Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model
The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/7/3295 |
| _version_ | 1797607937517027328 |
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| author | Aamer A. Shah Almani A. Aftab Xueshan Han Mazhar Hussain Baloch Mohamed Shaik Honnurvali Sohaib Tahir Chauhdary |
| author_facet | Aamer A. Shah Almani A. Aftab Xueshan Han Mazhar Hussain Baloch Mohamed Shaik Honnurvali Sohaib Tahir Chauhdary |
| author_sort | Aamer A. Shah |
| collection | DOAJ |
| description | The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power. |
| first_indexed | 2024-03-11T05:37:37Z |
| format | Article |
| id | doaj.art-371b66a12e8b4ca2a3db031a63947d01 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T05:37:37Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-371b66a12e8b4ca2a3db031a63947d012023-11-17T16:40:03ZengMDPI AGEnergies1996-10732023-04-01167329510.3390/en16073295Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX ModelAamer A. Shah0Almani A. Aftab1Xueshan Han2Mazhar Hussain Baloch3Mohamed Shaik Honnurvali4Sohaib Tahir Chauhdary5School of Electrical and Control Engineering, Xuzhou University of Technology, Xuzhou 221018, ChinaKey Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250061, ChinaKey Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250061, ChinaCollege of Engineering, A’ Sharqiyah University, Ibra 400, OmanCollege of Engineering, A’ Sharqiyah University, Ibra 400, OmanCollege of Engineering, Dhofar University, Salalah 211, OmanThe volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power.https://www.mdpi.com/1996-1073/16/7/3295wind power generationshort-term forecastingartificial neural network (ANN)power forecastingShenyang offshore wind power |
| spellingShingle | Aamer A. Shah Almani A. Aftab Xueshan Han Mazhar Hussain Baloch Mohamed Shaik Honnurvali Sohaib Tahir Chauhdary Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model Energies wind power generation short-term forecasting artificial neural network (ANN) power forecasting Shenyang offshore wind power |
| title | Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model |
| title_full | Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model |
| title_fullStr | Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model |
| title_full_unstemmed | Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model |
| title_short | Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model |
| title_sort | prediction error based power forecasting of wind energy system using hybrid wt ropso narmax model |
| topic | wind power generation short-term forecasting artificial neural network (ANN) power forecasting Shenyang offshore wind power |
| url | https://www.mdpi.com/1996-1073/16/7/3295 |
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