Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model
A combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, wh...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9718328/ |
| _version_ | 1819349608497676288 |
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| author | Nikhil Pachauri Chang Wook Ahn |
| author_facet | Nikhil Pachauri Chang Wook Ahn |
| author_sort | Nikhil Pachauri |
| collection | DOAJ |
| description | A combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, which makes it challenging to operate. Therefore, a reliable assessment of the maximum output power of a CCPP is required to improve plant reliability and monetary performance. In this paper, a predictive model based on a generalized additive model (GAM) is proposed for the electrical power prediction of a CCPP at full load. In GAM, a boosted tree and gradient boosting algorithm are considered as shape function and learning technique for modeling a non-linear relationship between input and output attributes. Furthermore, predictive models based on linear regression (LR), Gaussian process regression (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and bootstrap-aggregated tree (BBT) are also designed for comparison purposes. Results reveal that GAM improves the RMSE by 74%, 68.8%, 70.3%, 54.8%, 21.2%, and 17.3% compared to LR, GPR, MLP, SVR, DT, and BBT, respectively. Furthermore, the results of the Man-Whitney U test and rank analysis also confirm the effectiveness of GAM for energy prediction of CCPP. Finally, it can be concluded that the proposed method is effective, robust, and accurate for the assessment of the maximum output power of a CCPP to improve plant consistency and financial performance. |
| first_indexed | 2024-12-24T19:03:14Z |
| format | Article |
| id | doaj.art-8108df9598ee4db1927fe40f8b00ad69 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T19:03:14Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8108df9598ee4db1927fe40f8b00ad692022-12-21T16:43:09ZengIEEEIEEE Access2169-35362022-01-0110245662457710.1109/ACCESS.2022.31537209718328Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive ModelNikhil Pachauri0https://orcid.org/0000-0003-2363-3129Chang Wook Ahn1https://orcid.org/0000-0002-9902-5966Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South KoreaArtificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South KoreaA combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, which makes it challenging to operate. Therefore, a reliable assessment of the maximum output power of a CCPP is required to improve plant reliability and monetary performance. In this paper, a predictive model based on a generalized additive model (GAM) is proposed for the electrical power prediction of a CCPP at full load. In GAM, a boosted tree and gradient boosting algorithm are considered as shape function and learning technique for modeling a non-linear relationship between input and output attributes. Furthermore, predictive models based on linear regression (LR), Gaussian process regression (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and bootstrap-aggregated tree (BBT) are also designed for comparison purposes. Results reveal that GAM improves the RMSE by 74%, 68.8%, 70.3%, 54.8%, 21.2%, and 17.3% compared to LR, GPR, MLP, SVR, DT, and BBT, respectively. Furthermore, the results of the Man-Whitney U test and rank analysis also confirm the effectiveness of GAM for energy prediction of CCPP. Finally, it can be concluded that the proposed method is effective, robust, and accurate for the assessment of the maximum output power of a CCPP to improve plant consistency and financial performance.https://ieeexplore.ieee.org/document/9718328/Combined cycle power plantelectrical energygeneralized additive modellinear regressiondecision treeMan-Whitney U test |
| spellingShingle | Nikhil Pachauri Chang Wook Ahn Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model IEEE Access Combined cycle power plant electrical energy generalized additive model linear regression decision tree Man-Whitney U test |
| title | Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model |
| title_full | Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model |
| title_fullStr | Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model |
| title_full_unstemmed | Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model |
| title_short | Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model |
| title_sort | electrical energy prediction of combined cycle power plant using gradient boosted generalized additive model |
| topic | Combined cycle power plant electrical energy generalized additive model linear regression decision tree Man-Whitney U test |
| url | https://ieeexplore.ieee.org/document/9718328/ |
| work_keys_str_mv | AT nikhilpachauri electricalenergypredictionofcombinedcyclepowerplantusinggradientboostedgeneralizedadditivemodel AT changwookahn electricalenergypredictionofcombinedcyclepowerplantusinggradientboostedgeneralizedadditivemodel |