{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T13:45:16Z","timestamp":1768311916768,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid growth in the use of Solar Energy for sustaining energy demand around the world requires accurate forecasts of Solar Irradiance to estimate the contribution of solar power to the power grid. Accurate forecasts for higher time horizons help to balance the power grid effectively and efficiently. Traditional forecasting techniques rely on physical weather parameters and complex mathematical models. However, these techniques are time-consuming and produce accurate results only for short forecast horizons. Deep Learning Techniques like Long Short Term Memory (LSTM) networks are employed to learn and predict complex varying time series data. However, LSTM networks are susceptible to poor performance due to improper configuration of hyperparameters. This work introduces two new algorithms for hyperparameter tuning of LSTM networks and a Fast Fourier Transform (FFT) based data decomposition technique. This work also proposes an optimised workflow for training LSTM networks based on the above techniques. The results show a significant fitness increase from 81.20% to 95.23% and a 53.42% reduction in RMSE for 90 min ahead forecast after using the optimised training workflow. The results were compared to several other techniques for forecasting solar energy for multiple forecast horizons.<\/jats:p>","DOI":"10.3390\/rs15082076","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T09:23:43Z","timestamp":1681464223000},"page":"2076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6155-0300","authenticated-orcid":false,"given":"Harshal","family":"Dhake","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal 575025, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8425-7430","authenticated-orcid":false,"given":"Yashwant","family":"Kashyap","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal 575025, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0467-1835","authenticated-orcid":false,"given":"Panagiotis","family":"Kosmopoulos","sequence":"additional","affiliation":[{"name":"Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD\/NOA), 15236 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.renene.2016.01.020","article-title":"Short term solar irradiance forecasting using a mixed wavelet neural network","volume":"90","author":"Sharma","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.energy.2018.07.168","article-title":"Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques","volume":"161","author":"Wang","year":"2018","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0169-2070(03)00004-9","article-title":"Combining time series models for forecasting","volume":"20","author":"Zou","year":"2004","journal-title":"Int. J. Forecast."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ijforecast.2003.10.004","article-title":"Forecasting economic and financial time-series with non-linear models","volume":"20","author":"Clements","year":"2004","journal-title":"Int. J. Forecast."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/adgeo-45-139-2018","article-title":"A stochastic model for the hourly solar radiation process for application in renewable resources management","volume":"45","author":"Koudouris","year":"2018","journal-title":"Adv. Geosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Colak, I., Yesilbudak, M., Genc, N., and Bayindir, R. (2015, January 9\u201311). Multi-period prediction of solar radiation using ARMA and ARIMA models. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.33"},{"key":"ref_7","first-page":"100427","article-title":"Spatial forecasting of solar radiation using ARIMA model","volume":"20","author":"Shadab","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1007\/s11831-021-09695-3","article-title":"Weather forecasting for renewable energy system: A review","volume":"29","author":"Meenal","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Siami Namin, A. (2018, January 17\u201320). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4167","DOI":"10.1007\/s11269-021-02937-w","article-title":"Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting","volume":"35","author":"Rahimzad","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_12","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-Asia (ISGT Asia), Singapore.","DOI":"10.1109\/ISGT-Asia.2018.8467934"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116022","DOI":"10.1016\/j.enconman.2022.116022","article-title":"HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting","volume":"268","author":"Ewees","year":"2022","journal-title":"Energy Convers. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e1484","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges","volume":"13","author":"Bischl","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_15","unstructured":"Falkner, S., Klein, A., and Hutter, F. (2018, January 10\u201315). BOHB: Robust and efficient hyperparameter optimization at scale. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_16","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., and K\u00e9gl, B. (2011). Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gorgolis, N., Hatzilygeroudis, I., Istenes, Z., and Gyenne, L.G. (2019, January 15\u201317). Hyperparameter optimization of LSTM network models through genetic algorithm. Proceedings of the 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), Patras, Greece.","DOI":"10.1109\/IISA.2019.8900675"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chung, H., and Shin, K.S. (2018). Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability, 10.","DOI":"10.3390\/su10103765"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ali, M.A., P.P., F.R., and Abd Elminaam, D.S. (2022). An Efficient Heap Based Optimizer Algorithm for Feature Selection. Mathematics, 10.","DOI":"10.3390\/math10142396"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101728","DOI":"10.1016\/j.asej.2022.101728","article-title":"An efficient heap-based optimizer for parameters identification of modified photovoltaic models","volume":"13","author":"AbdElminaam","year":"2022","journal-title":"Ain Shams Eng. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11908","DOI":"10.1016\/j.ijhydene.2021.01.076","article-title":"An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies","volume":"46","author":"Mohamed","year":"2021","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"83695","DOI":"10.1109\/ACCESS.2021.3087449","article-title":"A novel heap-based optimizer for scheduling of large-scale combined heat and power economic dispatch","volume":"9","author":"Ginidi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113702","DOI":"10.1016\/j.eswa.2020.113702","article-title":"Heap-based optimizer inspired by corporate rank hierarchy for global optimization","volume":"161","author":"Askari","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kumar, A., Kashyap, Y., and Kosmopoulos, P. (2022). Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach. Remote Sens., 15.","DOI":"10.3390\/rs15010107"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.ijforecast.2008.08.007","article-title":"Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models","volume":"24","author":"Jursa","year":"2008","journal-title":"Int. J. Forecast."},{"key":"ref_26","first-page":"15","article-title":"Fourier transforms and the fast Fourier transform (FFT) algorithm","volume":"2","author":"Heckbert","year":"1995","journal-title":"Comput. Graph."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/MAP.2007.4293982","article-title":"Numerical Fourier transforms: DFT and FFT","volume":"49","author":"Sevgi","year":"2007","journal-title":"IEEE Antennas Propag. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"118222","DOI":"10.1016\/j.eswa.2022.118222","article-title":"Heap-based optimizer based on three new updating strategies","volume":"209","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: Past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lambora, A., Gupta, K., and Chopra, K. (2019, January 14\u201316). Genetic algorithm-A literature review. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India.","DOI":"10.1109\/COMITCon.2019.8862255"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fentis, A., Bahatti, L., Mestari, M., and Chouri, B. (2017, January 25\u201328). Short-term solar power forecasting using Support Vector Regression and feed-forward NN. Proceedings of the 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), Strasbourg, France.","DOI":"10.1109\/NEWCAS.2017.8010191"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Serttas, F., Hocaoglu, F.O., and Akarslan, E. (2018, January 4\u20136). Short term solar power generation forecasting: A novel approach. Proceedings of the 2018 International Conference on Photovoltaic Science and Technologies (PVCon), Ankara, Turkey.","DOI":"10.1109\/PVCon.2018.8523919"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.renene.2022.07.136","article-title":"Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad","volume":"198","author":"Haider","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lai, J.P., Chang, Y.M., Chen, C.H., and Pai, P.F. (2020). A survey of machine learning models in renewable energy predictions. Appl. Sci., 10.","DOI":"10.3390\/app10175975"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, T., Jin, H., Li, A., Fang, H., Wei, D., Xie, X., and Nan, X. (2022). Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method. Remote Sens., 14.","DOI":"10.3390\/rs14194733"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ijforecast.2019.08.006","article-title":"Artificial bee colony-based combination approach to forecasting agricultural commodity prices","volume":"38","author":"Wang","year":"2022","journal-title":"Int. J. Forecast."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"120069","DOI":"10.1016\/j.energy.2021.120069","article-title":"A novel genetic LSTM model for wind power forecast","volume":"223","author":"Shahid","year":"2021","journal-title":"Energy"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2076\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:16:04Z","timestamp":1760123764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,14]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082076"],"URL":"https:\/\/doi.org\/10.3390\/rs15082076","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,14]]}}}