{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T08:11:00Z","timestamp":1770019860488,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"university Rey Juan Carlos (Spain)"},{"name":"CNPq 309737\/2021-4"},{"name":"FAPES-2021-WMR44"},{"name":"FAPES-2022-BWBR2"},{"name":"NiDA Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).<\/jats:p>","DOI":"10.3390\/s24010085","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:49:27Z","timestamp":1703450967000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Application of AI for Short-Term PV Generation Forecast"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6215-664X","authenticated-orcid":false,"given":"Helder R. O.","family":"Rocha","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Av. Fernando Ferrari, 514, Vit\u00f3ria 29075-910, ES, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8355-6383","authenticated-orcid":false,"given":"Rodrigo","family":"Fiorotti","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Av. Fernando Ferrari, 514, Vit\u00f3ria 29075-910, ES, Brazil"},{"name":"Department of Electrical Engineering, Federal Institute of Esp\u00edrito Santo, S\u00e3o Mateus 29932-540, ES, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4785-556X","authenticated-orcid":false,"given":"Jussara F.","family":"Fardin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Esp\u00edrito Santo, Av. Fernando Ferrari, 514, Vit\u00f3ria 29075-910, ES, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8354-9354","authenticated-orcid":false,"given":"Hilel","family":"Garcia-Pereira","sequence":"additional","affiliation":[{"name":"Higher School of Experimental Sciences and Technology, University of Rey Juan Carlos, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-6801","authenticated-orcid":false,"given":"Yann E.","family":"Bouvier","sequence":"additional","affiliation":[{"name":"Higher School of Experimental Sciences and Technology, University of Rey Juan Carlos, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7592-9514","authenticated-orcid":false,"given":"Alba","family":"Rodr\u00edguez-Lorente","sequence":"additional","affiliation":[{"name":"Higher School of Experimental Sciences and Technology, University of Rey Juan Carlos, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5634-533X","authenticated-orcid":false,"given":"Imene","family":"Yahyaoui","sequence":"additional","affiliation":[{"name":"Higher School of Experimental Sciences and Technology, University of Rey Juan Carlos, 28933 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"ref_1","unstructured":"Fardin, J.F., de Oliveira Rocha, H.R., Donadel, C.B., and Fiorotti, R. (2018). Advances in Renewable Energies and Power Technologies, Elsevier."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116145","DOI":"10.1016\/j.apenergy.2020.116145","article-title":"An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes","volume":"282","author":"Rocha","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.ref.2023.04.005","article-title":"Demand planning of a nearly zero energy building in a PV\/grid-connected system","volume":"45","author":"Fiorotti","year":"2023","journal-title":"Renew. Energy Focus"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1755-1315\/726\/1\/012001","article-title":"Crystalline Silicon vs. Amorphous Silicon: The Significance of Structural Differences in Photovoltaic Applications","volume":"726","author":"Kang","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/TLA.2018.8362148","article-title":"Forecast of distributed electrical generation system capacity based on seasonal micro generators using ELM and PSO","volume":"16","author":"Rocha","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"122348","DOI":"10.1016\/j.apenergy.2023.122348","article-title":"Net Zero Energy cost Building system design based on Artificial Intelligence","volume":"355","author":"Rocha","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sumega, M., Bou Ezzeddine, A., Grmanov\u00e1, G., and Rozinajov\u00e1, V. (2020, January 14\u201320). Prediction of photovoltaic power using nature-inspired computing. Proceedings of the Advances in Swarm Intelligence: 11th International Conference, ICSI 2020, Belgrade, Serbia.","DOI":"10.1007\/978-3-030-53956-6_3"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s40095-023-00560-6","article-title":"Development of a day-ahead solar power forecasting model chain for a 250 MW PV Park in India","volume":"14","author":"Roy","year":"2023","journal-title":"Int. J. Energy Environ. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, Y., Ferrier, N.J., Collis, S.M., Sankaran, R., and Beckman, P.H. (2021). Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods. Atmosphere, 12.","DOI":"10.3390\/atmos12030395"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1016\/j.matpr.2020.08.449","article-title":"Short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant","volume":"39","author":"Das","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6777488","DOI":"10.1155\/2021\/6777488","article-title":"Forecasting of energy production for photovoltaic systems based on Arima and ann advanced models","volume":"2021","author":"Fara","year":"2021","journal-title":"Int. J. Photoenergy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"733","DOI":"10.3390\/en6020733","article-title":"A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control","volume":"6","author":"Bracale","year":"2013","journal-title":"Energies"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TSTE.2020.2993524","article-title":"Probabilistic Solar Power Forecasting Using Bayesian Model Averaging","volume":"12","author":"Doubleday","year":"2021","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1049\/tje2.12015","article-title":"Improved markov-chain-based ultra-short-term PV forecasting method for Enhancing Power System Resilience","volume":"2021","author":"Bai","year":"2021","journal-title":"J. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, L., Chen, X., and Guo, L. (2021, January 22\u201324). Photovoltaic Power Prediction Method Based on Markov Chain and Combined Model. Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA51329.2021.9362608"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sherratt, F., Plummer, A., and Iravani, P. (2021). Understanding LSTM network behaviour of IMU-based locomotion mode recognition for applications in prostheses and wearables. Sensors, 21.","DOI":"10.3390\/s21041264"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"60039","DOI":"10.1109\/ACCESS.2022.3179577","article-title":"Timedistributed-cnn-lstm: A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study","volume":"10","author":"Montaha","year":"2022","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"38287","DOI":"10.1109\/ACCESS.2019.2907000","article-title":"Modeling vehicle interactions via modified LSTM models for trajectory prediction","volume":"7","author":"Dai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhang, S., and Xu, X. (2022, January 21\u201324). Research on Fault Prognostic of Photovoltaic System Based on LSTM-SA. Proceedings of the 2022 13th International Conference on Reliability, Maintainability and Safety (ICRMS), Hong Kong, China.","DOI":"10.1109\/ICRMS55680.2022.9944602"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Roy, K., Ishmam, A., and Taher, K.A. (2021, January 8\u20139). Demand forecasting in smart grid using long short-term memory. Proceedings of the 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh.","DOI":"10.1109\/ACMI53878.2021.9528277"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sauter, E., Mughal, M., and Zhang, Z. (2023). Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction. Energies, 16.","DOI":"10.3390\/en16134908"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jakopli\u0107, A., Frankovi\u0107, D., Havelka, J., and Bulat, H. (2023). Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning. Energies, 16.","DOI":"10.3390\/en16145428"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, D., Zhang, C., Li, Q., Han, H., Huang, D., Li, T., and Wang, C. (November, January 30). Prediction of solar photovoltaic power generation based on MLP and LSTM neural networks. Proceedings of the 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China.","DOI":"10.1109\/EI250167.2020.9347223"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.egyr.2023.04.288","article-title":"Short-term prediction of wind power based on phase space reconstruction and BiLSTM","volume":"9","author":"Ying","year":"2023","journal-title":"Energy Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zheng, X., Wu, J., and Ye, Z. (2020, January 18\u201320). An End-To-End CNN-BiLSTM Attention Model for Gearbox Fault Diagnosis. Proceedings of the 2020 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China.","DOI":"10.1109\/PIC50277.2020.9350844"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"128762","DOI":"10.1016\/j.energy.2023.128762","article-title":"Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model","volume":"285","author":"Zhang","year":"2023","journal-title":"Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neucom.2022.06.117","article-title":"Multi-step prediction of photovoltaic power based on two-stage decomposition and BILSTM","volume":"504","author":"Lin","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gu, B., Li, X., Xu, F., Yang, X., Wang, F., and Wang, P. (2023). Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM. Sustainability, 15.","DOI":"10.3390\/su15086538"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhou, M., Zhang, S., Yang, X., Zhang, S., and Liu, H. (2021, January 28\u201330). Research on PV Power Forecasting Based on Wavelet Decomposition and Temporal Convolutional Networks. Proceedings of the 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China.","DOI":"10.1109\/CIEEC50170.2021.9510374"},{"key":"ref_30","unstructured":"Torres, J.F., Jim\u00e9nez-Navarro, M., Mart\u00ednez-\u00c1lvarez, F., and Troncoso, A. (2021, January 22\u201324). Electricity consumption time series forecasting using temporal convolutional networks. Proceedings of the Advances in Artificial Intelligence: 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020\/2021, M\u00e1laga, Spain. Proceedings 19."},{"key":"ref_31","unstructured":"Zhang, H., Hu, W., Cao, D., Huang, Q., Chen, Z., and Blaabjerg, F. (Csee J. Power Energy Syst., 2021). A temporal convolutional network based hybrid model of short-term electricity price forecasting, Csee J. Power Energy Syst., in press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121981","DOI":"10.1016\/j.energy.2021.121981","article-title":"Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks","volume":"238","author":"Li","year":"2022","journal-title":"Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.egyr.2020.11.219","article-title":"Short-term prediction for wind power based on temporal convolutional network","volume":"6","author":"Zhu","year":"2020","journal-title":"Energy Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"31692","DOI":"10.1109\/ACCESS.2022.3160484","article-title":"Solar power forecasting using deep learning techniques","volume":"10","author":"Elsaraiti","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.procs.2017.09.045","article-title":"Solar irradiance forecasting using deep neural networks","volume":"114","author":"Alzahrani","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, M.Y., Chiang, H.S., and Chang, C.Y. (2022, January 14\u201316). Solar Photovoltaic Power Generation Prediction based on Deep Learning Methods. Proceedings of the 2022 IET International Conference on Engineering Technologies and Applications (IET-ICETA), Changhua, Taiwan.","DOI":"10.1109\/IET-ICETA56553.2022.9971676"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ref.2023.04.010","article-title":"Improving PV power plant forecast accuracy: A hybrid deep learning approach compared across short, medium and long-term horizons","volume":"45","author":"Sadeghi","year":"2023","journal-title":"Renew. Energy Focus"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"107371","DOI":"10.1016\/j.cie.2021.107371","article-title":"Prediction of time series using an analysis filter bank of LSTM units","volume":"157","author":"Mejia","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, K., Peng, X., Li, Z., Cui, W., Yuan, H., Lai, C.S., and Lai, L.L. (2022). A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection. Energies, 15.","DOI":"10.3390\/en15155410"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bou-Rabee, M.A., Naz, M.Y., Albalaa, I.E., and Sulaiman, S.A. (2022). BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones. Energies, 15.","DOI":"10.3390\/en15062226"},{"key":"ref_44","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., and Hager, G.D. (2017, January 21\u201326). Temporal convolutional networks for action segmentation and detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chan, W., Jaitly, N., Le, Q., and Vinyals, O. (2016, January 20\u201325). Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472621"},{"key":"ref_47","unstructured":"Lin, Z., Feng, M., Santos, C.N.d., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017). A structured self-attentive sentence embedding. arXiv."},{"key":"ref_48","unstructured":"Tran, D., Wang, H., Torresani, L., and Feiszli, M. (November, January 27). Video classification with channel-separated convolutional networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nan, M., Tr\u0103sc\u0103u, M., Florea, A.M., and Iacob, C.C. (2021). Comparison between recurrent networks and temporal convolutional networks approaches for skeleton-based action recognition. Sensors, 21.","DOI":"10.3390\/s21062051"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shang, K., Cui, Z., Zhang, Z., and Zhang, F. (2023). Research on traffic flow prediction at intersections based on DT-TCN-attention. Sensors, 23.","DOI":"10.3390\/s23156683"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"16453","DOI":"10.1007\/s00500-020-04954-0","article-title":"Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station","volume":"24","author":"Hewage","year":"2020","journal-title":"Soft Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/85\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:57Z","timestamp":1760132457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/85"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,23]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010085"],"URL":"https:\/\/doi.org\/10.3390\/s24010085","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,23]]}}}