اخبار و اعلانات
آمار
| تعداد نشریات | 33 |
| تعداد شماره‌ها | 775 |
| تعداد مقالات | 7,506 |
| تعداد مشاهده مقاله | 12,578,252 |
| تعداد دریافت فایل اصل مقاله | 8,512,842 |
A New Hybrid Intelligent Method for Accurate Short Term Electric Power Production Forecasting From Uncertain Renewable Energy Resources | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 4، دوره 8، شماره 1، خرداد 2025، صفحه 45-55 اصل مقاله (984.75 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2024.48701.1566 | ||
| نویسندگان | ||
| Gholamreza Memarzadeh* 1؛ Farshid Keynia2؛ Faezeh Amirteimoury3؛ Rasoul Memarzadeh4؛ Hossein Noori1 | ||
| 1Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran | ||
| 2Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran | ||
| 3Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran | ||
| 4Department of Civil Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran | ||
| چکیده | ||
| In recent years, there has been a significant increase in the utilization of renewable resources for electricity generation. Consequently, accurate short-term forecasting of renewable power production has become crucial for power system operations. However, Renewable Power Production Forecasting (RPPF) presents unique challenges due to the intermittent and uncertain nature of renewable energy sources. This paper proposes a novel approach to short-term RPPF. The proposed model integrates various techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The aim is to enhance the accuracy and predictive performance of renewable power production forecasts. The suggested hybrid model employs the Modified Relief-Mutual Information (MRMI) feature selection technique to identify the most influential input data for prediction. Subsequently, the combined model generates a 24-hour ahead RPP prediction using a weighted output approach. By capitalizing on the strengths of each individual model, the combined method mitigates their weaknesses, thereby improving the overall efficiency of the forecasting process. The accuracy and performance of the proposed method are evaluated through two case studies involving solar farm power generation at the Mahan, Iran and Rafsanjan, Iran sites. The results demonstrate the effectiveness of the hybrid model in enhancing the accuracy of short-term RPPF. By combining multiple forecasting methods and utilizing the MRMI feature selection technique, the proposed method significantly improves prediction accuracy. | ||
| کلیدواژه‌ها | ||
| Gated Recurrent Unit؛ LSTM؛ MRMI Feature Selection؛ Renewable energy resources | ||
| مراجع | ||
|
[1] M. Vosoogh, M. Rashidinejad, A. Abdollahi, and M. Ghaseminezhad, "Efficient networked microgrid management considering plug-in electric vehicles and storage units," International Journal of Industrial Electronics Control and Optimization, Vol. 4, No. 2, pp. 245-255, 2021.
[2] Z. Allal, H. N. Noura, and K. Chahine, "Machine Learning Algorithms for Solar Irradiance Prediction: A Recent Comparative Study," e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 7, p. 100453, 2024. [3] H. B, V. G, Vaibhav, K. S. Anantharaju, and R. K. Pai, "Machine learning in the era of smart automation for renewable energy materials," e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 7, p. 100458, 2024. [4] S. M. Hadad Baygi and J. Farzaneh, "Application of Artificial intelligence techniques for optimum design of hybrid grid-independent PV/WT/battery power system," International Journal of Industrial Electronics Control and Optimization, Vol. 3, No. 3, pp. 275-289, 2020. [5] J. d. S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable energy, Vol. 36, No. 4, pp. 1245-1251, 2011. [6] M. G. De Giorgi, A. Ficarella, and M. Tarantino, "Error analysis of short term wind power prediction models," Applied Energy, Vol. 88, No. 4, pp. 1298-1311, 2011. [7] J. Jung, S. Han, and B. Kim, "Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes," Applied Energy, Vol. 242, pp. 57-68, 2019. [8] O. Ait Maatallah, A. Achuthan, K. Janoyan, and P. Marzocca, "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Vol. 145, pp. 191-197, 2015. [9] D. Yang, V. Sharma, Z. Ye, L. I. Lim, L. Zhao, and A. W. Aryaputera, "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Vol. 81, pp. 111-119, 2015. [10] J. L. Torres, A. García, M. De Blas, and A. De Francisco, "Forecast of hourly average wind speed with ARMA models in Navarre (Spain)," Solar Energy, Vol. 79, No. 1, pp. 65-77, 2005. [11] F. Besharat, A. A. Dehghan, and A. R. Faghih, "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Vol. 21, pp. 798-821, 2013/05/01/ 2013. [12] J. Wang, P. Du, T. Niu, and W. Yang, "A novel hybrid system based on a new proposed algorithm—Multi- Objective Whale Optimization Algorithm for wind speed forecasting," Applied Energy, Vol. 208, pp. 344-360, 2017. [13] W. Sun and M. Liu, "Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China," Energy Conversion and Management, Vol. 114, pp. 197-208, 2016. [14] T. Niu, J. Wang, K. Zhang, and P. Du, "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Vol. 118, pp. 213-229, 2018. [15] H. Shao, X. Deng, and F. Cui, "Short‐term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China," IET Generation, Transmission & Distribution, Vol. 10, No. 11, pp. 2585-2592, 2016. [16] H. You, R. Jia, X. Chen, and L. Huang, "A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network," Journal of Renewable and Sustainable Energy, Vol. 15, No. 4, 2023. [17] A. K. Yadav and S. Chandel, "Solar radiation prediction using Artificial Neural Network techniques: A review," Renewable and sustainable energy reviews, Vol. 33, pp. 772-781, 2014. [18] G. Perveen, M. Rizwan, and N. Goel, "Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system," IET Energy Systems Integration, Vol. 1, No. 1, pp. 34-51, 2019. [19] O. M. Babatunde, J. L. Munda, Y. Hamam, and C. G. Monyei, "A critical overview of the (Im)practicability of solar radiation forecasting models," e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 5, p. 100213, 2023. [20] Q. Yu and Z. Li, "Correlated load forecasting in active distribution networks using Spatial ‐ Temporal Synchronous Graph Convolutional Networks," IET Energy Systems Integration, Vol. 3, No. 3, pp. 355-366, 2021. [21] M. Khodayar, O. Kaynak, and M. E. Khodayar, "Rough deep neural architecture for short-term wind speed forecasting," IEEE Transactions on Industrial Informatics, Vol. 13, No. 6, pp. 2770-2779, 2017. [22] Q. Hu, R. Zhang, and Y. Zhou, "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Vol. 85, pp. 83-95, 2016. [23] H.-z. Wang, G.-q. Li, G.-b. Wang, J.-c. Peng, H. Jiang, and Y.-t. Liu, "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied energy, Vol. 188, pp. 56-70, 2017. [24] L. Zeng, X. Lan, and S. Wang, "Short-term wind power prediction based on the combination of numerical weather forecast and time series," Journal of Renewable and Sustainable Energy, Vol. 15, No. 1, 2023. [25] X. Yang, Z. Zhao, Y. Peng, and J. Ma, "Research on distributed photovoltaic power prediction based on spatiotemporal information ensemble method," Journal of Renewable and Sustainable Energy, Vol. 15, No. 3, 2023. [26] Y. Sun, V. Venugopal, and A. R. Brandt, "Short-term solar power forecast with deep learning: Exploring optimal input and output configuration," Solar Energy, Vol. 188, pp. 730-741, 2019. [27] X. Liu, H. Zhang, X. Kong, and K. Y. Lee, "Wind speed forecasting using deep neural network with feature selection," Neurocomputing, Vol. 397, pp. 393-403, 2020. [28] H. Sharadga, S. Hajimirza, and R. S. Balog, "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Vol. 150, pp. 797-807, 2020. [29] M. Alaraj, A. Kumar, I. Alsaidan, M. Rizwan, and M. Jamil, "Energy production forecasting from Solar photovoltaic plants based on meteorological parameters for Qassim region, Saudi Arabia," Ieee Access, Vol. 9, pp. 83241-83251, 2021. [30] S. S. Pattanaik, A. K. Sahoo, R. Panda, and S. Behera, "Life cycle assessment and forecasting for 30kW solar power plant using machine learning algorithms," e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 7, p. 100476, 2024. [31] J. Zhu, L. Su, and Y. Li, "Wind power forecasting based on new hybrid model with TCN residual modification," Energy and AI, Vol. 10, p. 100199, 2022. [32] J. Xiong, T. Peng, Z. Tao, C. Zhang, S. Song, and M. S. Nazir, "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Vol. 266, p. 126419, 2023. [33] X.-J. Chen, J. Zhao, X.-Z. Jia, and Z.-L. Li, "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Vol. 165, pp. 595-611, 2021. [34] S. R. Moreno, R. G. da Silva, V. C. Mariani, and L. dos Santos Coelho, "Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network," Energy Conversion and Management, Vol. 213, p. 112869, 2020. [35] J. Duan et al., "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Vol. 200, pp. 788-808, 2022. [36] V. Kosana, K. Teeparthi, and S. Madasthu, "Hybrid wind speed prediction framework using data pre-processing strategy based autoencoder network," Electric Power Systems Research, Vol. 206, p. 107821, 2022. [37] T. Liu, Q. Zhao, J. Wang, and Y. Gao, "A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations," Renewable Energy, Vol. 163, pp. 88-104, 2021. [38] Z. Cai, S. Dai, Q. Ding, J. Zhang, D. Xu, and Y. Li, "Gray wolf optimization-based wind power load mid-long term forecasting algorithm," Computers and Electrical Engineering, Vol. 109, p. 108769, 2023. [39] C. Scott, M. Ahsan, and A. Albarbar, "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Vol. 278, p. 127807, 2023. [40] S. Theocharides, G. Makrides, A. Livera, M. Theristis, P. Kaimakis, and G. E. Georghiou, "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post -processing," Applied Energy, Vol. 268, p. 115023, 2020. [41] J. Heo, K. Song, S. Han, and D.-E. Lee, "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Vol. 295, p. 117083, 2021. [42] W. Hu et al., "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Vol. 179, pp. 2174-2186, 2021. [43] H. H. Aly, "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Vol. 213, p. 118773, 2020. [44] M. Jurado, M. Samper, and R. Rosés, "An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting," Electric Power Systems Research, Vol. 217, p. 109153, 2023. [45] C. Brester, V. Kallio-Myers, A. V. Lindfors, M. Kolehmainen, and H. Niska, "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Vol. 207, pp. 266-274, 2023. [46] F. Keynia and G. Memarzadeh, "A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system," IET Generation, Transmission & Distribution, Vol. 16, No. 5, pp. 851-868, 2022. [47] G. Memarzadeh and F. Keynia, "A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets," Energy Conversion and Management, Vol. 213, p. 112824, 2020. [48] S. Kouhi and F. Keynia, "A new cascade NN based method to short-term load forecast in deregulated electricity market," Energy Conversion and Management, Vol. 71, pp. 76-83, 2013. [49] N. Darjani, and H. Omranpour, . "Comprehensive learning polynomial auto-regressive model based on optimization with application of time series forecasting," International Journal of Industrial Electronics Control and Optimization, Vol. 5, No. 1, pp. 43-50, 2022. [50] G. Memarzadeh and F. Keynia, "Solar power generation forecasting by a new hybrid cascaded extreme learning method with maximum relevance interaction gain feature selection," Energy Conversion and Management, Vol. 298, p. 117763, 2023. | ||
|
آمار تعداد مشاهده مقاله: 389 تعداد دریافت فایل اصل مقاله: 259 |
||