{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:32:30Z","timestamp":1776184350016,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on Pearson Correlation Coefficient (PCC), Complete Ensemble Empirical Mode Decomposition (CEEMDAN), K-means clustering, Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). By making full use of the symmetric structure of the BiLSTM algorithm, one part is used to process the data sequence in order, and the other part is used to process the data sequence in reverse order. It captures the characteristics of sequence data by simultaneously processing a \u2018symmetric\u2019 information. Firstly, the historical photovoltaic data are preprocessed, and the correlation analysis of meteorological factors is carried out by PCC, and the high correlation factors are extracted to obtain the multivariate time series feature matrix of meteorological factors. Then, the historical photovoltaic power data are decomposed into multiple intrinsic modes and a residual component at one time by CEEMDAN. The high-frequency components are clustered by K-means combined with sample entropy, and the high-frequency components are decomposed and refined by VMD to form a multi-scale characteristic mode matrix. Finally, the obtained features are input into the CNN\u2013BiLSTM model for the final photovoltaic power prediction results. After experimental verification, compared with the traditional single-mode decomposition algorithm (such as CEEMDAN\u2013BiLSTM, VMD\u2013BiLSTM), the combined prediction method proposed reduces MAE by more than 0.016 and RMSE by more than 0.017, which shows excellent accuracy and stability.<\/jats:p>","DOI":"10.3390\/sym17030414","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T08:46:41Z","timestamp":1741596401000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Photovoltaic Power Prediction Technology Based on Multi-Source Feature Fusion"],"prefix":"10.3390","volume":"17","author":[{"given":"Xia","family":"Zhou","sequence":"first","affiliation":[{"name":"Carbon Neutralization Advanced Technology Research Institute, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Xize","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Jianfeng","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2503-7024","authenticated-orcid":false,"given":"Tengfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ye, X., Ye, J., and Liang, G. (2024, January 26\u201328). A Day-ahead Photovoltaic Power Generation Prediction Method Based on Data Mining and Micro-meteorological Information. Proceedings of the 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS62053.2024.10796814"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100452","DOI":"10.1016\/j.egyai.2024.100452","article-title":"Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU","volume":"18","author":"Riedel","year":"2024","journal-title":"Energy AI"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101886","DOI":"10.1016\/j.rineng.2024.101886","article-title":"Bi-LSTM, GRU and 1D-CNN models for short-term photovoltaic panel efficiency forecasting case amorphous silicon grid-connected PV system","volume":"21","author":"Mansour","year":"2024","journal-title":"Results Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112766","DOI":"10.1016\/j.enconman.2020.112766","article-title":"A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework","volume":"212","author":"Wang","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1016\/j.egyr.2024.08.033","article-title":"Empowering federated learning techniques for privacy-preserving pv forecasting","volume":"12","author":"Michalakopoulos","year":"2024","journal-title":"Energy Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.renene.2023.01.118","article-title":"Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model","volume":"205","author":"Limouni","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_7","first-page":"100892","article-title":"A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems","volume":"40","author":"Liu","year":"2023","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Q., Chu, A., Du, J., and Wang, M. (2024, January 26\u201328). Short Term Forecast of Photovoltaic Power Generation Based on WOA-LSTM. Proceedings of the 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS62053.2024.10795916"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"121057","DOI":"10.1016\/j.renene.2024.121057","article-title":"Hybrid model for intra-day probabilistic PV power forecast","volume":"232","author":"Thaker","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.ifacol.2022.07.051","article-title":"Comparison of PV power generation forecasting in a residential building using ANN and DNN","volume":"55","author":"Tavares","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1109\/TAI.2024.3404408","article-title":"Dynamic Combination Forecasting for Short-Term Photovoltaic Power","volume":"5","author":"Huang","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103299","DOI":"10.1109\/ACCESS.2024.3432574","article-title":"Forecasting and performance analysis of energy production in solar power plants using long short-term memory (LSTM) and random forest models","volume":"12","author":"Olcay","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, F., Wang, Y., Ren, J., and Zhou, Q. (2024, January 21\u201323). Multidimensional Temporal Photovoltaic Power Prediction Based on VMD-SSA-LSTM. Proceedings of the 2024 6th International Conference on Energy Systems and Electrical Power (ICESEP), Wuhan, China.","DOI":"10.1109\/ICESEP62218.2024.10651709"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Saha, S.K., and Mahajan, S.M. (2024, January 13\u201315). Multivariate Optimal Hybrid Deep Learning Model for Forecasting of Day-Ahead Solar Irradiance with Meteorological Constraints. Proceedings of the 2024 56th North American Power Symposium (NAPS), El Paso, TX, USA.","DOI":"10.1109\/NAPS61145.2024.10741707"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yuan, Y., Gong, Y., and Jiang, Y. (2024, January 18\u201320). The Short-Term Photovoltaic Power Prediction Model Based on FCM-BLS. Proceedings of the 2024 7th International Conference on Power and Energy Applications (ICPEA), Taiyuan, China.","DOI":"10.1109\/ICPEA63589.2024.10784950"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/JPHOTOV.2024.3453651","article-title":"Short-Term Photovoltaic Power Prediction Based on CEEMDAN and Hybrid Neural Networks","volume":"14","author":"Wu","year":"2024","journal-title":"IEEE J. Photovolt."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liang, H., Li, G., Xu, L., and Liu, Q. (2024, January 26\u201328). Short-Term Load Forecasting for A Power Supplying District Based on CEEMDAN-WPE-LSTM-Stacking Methods. Proceedings of the 2024 7th International Conference on Energy, Electrical and Power Engineering (CEEPE), Yangzhou, China.","DOI":"10.1109\/CEEPE62022.2024.10586323"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2526012","DOI":"10.1109\/TIM.2023.3310090","article-title":"Short-term PV power forecasting based on CEEMDAN and ensemble DeepTCN","volume":"72","author":"Huang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pan, Y., Wang, J., Li, P., Wang, L., Li, J., and Yin, Y. (2021, January 22\u201324). Photovoltaic power forecasting based on similar day theory and CEEMDAN-CSO-BP. Proceedings of the 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China.","DOI":"10.1109\/EI252483.2021.9713476"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wu, Y., Liu, J., Zhang, S., Li, G., and Zou, G. (2022, January 25\u201327). Ultra-Short-Term Photovoltaic Power Prediction Based on Improved Kmeans Algorithm and VMD-SVR-LSTM Model. Proceedings of the 2022 6th International Conference on Power and Energy Engineering (ICPEE), Shanghai, China.","DOI":"10.1109\/ICPEE56418.2022.10050294"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, S., Zhou, B., and Xu, B. (2022, January 23\u201325). Ultra-short-term forecast of photovoltaic power based on vmd error correction and cnn-gru-am. Proceedings of the 2022 3rd International Conference on Advanced Electrical and Energy Systems (AEES), Lanzhou, China.","DOI":"10.1109\/AEES56284.2022.10079672"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, G., Ding, C., Zhang, R., Chen, Y., Zhao, N., and Zhu, R. (2023, January 12\u201314). Short-Term Prediction of PV Power Based on Hybrid CNN\u2013BiLSTM-Attention Model and VMD. Proceedings of the 2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE), Guangzhou, China.","DOI":"10.1109\/CEEPE58418.2023.10166282"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Z., and Ju, Y. (2024, January 24\u201326). Short-Term Photovoltaic Power Prediction Based on VMD-mRMR and TCN-BIGRU-ATTENTION. Proceedings of the 2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT), Xi\u2019an, China.","DOI":"10.1109\/ISCIPT61983.2024.10673373"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xin, Y., Li, M., Hong, Y., Qiu, Y., Wu, H., and Wang, P. (2024, January 24\u201326). Digital Twin Model of Photovoltaic Power Generation Prediction based on VMD and Bi-LSTM. Proceedings of the 2024 8th International Conference on Power Energy Systems and Applications (ICoPESA), Hong Kong.","DOI":"10.1109\/ICOPESA61191.2024.10743454"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9002904","DOI":"10.1109\/TASC.2024.3465370","article-title":"Prediction Method of Direct Normal Irradiance for Solar Thermal Power Plants Based on VMD-WOA-DELM","volume":"34","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Appl. Supercond."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2156","DOI":"10.1016\/j.egyr.2024.08.008","article-title":"Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation","volume":"12","author":"Aljafari","year":"2024","journal-title":"Energy Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"144599","DOI":"10.1016\/j.jclepro.2024.144599","article-title":"The short-term forecasting of distributed photovoltaic power considering the sensitivity of meteorological data","volume":"486","author":"Ma","year":"2025","journal-title":"J. Clean. Prod."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:50:03Z","timestamp":1760028603000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":27,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030414"],"URL":"https:\/\/doi.org\/10.3390\/sym17030414","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,10]]}}}