{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:15:02Z","timestamp":1778602502742,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"China National Natural Science Foundation","doi-asserted-by":"publisher","award":["61972165"],"award-info":[{"award-number":["61972165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.<\/jats:p>","DOI":"10.3390\/s22249630","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:59:46Z","timestamp":1670558386000},"page":"9630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0405-9846","authenticated-orcid":false,"given":"Xianchao","family":"Guo","sequence":"first","affiliation":[{"name":"Fujian Province University Key Laboratory of Computational Science, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchang","family":"Mo","sequence":"additional","affiliation":[{"name":"Fujian Province University Key Laboratory of Computational Science, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143345","DOI":"10.1016\/j.scitotenv.2020.143345","article-title":"Impact of Energy Technology and Structural Change on Energy Demand in China","volume":"760","author":"Huang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_2","first-page":"100707","article-title":"An Analytical Study to Predict the Future of Pakistan\u2019s Energy Sustainability versus Rest of South Asia","volume":"39","author":"Rasheed","year":"2020","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.rser.2017.08.017","article-title":"Forecasting of Photovoltaic Power Generation and Model Optimization: A Review","volume":"81","author":"Das","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TSTE.2021.3123337","article-title":"Intra-Hour Photovoltaic Generation Forecasting Based on Multi-Source Data and Deep Learning Methods","volume":"13","author":"Yao","year":"2022","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109792","DOI":"10.1016\/j.rser.2020.109792","article-title":"A Review and Evaluation of the State-of-the-Art in PV Solar Power Forecasting: Techniques and Optimization","volume":"124","author":"Ahmed","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TSTE.2017.2694551","article-title":"Day-Ahead Prediction of Bihourly Solar Radiance with a Markov Switch Approach","volume":"8","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.renene.2016.06.018","article-title":"Combining Solar Irradiance Measurements, Satellite-Derived Data and a Numerical Weather Prediction Model to Improve Intra-Day Solar Forecasting","volume":"97","author":"Aguiar","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Singh, B., and Pozo, D. (October, January 29). A guide to solar power forecasting using ARMA models. Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania.","DOI":"10.1109\/ISGTEurope.2019.8905430"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.solener.2014.02.015","article-title":"Artificial Neural Network Model Based on Interrelationship of Direct, Diffuse and Global Solar Radiations","volume":"103","author":"Kaushika","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.solener.2010.02.006","article-title":"A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy","volume":"84","author":"Mellit","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9918","DOI":"10.1109\/TIE.2018.2856199","article-title":"Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8514","DOI":"10.1109\/TII.2021.3065425","article-title":"Multivariate Air Quality Forecasting with Nested Long Short Term Memory Neural Network","volume":"17","author":"Jin","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108822","DOI":"10.1016\/j.buildenv.2022.108822","article-title":"Air Quality Forecasting with Hybrid LSTM and Extended Stationary Wavelet Transform","volume":"213","author":"Zeng","year":"2022","journal-title":"Build. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6709779","DOI":"10.1155\/2022\/6709779","article-title":"Functional Data Approach for Short-Term Electricity Demand Forecasting","volume":"2022","author":"Shah","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/2372748","article-title":"Short-Term Solar Irradiance Prediction Based on Multichannel LSTM Neural Networks Using Edge-Based IoT System","volume":"2022","author":"Pi","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TSMC.2021.3093519","article-title":"Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting","volume":"52","author":"Jalali","year":"2022","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"126100","DOI":"10.1016\/j.energy.2022.126100","article-title":"Multivariate Wind Speed Forecasting Based on Multi-Objective Feature Selection Approach and Hybrid Deep Learning Model","volume":"263","author":"Lv","year":"2022","journal-title":"Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"157633","DOI":"10.1109\/ACCESS.2019.2949065","article-title":"A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households","volume":"7","author":"Yan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jpdc.2022.01.012","article-title":"Collaborative Deep Learning Framework on IoT Data with Bidirectional NLSTM Neural Networks for Energy Consumption Forecasting","volume":"163","author":"Yan","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101442","DOI":"10.1016\/j.aei.2021.101442","article-title":"Highly Accurate Energy Consumption Forecasting Model Based on Parallel LSTM Neural Networks","volume":"51","author":"Jin","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.renene.2019.05.039","article-title":"Smart Wind Speed Deep Learning Based Multi-Step Forecasting Model Using Singular Spectrum Analysis, Convolutional Gated Recurrent Unit Network and Support Vector Regression","volume":"143","author":"Liu","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.rser.2016.10.068","article-title":"Very Short-Term Photovoltaic Power Forecasting with Cloud Modeling: A Review","volume":"75","author":"Barbieri","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119682","DOI":"10.1016\/j.apenergy.2022.119682","article-title":"Quad-Kernel Deep Convolutional Neural Network for Intra-Hour Photovoltaic Power Forecasting","volume":"323","author":"Ren","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yan, K., Wang, X., Du, Y., Jin, N., Huang, H., and Zhou, H. (2018). Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy. Energies, 11.","DOI":"10.3390\/en11113089"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4557","DOI":"10.1049\/iet-gtd.2018.5847","article-title":"Hybrid Method for Short-Term Photovoltaic Power Forecasting Based on Deep Convolutional Neural Network","volume":"12","author":"Zang","year":"2018","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"78063","DOI":"10.1109\/ACCESS.2019.2923006","article-title":"Short-Term Photovoltaic Power Forecasting Based on Long Short-Term Memory Neural Network and Attention Mechanism","volume":"7","author":"Zhou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116225","DOI":"10.1016\/j.energy.2019.116225","article-title":"Photovoltaic Power Forecasting Based LSTM-Convolutional Network","volume":"189","author":"Wang","year":"2019","journal-title":"Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.isatra.2021.11.008","article-title":"Photovoltaic Power Prediction Based on Hybrid Modeling of Neural Network and Stochastic Differential Equation","volume":"128","author":"Zhang","year":"2022","journal-title":"ISA Trans."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3581037","DOI":"10.1155\/2022\/3581037","article-title":"Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches","volume":"2022","author":"Shah","year":"2022","journal-title":"J. Math."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6473","DOI":"10.1016\/j.egyr.2021.09.115","article-title":"Universities Power Energy Management: A Novel Hybrid Model Based on ICEEMDAN and Bayesian Optimized LSTM","volume":"7","author":"He","year":"2021","journal-title":"Energy Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102520","DOI":"10.1016\/j.resourpol.2021.102520","article-title":"Copper Price Forecasted by Hybrid Neural Network with Bayesian Optimization and Wavelet Transform","volume":"75","author":"Liu","year":"2022","journal-title":"Resour. Policy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115944","DOI":"10.1016\/j.enconman.2022.115944","article-title":"Short-Term Photovoltaic Power Forecasting Based on Signal Decomposition and Machine Learning Optimization","volume":"267","author":"Zhou","year":"2022","journal-title":"Energy Convers. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123574","DOI":"10.1016\/j.energy.2022.123574","article-title":"Online Prediction of Ultra-Short-Term Photovoltaic Power Using Chaotic Characteristic Analysis, Improved PSO and KELM","volume":"248","author":"Chen","year":"2022","journal-title":"Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.renene.2017.10.075","article-title":"Multi-Step-Ahead Wind Speed Forecasting Based on Optimal Feature Selection and a Modified Bat Algorithm with the Cognition Strategy","volume":"118","author":"Niu","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14997","DOI":"10.1016\/j.egyr.2022.11.051","article-title":"Short-Term Wind Speed Forecasting Based on Two-Stage Preprocessing Method, Sparrow Search Algorithm and Long Short-Term Memory Neural Network","volume":"8","author":"Ai","year":"2022","journal-title":"Energy Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1109\/TII.2019.2952917","article-title":"LSTM Learning with Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT","volume":"16","author":"Wu","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"126526","DOI":"10.1016\/j.jhydrol.2021.126526","article-title":"A Novel Attention-Based LSTM Cell Post-Processor Coupled with Bayesian Optimization for Streamflow Prediction","volume":"601","author":"Alizadeh","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s12667-019-00356-w","article-title":"Forecasting Next-Day Electricity Demand and Prices Based on Functional Models","volume":"11","author":"Lisi","year":"2020","journal-title":"Energy Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shah, I., Iftikhar, H., Ali, S., and Wang, D. (2019). Short-Term Electricity Demand Forecasting Using Components Estimation Technique. Energies, 12.","DOI":"10.3390\/en12132532"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108504","DOI":"10.1016\/j.ijepes.2022.108504","article-title":"Multi-Step Short-Term Wind Speed Forecasting Based on Multi-Stage Decomposition Coupled with Stacking-Ensemble Learning Approach","volume":"143","author":"Moreno","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., and Mohamed, A. (2013, January 8\u201312). Hybrid speech recognition with deep bidirectional LSTM. Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref_43","unstructured":"Moniz, J.R.A., and Krueger, D. (2018, January 15\u201317). Nested LSTMs. Proceedings of the Ninth Asian Conference on Machine Learning, Seoul, Republic of Korea."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"39409","DOI":"10.1007\/s11356-021-12657-8","article-title":"A Hybrid Deep Learning Technology for PM2.5 Air Quality Forecasting","volume":"28","author":"Zhang","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The performance of LSTM and BiLSTM in forecasting time series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational Mode Decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1007\/s00202-020-01135-y","article-title":"Long Short-Term Memory-Singular Spectrum Analysis-Based Model for Electric Load Forecasting","volume":"103","author":"Neeraj","year":"2021","journal-title":"Electr. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9630\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:36Z","timestamp":1760146596000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9630"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":47,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249630"],"URL":"https:\/\/doi.org\/10.3390\/s22249630","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,8]]}}}