{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:39:39Z","timestamp":1777682379544,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of High Speed Networks"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>As efforts to reduce the environmental impact of energy production expand, renewable energy sources are becoming increasingly significant in the global energy balance. Over the anticipated 20-year life of a wind turbine, operation and maintenance (O&amp;M) expenditures are predicted to account for 65%\u201390% of the overall investment cost, including inflation and crane charges. The higher estimate is based on 600\u20137500\u2005kW machines in North America, while the lower estimate derives from the Danish fleet of 600\u2005kW turbines. Reliability studies indicate that O&amp;M costs contribute roughly 20%\u201325% of the levelized cost per kWh. These expenses strongly influence the profitability of wind farms and the competitiveness of wind turbines compared to other renewable energy options, highlighting significant potential for technological improvement. The profitability of a wind farm and the competitiveness of wind turbines compared to other green energy options are strongly influenced by O&amp;M costs. Accurate wind speed and power generation prediction is therefore essential to improve efficiency and reduce investment costs. To address this, machine learning algorithms such as long-short term memory (LSTM), gated recurrent unit (GRU), artificial neural network (ANN), XGBoost, random forest (RF), and support vector machine (SVM) have been applied for forecasting wind speed and predicting power generation. Specifically, LSTM, GRU, and ANN are employed for wind speed forecasting, while XGBoost, RF, and SVM are used for electricity generation prediction. Results show that LSTM and GRU achieve lower root mean squared error than ANN in wind speed forecasting, while RF provides higher accuracy for power generation prediction compared to XGBoost and SVM. Overall, LSTM, GRU, and RF demonstrate strong performance in wind forecasting and power generation prediction.<\/jats:p>","DOI":"10.1177\/09266801251412546","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:39:33Z","timestamp":1772181573000},"page":"87-106","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting of wind speed and power generation prediction using machine learning algorithms"],"prefix":"10.1177","volume":"32","author":[{"given":"M","family":"Rajkamal","sequence":"first","affiliation":[{"name":"IBM India Pvt Ltd, Bangalore, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8039-0920","authenticated-orcid":false,"given":"M","family":"Sumathi","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India"}]},{"given":"SP","family":"Raja","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}]},{"given":"Shaman","family":"Moosan","sequence":"additional","affiliation":[{"name":"IBM India Pvt Ltd, Bangalore, India"}]},{"given":"M","family":"Logesh","sequence":"additional","affiliation":[{"name":"SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India"}]},{"given":"Ark","family":"Mishara","sequence":"additional","affiliation":[{"name":"School of Information Technology, Birla Institute of Technology and Science, Pilani, Rajasthan, India"}]}],"member":"179","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2023.01.015"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09923-4"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-05250-3"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2022.12.121"},{"key":"e_1_3_2_6_2","first-page":"1","article-title":"Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm","volume":"9","author":"Faruque MO","year":"2024","unstructured":"Faruque MO, Hossain MA, Islam MR, et al. Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm. Cleaner Energ Syst 2024; 9: 1\u201315.","journal-title":"Cleaner Energ Syst"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e34807"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/su17073239"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2023.109159"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11191-0"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11230-5"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.109103"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-024-01388-2"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.5194\/wes-10-1137-2025"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126283"},{"key":"e_1_3_2_16_2","first-page":"1","article-title":"Comparative study of time-series forecasting models for wind power generation in Gujarat, India","volume":"8","author":"Mahata S","year":"2024","unstructured":"Mahata S, Harsh P, Shekher V. Comparative study of time-series forecasting models for wind power generation in Gujarat, India. e-Prime \u2013 Adv Electron Eng Electr Energy 2024; 8: 1\u201315.","journal-title":"e-Prime \u2013 Adv Electron Eng Electr Energy"},{"key":"e_1_3_2_17_2","first-page":"1","article-title":"Hybrid intelligent optimization for onshore wind farm forecasting","volume":"10","author":"Gwabavv M","year":"2025","unstructured":"Gwabavv M, Bansal RC, Bryce A. Hybrid intelligent optimization for onshore wind farm forecasting. Smart Grids Sustain Energy 2025; 10: 1\u201332.","journal-title":"Smart Grids Sustain Energy"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1016\/j.egyr.2021.12.062","article-title":"Wind speed prediction using measurements from neighbouring locations and combining the extreme learning machine and the adaboost algorithm","volume":"8","author":"Wang L","year":"2022","unstructured":"Wang L, Guo Y, Fan M, et al. Wind speed prediction using measurements from neighbouring locations and combining the extreme learning machine and the adaboost algorithm. Energy Rep 2022; 8: 1508\u20131518.","journal-title":"Energy Rep"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/en18051155"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3147602"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TLA.2021.9461848"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2021.3078774"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2021.3069111"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2020.2990937"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2020.3015169"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2020.2986984"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2020.3025609"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2019.2891962"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2019.2921940"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2019.2923575"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2019.2896152"}],"container-title":["Journal of High Speed Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09266801251412546","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09266801251412546","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09266801251412546","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:42:42Z","timestamp":1777452162000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09266801251412546"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1177\/09266801251412546"],"URL":"https:\/\/doi.org\/10.1177\/09266801251412546","relation":{},"ISSN":["0926-6801","1875-8940"],"issn-type":[{"value":"0926-6801","type":"print"},{"value":"1875-8940","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]}}}