{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T17:23:41Z","timestamp":1763227421675,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa B\u00e1rbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors.<\/jats:p>","DOI":"10.3390\/app14135769","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T11:33:53Z","timestamp":1719920033000},"page":"5769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancing Weather Forecasting Integrating LSTM and GA"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5694-2173","authenticated-orcid":false,"given":"Rita","family":"Teixeira","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7494-6566","authenticated-orcid":false,"given":"Adelaide","family":"Cerveira","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"INEC-TEC UTAD Pole, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3224-4926","authenticated-orcid":false,"given":"Eduardo J. Solteiro","family":"Pires","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"INEC-TEC UTAD Pole, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0297-4709","authenticated-orcid":false,"given":"Jos\u00e9","family":"Baptista","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"INEC-TEC UTAD Pole, University of Tr\u00e1s-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"ref_1","first-page":"3393","article-title":"Deterministic weather forecasting models based on intelligent predictors: A survey","volume":"34","author":"Jaseena","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_2","first-page":"31","article-title":"Weather Variability Forecasting Model through Data Mining Techniques","volume":"11","author":"Shekana","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jain, H., and Jain, R. (2017, January 23\u201325). Big data in weather forecasting: Applications and challenges. Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, India.","DOI":"10.1109\/ICBDACI.2017.8070824"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2019.12.030","article-title":"Transductive LSTM for time-series prediction: An application to weather forecasting","volume":"125","author":"Karevan","year":"2020","journal-title":"Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"van Buuren, S. (2018). Flexible Imputation of Missing Data, CRC Press. [2nd ed.].","DOI":"10.1201\/9780429492259"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Doreswamy, I.G., and Manjunatha, B. (2017, January 13\u201316). Performance evaluation of predictive models for missing data imputation in weather data. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126025"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.25046\/aj050217","article-title":"Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction","volume":"5","author":"Mittal","year":"2020","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_8","first-page":"7","article-title":"Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks","volume":"143","author":"Zaytar","year":"2016","journal-title":"Int. J. Comput. Appl."},{"key":"ref_9","unstructured":"Rojas, I., Joya, G., and Catala, A. (2019, January 27). A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. Proceedings of the Advances in Computational Intelligence, San Luis Potosi, Mexico."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Santra, A.S., and Lin, J.L. (2019). Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting. Energies, 12.","DOI":"10.3390\/en12112040"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, W., Zang, C., Liu, D., and Zeng, P. (November, January 30). Short-term Load Forecasting of Long-short Term Memory Neural Network Based on Genetic Algorithm. Proceedings of the 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China.","DOI":"10.1109\/EI250167.2020.9346907"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bouktif, S., Fiaz, A., Ouni, A., and Serhani, M.A. (2018). Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies, 11.","DOI":"10.3390\/en11071636"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119361","DOI":"10.1016\/j.energy.2020.119361","article-title":"A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting","volume":"217","author":"Jiang","year":"2021","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.38094\/jastt1457","article-title":"A Review on Linear Regression Comprehensive in Machine Learning","volume":"1","author":"Maulud","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17754","DOI":"10.1038\/s41598-021-97221-7","article-title":"An interaction regression model for crop yield prediction","volume":"11","author":"Ansarifar","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Barriguinha, A., de Castro Neto, M., and Gil, A. (2021). Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review. Agronomy, 11.","DOI":"10.3390\/agronomy11091789"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"00368504221132144","DOI":"10.1177\/00368504221132144","article-title":"Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems","volume":"105","author":"Amir","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118936","DOI":"10.1016\/j.apenergy.2022.118936","article-title":"Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain","volume":"314","author":"Carneiro","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103426","DOI":"10.1016\/j.advengsoft.2023.103426","article-title":"New ridge regression, artificial neural networks and support vector machine for wind speed prediction","volume":"179","author":"Zheng","year":"2023","journal-title":"Adv. Eng. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109725","DOI":"10.1016\/j.rser.2020.109725","article-title":"A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea","volume":"122","author":"Nam","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Phan, Q.T., Wu, Y.K., and Phan, Q.D. (2021, January 16\u201319). Short-term Solar Power Forecasting Using XGBoost with Numerical Weather Prediction. Proceedings of the 2021 IEEE International Future Energy Electronics Conference (IFEEC), Taipei, Taiwan.","DOI":"10.1109\/IFEEC53238.2021.9661874"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wadhwa, S., and Tiwari, R.G. (2023, January 28\u201329). Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms. Proceedings of the 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), Bhopal, India.","DOI":"10.1109\/APSIT58554.2023.10201679"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Galindo Padilha, G.A., Ko, J., Jung, J.J., and de Mattos Neto, P.S.G. (2022). Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy. Appl. Sci., 12.","DOI":"10.3390\/app122110985"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127678","DOI":"10.1016\/j.energy.2023.127678","article-title":"A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy","volume":"278","author":"Nascimento","year":"2023","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Walczewski, M.J., and W\u00f6hrle, H. (2024). Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany. Energies, 17.","DOI":"10.3390\/en17040844"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"117440","DOI":"10.1016\/j.enconman.2023.117440","article-title":"Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources","volume":"293","author":"Pineda","year":"2023","journal-title":"Energy Convers. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"125888","DOI":"10.1016\/j.energy.2022.125888","article-title":"An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events","volume":"263","author":"Cui","year":"2023","journal-title":"Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106996","DOI":"10.1016\/j.asoc.2020.106996","article-title":"A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer","volume":"100","author":"Altan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","article-title":"A Review on the Long Short-Term Memory Model","volume":"53","author":"Mosquera","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Thirunavukkarasu, G.S., Kalair, A.R., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., and Stojcevski, A. (2022, January 5\u20138). Very Short-Term Solar Irradiance Forecasting using Multilayered Long-Short Term Memory. Proceedings of the 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), Split\/Bol, Croatia.","DOI":"10.23919\/SpliTech55088.2022.9854244"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.renene.2021.12.100","article-title":"Short-term offshore wind power forecasting\u2014A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)","volume":"185","author":"Zhang","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Salehin, I., Talha, I.M., Mehedi Hasan, M., Dip, S.T., Saifuzzaman, M., and Moon, N.N. (2020, January 26\u201327). An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network. Proceedings of the 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India.","DOI":"10.1109\/WIECON-ECE52138.2020.9398022"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112364","DOI":"10.1016\/j.rser.2022.112364","article-title":"Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction","volume":"161","author":"Markovics","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118993","DOI":"10.1016\/j.renene.2023.118993","article-title":"Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction","volume":"215","author":"Yang","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"119270","DOI":"10.1016\/j.eswa.2022.119270","article-title":"DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)","volume":"213","author":"Venkatachalam","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_37","unstructured":"Van Rossum, G., and Drake, F.L. (2009). Python 3 Reference Manual, CreateSpace."},{"key":"ref_38","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201916), Savannah, GA, USA."},{"key":"ref_39","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/13\/5769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:08:52Z","timestamp":1760108932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/13\/5769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,1]]},"references-count":39,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["app14135769"],"URL":"https:\/\/doi.org\/10.3390\/app14135769","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2024,7,1]]}}}