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31 March 2023
Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods
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The inherent intermittency and uncertainty of photovoltaic (PV) power generation impede the development of grid-connected PV systems. Accurately forecasting PV output power is an effective way to address this problem. A hybrid forecasting model that combines the clustering of a trained self-organizing map (SOM) network and an optimized kernel extreme learning machine (KELM) method to improve the accuracy of short-term PV power generation forecasting are proposed. First, pure SOM is employed to complete the initial partitions of the training dataset; then the fuzzy c-means (FCM) algorithm is used to cluster the trained SOM network and the Davies-Bouldin index (DBI) is utilized to determine the optimal size of clusters, simultaneously. Finally, in each data partition, the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression (MR) using least squares to complete coefficient evaluation to establish a regional MR model. The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014. Compared with other single global models, the root mean square errors (RMSEs) of the proposed regional KELM model are reduced by 52.06% in plant 1, 54.56% in plant 2, and 51.43% in plant 3 on average. Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models. In addition, the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods. The forecasts of different methods in different seasons revealed the strong robustness of the proposed method. In four seasons, the MAEs and RMSEs of the proposed SF-KELM are generally the smallest. Moreover, the R2 value exceeds 0.9, which is the closest to 1.
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Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods
),
Abstract
The inherent intermittency and uncertainty of photovoltaic (PV) power generation impede the development of grid-connected PV systems. Accurately forecasting PV output power is an effective way to address this problem. A hybrid forecasting model that combines the clustering of a trained self-organizing map (SOM) network and an optimized kernel extreme learning machine (KELM) method to improve the accuracy of short-term PV power generation forecasting are proposed. First, pure SOM is employed to complete the initial partitions of the training dataset; then the fuzzy c-means (FCM) algorithm is used to cluster the trained SOM network and the Davies-Bouldin index (DBI) is utilized to determine the optimal size of clusters, simultaneously. Finally, in each data partition, the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression (MR) using least squares to complete coefficient evaluation to establish a regional MR model. The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014. Compared with other single global models, the root mean square errors (RMSEs) of the proposed regional KELM model are reduced by 52.06% in plant 1, 54.56% in plant 2, and 51.43% in plant 3 on average. Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models. In addition, the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods. The forecasts of different methods in different seasons revealed the strong robustness of the proposed method. In four seasons, the MAEs and RMSEs of the proposed SF-KELM are generally the smallest. Moreover, the R2 value exceeds 0.9, which is the closest to 1.
References(28)
S Sobri, S Koohi-Kamali, N A Rahim. Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 2018, 156(1): 459-497.
X Yang, M Xu, S Xu, et al. Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining. Applied Energy, 2017, 206: 683-696.
M Pan, C Li, R Gao, et al. Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization. Journal of Cleaner Production, 2020, 277: 123948.
U K Das, K S Tey, M Seyedmahmoudian, et al. Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 2018, 81(1): 912-928.
L Wang, O Kisi, M Zounemat-Kermani, et al. Solar radiation prediction using different techniques: Model evaluation and comparison. Renewable and Sustainable Energy Reviews, 2016, 61: 384-397.
F Almonacid, P J Pérez-Higueras, E F Fernández, et al. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Conversion and Management, 2014, 85: 389-398.
A Dolara, F Grimaccia, S Leva, et al. A physical hybrid artificial neural network for short term forecasting of PV plant power output. Energies, 2015, 8(2): 1138-1153.
P Li, K Zhou, X Lu, et al. A hybrid deep learning model for short-term PV power forecasting. Applied Energy, 2020, 259: 114216.
Z Yang, M Mourshed, K Liu, et al. A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting. Neurocomputing, 2020, 397: 415-421.
W Vandeventer, E Jamei, G S Thirunavukkarasu, et al. Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 2019, 140: 367-379.
M Seyedmahmoudian, E Jamei, G S Thirunavukkarasu, et al. Short-term forecasting of the output power of a building-integrated photovoltaic system using a metaheuristic approach. Energies, 2018, 11(5): 1260.
A Lendasse, J Lee, V Wertz, et al. Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing, 2002, 48(1-4): 299-311.
G A Barreto, A F R Araujo. Identification and control of dynamical systems using the self-organizing map. IEEE Transactions on Neural Networks, 2004, 15(5): 1244-1259.
G Simon, A Lendasse, M Cottrell, et al. Time series forecasting: Obtaining long term trends with self-organizing maps. Pattern Recognition Letters, 2005, 26(12): 1795-1808.
A H S Júnior, G A Barreto, F Corona. Regional models: A new approach for nonlinear system identification via clustering of the self-organizing map. Neurocomputing, 2015, 147: 31-46.
W Fu, K Zhang, K Wang, et al. A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM. Renewable Energy, 2021, 164: 211-229.
R Ye, Q Dai. MultiTL-KELM: A multi-task learning algorithm for multi-step-ahead time series prediction. Applied Soft Computing, 2019, 79: 227-253.
L Xiao, W Shao, F Jin, et al. A self-adaptive kernel extreme learning machine for short-term wind speed forecasting. Applied Soft Computing, 2021, 99: 106917.
J Vesanto, E Alhoniemi. Clustering of the self-organizing map. IEEE Transactions on neural networks, 2000, 11(3): 586-600.
G B Huang, H Zhou, X Ding, et al. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 42(2): 513-529.
P Andras. Kernel-kohonen networks. International Journal of Neural Systems, 2002, 12(2): 117-135.
B Yan, N Xu, L P Xu, et al. An improved partitioning algorithm based on FCM algorithm for extended target tracking in PHD filter. Digital Signal Processing, 2019, 90: 54-70.
M Halkidi, Y Batistakis, M Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 2001, 17(2): 107-145.
T Hong, P Pinson, S Fan, et al. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. International Journal of Forecasting, 2016, 32(3): 896-913.
G A Barreto. Time series prediction with the self-organizing map: A review. Perspectives of Neural-symbolic Integration, 2007: 135-158.
J Li, M Li. Prediction of ultra-short-term wind power based on BBO-KELM method. Journal of Renewable and Sustainable Energy, 2019, 11(5): 056104.
T Matias, F Souza, R Araújo, et al. Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing, 2014, 129: 428-436.
P Zhu, W Zhu, Q Hu, et al. Subspace clustering guided unsupervised feature selection. Pattern Recognition, 2017, 66: 364-374.
