A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network
Energy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/6/2844 |
| _version_ | 1827750004385644544 |
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| author | Sarhang Sorguli Husam Rjoub |
| author_facet | Sarhang Sorguli Husam Rjoub |
| author_sort | Sarhang Sorguli |
| collection | DOAJ |
| description | Energy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5). |
| first_indexed | 2024-03-11T06:36:28Z |
| format | Article |
| id | doaj.art-05c9a27269ad4762ac1e3bf85080d60f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:36:28Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-05c9a27269ad4762ac1e3bf85080d60f2023-11-17T10:51:45ZengMDPI AGEnergies1996-10732023-03-01166284410.3390/en16062844A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural NetworkSarhang Sorguli0Husam Rjoub1Department of Accounting and Finance, Faculty of Economics and Administrative Sciences, Cyprus International University, Mersin 10, Haspolat 99040, TurkeyDepartment of Accounting and Finance, Faculty of Economics and Administrative Sciences, Cyprus International University, Mersin 10, Haspolat 99040, TurkeyEnergy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5).https://www.mdpi.com/1996-1073/16/6/2844energy accountinglinear-scaling normalizationfuzzy restricted Boltzmann machinerecurrent neural network (FRBM-RNN)adaptive fuzzy Adam optimization algorithm (AFAOA) |
| spellingShingle | Sarhang Sorguli Husam Rjoub A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network Energies energy accounting linear-scaling normalization fuzzy restricted Boltzmann machine recurrent neural network (FRBM-RNN) adaptive fuzzy Adam optimization algorithm (AFAOA) |
| title | A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network |
| title_full | A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network |
| title_fullStr | A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network |
| title_full_unstemmed | A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network |
| title_short | A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network |
| title_sort | novel energy accounting model using fuzzy restricted boltzmann machine recurrent neural network |
| topic | energy accounting linear-scaling normalization fuzzy restricted Boltzmann machine recurrent neural network (FRBM-RNN) adaptive fuzzy Adam optimization algorithm (AFAOA) |
| url | https://www.mdpi.com/1996-1073/16/6/2844 |
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