Average Monthly Wind Power Forecasting Using Fuzzy Approach
The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable bas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9344689/ |
| _version_ | 1818418417424662528 |
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| author | Iram Akhtar Sheeraz Kirmani Mohmmad Ahmad Sultan Ahmad |
| author_facet | Iram Akhtar Sheeraz Kirmani Mohmmad Ahmad Sultan Ahmad |
| author_sort | Iram Akhtar |
| collection | DOAJ |
| description | The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications. |
| first_indexed | 2024-12-14T12:22:21Z |
| format | Article |
| id | doaj.art-74bf9749541d48a598ba8cfa5b29db69 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T12:22:21Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-74bf9749541d48a598ba8cfa5b29db692022-12-21T23:01:27ZengIEEEIEEE Access2169-35362021-01-019304263044010.1109/ACCESS.2021.30565629344689Average Monthly Wind Power Forecasting Using Fuzzy ApproachIram Akhtar0https://orcid.org/0000-0003-3852-8372Sheeraz Kirmani1https://orcid.org/0000-0003-2174-5688Mohmmad Ahmad2https://orcid.org/0000-0002-0022-9927Sultan Ahmad3https://orcid.org/0000-0002-3198-7974Department of Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, Delhi, IndiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, Delhi, IndiaDepartment of Electrical Engineering, Rajkiya Engineering College at Bijnor, Bijnor, IndiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi ArabiaThe growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications.https://ieeexplore.ieee.org/document/9344689/Wind energymodel predictive controlfuzzy logicmicrogrid |
| spellingShingle | Iram Akhtar Sheeraz Kirmani Mohmmad Ahmad Sultan Ahmad Average Monthly Wind Power Forecasting Using Fuzzy Approach IEEE Access Wind energy model predictive control fuzzy logic microgrid |
| title | Average Monthly Wind Power Forecasting Using Fuzzy Approach |
| title_full | Average Monthly Wind Power Forecasting Using Fuzzy Approach |
| title_fullStr | Average Monthly Wind Power Forecasting Using Fuzzy Approach |
| title_full_unstemmed | Average Monthly Wind Power Forecasting Using Fuzzy Approach |
| title_short | Average Monthly Wind Power Forecasting Using Fuzzy Approach |
| title_sort | average monthly wind power forecasting using fuzzy approach |
| topic | Wind energy model predictive control fuzzy logic microgrid |
| url | https://ieeexplore.ieee.org/document/9344689/ |
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