{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T21:48:42Z","timestamp":1774561722093,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.<\/jats:p>","DOI":"10.3390\/bdcc8030023","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T06:50:23Z","timestamp":1708930223000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Solar and Wind Data Recognition: Fourier Regression for Robust Recovery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4191-893X","authenticated-orcid":false,"given":"Abdullah F.","family":"Al-Aboosi","sequence":"first","affiliation":[{"name":"Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Aldo Jonathan","family":"Mu\u00f1oz Vazquez","sequence":"additional","affiliation":[{"name":"Department of Multidisciplinary Engineering, Texas A&M University, McAllen, TX 78504, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1581-5566","authenticated-orcid":false,"given":"Fadhil Y.","family":"Al-Aboosi","sequence":"additional","affiliation":[{"name":"RAPID Manufacturing Institute-AIChE, New York, NY 10005, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-2281","authenticated-orcid":false,"given":"Mahmoud","family":"El-Halwagi","sequence":"additional","affiliation":[{"name":"The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Wei","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109337","DOI":"10.1016\/j.jenvman.2019.109337","article-title":"Solar PV adoption in wastewater treatment plants: A review of practice in California","volume":"248","author":"Strazzabosco","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119217","DOI":"10.1016\/j.energy.2020.119217","article-title":"Eco-energetic feasibility study of using grid-connected photovoltaic system in wastewater treatment plant","volume":"216","author":"Bey","year":"2021","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10548","DOI":"10.1021\/es402534m","article-title":"A natural driven membrane process for brackish and wastewater treatment: Photovoltaic powered ED and FO hybrid system","volume":"47","author":"Zhang","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Aboosi, F.Y., and Al-Aboosi, A.F. (2021). Preliminary Evaluation of a Rooftop Grid-Connected Photovoltaic System Installation under the Climatic Conditions of Texas (USA). Energies, 14.","DOI":"10.3390\/en14030586"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s40095-019-00326-z","article-title":"Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study","volume":"11","year":"2020","journal-title":"Int. J. Energy Environ. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.rser.2013.08.055","article-title":"Solar radiation prediction using Artificial Neural Network techniques: A review","volume":"33","author":"Yadav","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sharma, N., Sharma, P., Irwin, D., and Shenoy, P. (2011, January 17\u201320). Predicting solar generation from weather forecasts using machine learning. Proceedings of the 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium.","DOI":"10.1109\/SmartGridComm.2011.6102379"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120109","DOI":"10.1016\/j.energy.2021.120109","article-title":"Prediction of solar energy guided by pearson correlation using machine learning","volume":"224","author":"Jebli","year":"2021","journal-title":"Energy"},{"key":"ref_9","unstructured":"Torabi, M., Mosavi, A., Ozturk, P., Varkonyi-Koczy, A., and Istvan, V. (2019). Recent Advances in Technology Research and Education: Proceedings of the 17th International Conference on Global Research and Education Inter-Academia\u20132018, Kaunas, Lithuania, 24\u201327 September 2018, Springer."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hassan, M.Z., Ali, M.E.K., Ali, A.S., and Kumar, J. (2017, January 11\u201313). Forecasting day-ahead solar radiation using machine learning approach. Proceedings of the 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), Mana Island, Fiji.","DOI":"10.1109\/APWConCSE.2017.00050"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Plaza, E., Garc\u00eda, D., and Prieto, J.-I. (2024). Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain. Sustainability, 16.","DOI":"10.3390\/su16031293"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, L., Li, Y., Wang, X., Liu, L., Ma, M., and Yang, J. (2024). A Machine Learning Approach to Estimating Solar Radiation Shading Rates in Mountainous Areas. Sustainability, 16.","DOI":"10.3390\/su16020931"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ins.2014.02.159","article-title":"A novel hybrid approach for wind speed prediction","volume":"273","author":"Wang","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.4236\/jpee.2014.24023","article-title":"A literature review of wind forecasting methods","volume":"2","author":"Chang","year":"2014","journal-title":"J. Power Energy Eng."},{"key":"ref_15","first-page":"1","article-title":"Moat Mentality: Onshore and Offshore Approaches to Wind Waking","volume":"1","author":"DuVivier","year":"2020","journal-title":"Notre Dame J. Emerg. Tech."},{"key":"ref_16","unstructured":"Hemami, A. (2012). Wind Turbine Technology, Cengage Learning."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Delgado, I., and Fahim, M. (2020). Wind turbine data analysis and LSTM-based prediction in SCADA system. Energies, 14.","DOI":"10.3390\/en14010125"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TSTE.2017.2715061","article-title":"Performance assessment of wind turbines: Data-derived quantitative metrics","volume":"9","author":"He","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.apenergy.2017.10.044","article-title":"Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors","volume":"208","author":"Becker","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.egypro.2011.10.102","article-title":"Review of evaluation criteria and main methods of wind power forecasting","volume":"12","author":"Zhao","year":"2011","journal-title":"Energy Procedia"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, Y.-K., and Hong, J.-S. (2007, January 1\u20135). A literature review of wind forecasting technology in the world. Proceedings of the 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland.","DOI":"10.1109\/PCT.2007.4538368"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.rser.2008.02.002","article-title":"A review on the forecasting of wind speed and generated power","volume":"13","author":"Lei","year":"2009","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Firat, U., Engin, S.N., Saraclar, M., and Ertuzun, A.B. (2010, January 12\u201314). Wind speed forecasting based on second order blind identification and autoregressive model. Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications, Washington, DC, USA.","DOI":"10.1109\/ICMLA.2010.106"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1016\/j.knosys.2011.04.019","article-title":"A case study on a hybrid wind speed forecasting method using BP neural network","volume":"24","author":"Guo","year":"2011","journal-title":"Knowl. Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Faria, D.L., Castro, R., Philippart, C., and Gusmao, A. (2009, January 18\u201320). Wavelets pre-filtering in wind speed prediction. Proceedings of the 2009 International Conference on Power Engineering, Energy and Electrical Drives, Lisbon, Portugal.","DOI":"10.1109\/POWERENG.2009.4915221"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Che, G., Zhou, D., Wang, R., Zhou, L., Zhang, H., and Yu, S. (2024). Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models. Sustainability, 16.","DOI":"10.3390\/su16020898"},{"key":"ref_27","unstructured":"Rueda-Bayona, J.G., Cabello Eras, J.J., and Sagastume, A. (2024, January 02). Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Mode. Available online: https:\/\/www.iieta.org\/journals\/mmep\/paper\/10.18280\/mmep.080313."},{"key":"ref_28","unstructured":"Laboratory, S.E. (2024, January 02). National Solar Radiation Data Base Sites for Texas. Available online: http:\/\/www.me.utexa."},{"key":"ref_29","unstructured":"(2023, December 03). Midland Map. Available online: https:\/\/www.google.com\/maps."},{"key":"ref_30","unstructured":"EIA\u2014U.S. Energy Information Administration (2024, February 12). Use of Energy in Homes.Energy Explained, Available online: https:\/\/www.eia.gov\/energyexplained\/use-of-energy\/homes.php."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lee, M.H.L., Ser, Y.C., Selvachandran, G., Thong, P.H., Cuong, L., Son, L.H., Tuan, N.T., and Gerogiannis, V.C. (2022). A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models. Mathematics, 10.","DOI":"10.3390\/math10081329"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/3\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:04:17Z","timestamp":1760105057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/3\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,24]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["bdcc8030023"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8030023","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,24]]}}}