{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T03:05:05Z","timestamp":1772075105432,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Softex in partnership with Centro de Inova\u00e7\u00e3o Edge"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Macei\u00f3 (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.<\/jats:p>","DOI":"10.3390\/s23031357","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T01:30:30Z","timestamp":1674696630000},"page":"1357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4065-5004","authenticated-orcid":false,"given":"Carlos Henrique Torres de","family":"Andrade","sequence":"first","affiliation":[{"name":"Computing Institute, A. C. Sim\u00f5es Campus, Federal University of Alagoas\u2014UFAL, Macei\u00f3 57072-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4111-8721","authenticated-orcid":false,"given":"Gustavo Costa Gomes de","family":"Melo","sequence":"additional","affiliation":[{"name":"Computing Institute, A. C. Sim\u00f5es Campus, Federal University of Alagoas\u2014UFAL, Macei\u00f3 57072-970, Brazil"}]},{"given":"Tiago Figueiredo","family":"Vieira","sequence":"additional","affiliation":[{"name":"Center of Agrarian Sciences, Engineering and Agricultural Sciences Campus, Federal University of Alagoas\u2014UFAL, Rio Largo 57100-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-4946","authenticated-orcid":false,"given":"\u00cdcaro Bezzera Queiroz de","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Computing Institute, A. C. Sim\u00f5es Campus, Federal University of Alagoas\u2014UFAL, Macei\u00f3 57072-970, Brazil"}]},{"given":"Allan de","family":"Medeiros Martins","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Center of Technology, Federal University of Rio Grande do Norte\u2014UFRN, Natal 59072-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0643-737X","authenticated-orcid":false,"given":"Igor Cavalcante","family":"Torres","sequence":"additional","affiliation":[{"name":"Center of Agrarian Sciences, Engineering and Agricultural Sciences Campus, Federal University of Alagoas\u2014UFAL, Rio Largo 57100-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6389-4895","authenticated-orcid":false,"given":"Davi Bibiano","family":"Brito","sequence":"additional","affiliation":[{"name":"Computing Institute, A. C. Sim\u00f5es Campus, Federal University of Alagoas\u2014UFAL, Macei\u00f3 57072-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1385","authenticated-orcid":false,"given":"Alana Kelly Xavier","family":"Santos","sequence":"additional","affiliation":[{"name":"Center of Agrarian Sciences, Engineering and Agricultural Sciences Campus, Federal University of Alagoas\u2014UFAL, Rio Largo 57100-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"ref_1","unstructured":"REN21 Secretariat (2021). Renewables 2021 Global Status Report, UNEP. Technical Report."},{"key":"ref_2","unstructured":"Brazilian Ministry of Mines and Energy (2022, November 12). Monthly Bulletin on Monitoring the Brazilian Electrical System, Available online: https:\/\/www.gov.br\/mme\/pt-br\/assuntos\/secretarias\/energia-eletrica\/publicacoes\/boletim-de-monitoramento-do-sistema-eletrico\/2022\/boletim-de-monitoramento-do-sistema-eletrico-abr-2022.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Teo, T.T., Logenthiran, T., and Woo, W.L. (2015, January 3\u20136). Forecasting of photovoltaic power using extreme learning machine. Proceedings of the 2015 IEEE Innovative Smart Grid Technologies\u2014Asia (ISGT ASIA), Bangkok, Thailand.","DOI":"10.1109\/ISGT-Asia.2015.7387113"},{"key":"ref_4","first-page":"428","article-title":"Solar photovoltaic power forecasting using optimized modified extreme learning machine technique","volume":"21","author":"Behera","year":"2018","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_5","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","volume":"81","author":"Das","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.solener.2016.06.073","article-title":"On recent advances in PV output power forecast","volume":"136","author":"Raza","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.rser.2018.02.007","article-title":"Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting","volume":"87","author":"Notton","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/TSTE.2016.2577559","article-title":"Distribution Voltage Regulation Through Active Power Curtailment With PV Inverters and Solar Generation Forecasts","volume":"8","author":"Ghosh","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MPE.2017.2729100","article-title":"Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors","volume":"15","author":"Dobschinski","year":"2017","journal-title":"IEEE Power Energy Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1007\/s00521-017-3225-z","article-title":"Accurate photovoltaic power forecasting models using deep LSTM-RNN","volume":"31","author":"Mahmoud","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lim, W.T., Wang, L., Wang, Y., and Chang, Q. (2016, January 13\u201315). Housing price prediction using neural networks. Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603227"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wu, J., and Wang, Z. (2022). A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory. Water, 14.","DOI":"10.3390\/w14040610"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38","DOI":"10.17775\/CSEEJPES.2015.00046","article-title":"Photovoltaic and solar power forecasting for smart grid energy management","volume":"1","author":"Wan","year":"2015","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_14","unstructured":"Sabino, E.R.C. (2018, January 17\u201320). Previs\u00e3o de radia\u00e7\u00e3o solar e temperatura ambiente voltada para auxiliar a opera\u00e7\u00e3o de usina fotovoltaicas. Proceedings of the VII Brazilian Solar Energy Congress, Online."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"01004","DOI":"10.1051\/e3sconf\/20186901004","article-title":"Solar Power Prediction via Support Vector Machine and Random Forest","volume":"69","author":"Yen","year":"2018","journal-title":"E3S Web Conf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1016\/j.solener.2011.08.025","article-title":"Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed","volume":"85","author":"Chow","year":"2011","journal-title":"Sol. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.solener.2013.10.002","article-title":"A hybrid model (SARIMA\u2013SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant","volume":"98","author":"Bouzerdoum","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.renene.2017.02.052","article-title":"Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble","volume":"108","author":"Cervone","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_19","unstructured":"Aghaei, M. (2021). Solar Radiation, IntechOpen. Chapter 9."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Watetakarn, S., and Premrudeepreechacharn, S. (2015, January 3\u20135). Forecasting of solar irradiance for solar power plants by artificial neural network. Proceedings of the 2015 IEEE Innovative Smart Grid Technologies\u2014Asia (ISGT ASIA), Bangkok, Thailand.","DOI":"10.1109\/ISGT-Asia.2015.7387180"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.1468-0394.2004.00272.x","article-title":"Short-term electric power load forecasting using feedforward neural networks","volume":"21","author":"Malki","year":"2004","journal-title":"Expert Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17174","DOI":"10.1109\/ACCESS.2021.3053638","article-title":"A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De, V., Teo, T.T., Woo, W.L., and Logenthiran, T. (2018, January 22\u201325). Photovoltaic Power Forecasting using LSTM on Limited Dataset. Proceedings of the 2018 IEEE Innovative Smart Grid Technologies\u2014Asia (ISGT Asia), Singapore.","DOI":"10.1109\/ISGT-Asia.2018.8467934"},{"key":"ref_25","unstructured":"Gabriel, I., Gomes, G., Araujo, I., Barboza, E., Vieira, T., and Brito, D. (2020). Proceedings of the VIII Congresso Brasileiro de Energia Solar-CBENS 2020, ANAIS CBENS."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Harrou, F., and Sun, Y. (2020). Proceedings of the Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems, IntechOpen.","DOI":"10.5772\/intechopen.85999"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chi, Y., and Xiao, L. (2018, January 23\u201325). Solar Power Generation Forecast Based on LSTM. Proceedings of the 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2018.8663788"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.egyr.2021.09.167","article-title":"Photovoltaic power prediction of LSTM model based on Pearson feature selection","volume":"7","author":"Chen","year":"2021","journal-title":"Energy Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107908","DOI":"10.1016\/j.epsr.2022.107908","article-title":"CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production","volume":"208","author":"Agga","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Masuko, T. (2017, January 16\u201320). Computational cost reduction of long short-term memory based on simultaneous compression of input and hidden state. Proceedings of the 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Okinawa, Japan.","DOI":"10.1109\/ASRU.2017.8268926"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.solener.2011.11.013","article-title":"Short\u2013mid-term solar power prediction by using artificial neural networks","volume":"86","author":"Durna","year":"2012","journal-title":"Sol. Energy"},{"key":"ref_32","unstructured":"Chollet, F. (2022, November 12). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_33","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013). API design for machine learning software: Experiences from the scikit-learn project. arXiv."},{"key":"ref_34","unstructured":"Dewancker, I., McCourt, M., and Clark, S. (2022, November 26). Bayesian Optimization Primer. Available online: https:\/\/sigopt.com\/static\/pdf\/SigOpt_Bayesian_Optimization_Primer.pdf."},{"key":"ref_35","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_36","first-page":"28","article-title":"The Generalization of \u2018Student\u2019s\u2019 Problem when Several Different Population Variances are Involved","volume":"34","author":"Welch","year":"1947","journal-title":"Biometrika"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.rser.2019.03.040","article-title":"Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression","volume":"108","author":"Sharifzadeh","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Meng, M., and Song, C. (2020). Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter. Sustainability, 12.","DOI":"10.3390\/su12062247"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liao, W., and Chang, Y. (2018). Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting. Energies, 11.","DOI":"10.3390\/en11082163"},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:15:41Z","timestamp":1760120141000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031357"],"URL":"https:\/\/doi.org\/10.3390\/s23031357","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,25]]}}}