activation function at the hidden and outer layers, as well as the configuration with sigmoid activation functions at the hidden and outer layers, offer the best performance. The sensitivity analysis shows that the flow rate of the natural gas had the most significant effect on the predicted carbon tax. |
| first_indexed | 2024-12-17T15:19:23Z |
| format | Article |
| id | doaj.art-3eced280e857480f8a9d4d8b9842a046 |
| institution | Directory Open Access Journal |
| issn | 2590-1745 |
| language | English |
| last_indexed | 2024-12-17T15:19:23Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj.art-3eced280e857480f8a9d4d8b9842a0462022-12-21T21:43:26ZengElsevierEnergy Conversion and Management: X2590-17452021-12-0112100111Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parametersOzavize Freida Ayodele0Bamidele Victor Ayodele1Siti Indati Mustapa2Yudi Fernando3Department of Accounting and Finance, Faculty of Business and Management, UCSI University Kuala Lumpur, Malaysia; Corresponding authors.Institute of Energy Policy and Research, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia; Corresponding authors.Institute of Energy Policy and Research, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, MalaysiaFaculty of Industrial Management, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang-Kuantan, Malaysia; Management Department, BINUS Online Learning, Bina Nusantara University, 11530 IndonesiaHuge emissions of carbon dioxide (CO2) from the utilization of fossil fuel for power generation has significantly contributed to global warming. In view of this, technological pathways have been initiated to mitigate the effect of CO2 emissions through capture, storage, and utilization. Besides, there is an increasing acceptance of carbon tax which is levied in the proportion of carbon emissions from the utilization of fossil fuel. In this study, the nexus between carbon tax, equivalent CO2 emissions from the gas-fired power plant, natural gas flow rate, and air-to-fuel ratio was modeled using a perceptron neural network. The effect of various combinations of identity, hyperbolic tangent, and sigmoid activation functions at the hidden and outer layer of the neural network on the performance of the models was investigated. The various network configurations were trained using the Levenberg-Marquardt algorithm with the network errors backpropagated to enhance the performance. The optimized networks consist of three input units, 15 hidden neurons, and one output unit. The network performance in modeling the carbon tax prediction resulted in R2 of 0.999, 0.999, 0.999, 0.998, and 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively which is an indication that the calculated carbon tax was strongly correlated with the predicted values. The prediction errors of 0.019, 0.009, 0.002, 0.016, 0.002 obtained from model 1, model 2, model 3, model 4, and model 5, respectively revealed the robustness of the models in predicting the carbon tax with minimum error. Among the various configurations investigated, the perceptron neural network configured with hyperbolic tangent and sigmoid activation function at the hidden and outer layers, as well as the configuration with sigmoid activation functions at the hidden and outer layers, offer the best performance. The sensitivity analysis shows that the flow rate of the natural gas had the most significant effect on the predicted carbon tax.http://www.sciencedirect.com/science/article/pii/S2590174521000362Activated functionCarbon taxEmission tradingPerceptron neural networkCO2 emissions |
| spellingShingle | Ozavize Freida Ayodele Bamidele Victor Ayodele Siti Indati Mustapa Yudi Fernando Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters Energy Conversion and Management: X Activated function Carbon tax Emission trading Perceptron neural network CO2 emissions |
| title | Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters |
| title_full | Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters |
| title_fullStr | Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters |
| title_full_unstemmed | Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters |
| title_short | Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters |
| title_sort | effect of activation function in modeling the nexus between carbon tax co2 emissions and gas fired power plant parameters |
| topic | Activated function Carbon tax Emission trading Perceptron neural network CO2 emissions |
| url | http://www.sciencedirect.com/science/article/pii/S2590174521000362 |
| work_keys_str_mv | AT ozavizefreidaayodele effectofactivationfunctioninmodelingthenexusbetweencarbontaxco2emissionsandgasfiredpowerplantparameters AT bamidelevictorayodele effectofactivationfunctioninmodelingthenexusbetweencarbontaxco2emissionsandgasfiredpowerplantparameters AT sitiindatimustapa effectofactivationfunctioninmodelingthenexusbetweencarbontaxco2emissionsandgasfiredpowerplantparameters AT yudifernando effectofactivationfunctioninmodelingthenexusbetweencarbontaxco2emissionsandgasfiredpowerplantparameters |