Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction
Accurate wind power prediction is very predominant for genuine and effective power systems with high wind power perception. Wind power prediction, as well as wind power generation resources, receives the electrical energy by converting wind into rotational energy of the blades and converting rotatio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Hindawi-Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/3686786 |
| _version_ | 1797977094482821120 |
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| author | W. G. Jency J. E. Judith |
| author_facet | W. G. Jency J. E. Judith |
| author_sort | W. G. Jency |
| collection | DOAJ |
| description | Accurate wind power prediction is very predominant for genuine and effective power systems with high wind power perception. Wind power prediction, as well as wind power generation resources, receives the electrical energy by converting wind into rotational energy of the blades and converting rotational energy into electrical energy by the generator. Wind energy increases with the cube of wind speed. There are numerous common and deep learning methods that have evolved to attain wind power prediction. Deep learning-based methods are referred to as straightforward, and robust, and have been utilized in the recent few years for wind power prediction with a certain level of success. However, due to the lack of an appropriate feature selection process and to minimize the effect of losses used for wind power prediction, a large amount of computation is necessitated when processing multi-input wind power data, therefore causing a negative influence on scalability and hence affecting wind power prediction time. To address these issues, in this work, a method called, Homogenized Point Mutual Information and Deep Quantum Reinforced (HPMI-QDR) wind power prediction are proposed. The HPMI-DQR method is split into two sections. In the first section, informative and relevant features required for robust wind power prediction using input wind turbine data are designed using Homogenized Point Mutual (HPM) Feature Selection model. With the relevant features selected, in the second section, the actual wind power prediction is made using the Deep Quantum Reinforced Learning model. To validate the proposed method, Wind Turbine SCADA Dataset is used for constructing and testing. Simulation of proposed method attains enhancement within wind power prediction accuracy as 13%, minimal wind power prediction time as 25%, as well as better wind energy generation as 20% and true positive rate as 25%, compared using conventional techniques. Moreover, a substantial improvement was also found in wind power prediction time with minimum error. |
| first_indexed | 2024-04-11T05:01:29Z |
| format | Article |
| id | doaj.art-e7f55b9d3e6e4254aea36525bbf63069 |
| institution | Directory Open Access Journal |
| issn | 2050-7038 |
| language | English |
| last_indexed | 2024-04-11T05:01:29Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj.art-e7f55b9d3e6e4254aea36525bbf630692022-12-26T01:12:09ZengHindawi-WileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/3686786Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power PredictionW. G. Jency0J. E. Judith1Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringAccurate wind power prediction is very predominant for genuine and effective power systems with high wind power perception. Wind power prediction, as well as wind power generation resources, receives the electrical energy by converting wind into rotational energy of the blades and converting rotational energy into electrical energy by the generator. Wind energy increases with the cube of wind speed. There are numerous common and deep learning methods that have evolved to attain wind power prediction. Deep learning-based methods are referred to as straightforward, and robust, and have been utilized in the recent few years for wind power prediction with a certain level of success. However, due to the lack of an appropriate feature selection process and to minimize the effect of losses used for wind power prediction, a large amount of computation is necessitated when processing multi-input wind power data, therefore causing a negative influence on scalability and hence affecting wind power prediction time. To address these issues, in this work, a method called, Homogenized Point Mutual Information and Deep Quantum Reinforced (HPMI-QDR) wind power prediction are proposed. The HPMI-DQR method is split into two sections. In the first section, informative and relevant features required for robust wind power prediction using input wind turbine data are designed using Homogenized Point Mutual (HPM) Feature Selection model. With the relevant features selected, in the second section, the actual wind power prediction is made using the Deep Quantum Reinforced Learning model. To validate the proposed method, Wind Turbine SCADA Dataset is used for constructing and testing. Simulation of proposed method attains enhancement within wind power prediction accuracy as 13%, minimal wind power prediction time as 25%, as well as better wind energy generation as 20% and true positive rate as 25%, compared using conventional techniques. Moreover, a substantial improvement was also found in wind power prediction time with minimum error.http://dx.doi.org/10.1155/2022/3686786 |
| spellingShingle | W. G. Jency J. E. Judith Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction International Transactions on Electrical Energy Systems |
| title | Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction |
| title_full | Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction |
| title_fullStr | Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction |
| title_full_unstemmed | Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction |
| title_short | Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction |
| title_sort | homogenized point mutual information and deep quantum reinforced wind power prediction |
| url | http://dx.doi.org/10.1155/2022/3686786 |
| work_keys_str_mv | AT wgjency homogenizedpointmutualinformationanddeepquantumreinforcedwindpowerprediction AT jejudith homogenizedpointmutualinformationanddeepquantumreinforcedwindpowerprediction |