{"id":7610,"date":"2026-03-10T18:37:51","date_gmt":"2026-03-10T18:37:51","guid":{"rendered":"https:\/\/www.etran.rs\/2026\/?p=7610"},"modified":"2026-04-02T18:48:49","modified_gmt":"2026-04-02T18:48:49","slug":"gokhan-yuksek-operational-implications-of-renewable-power-forecasting-in-modern-grids","status":"publish","type":"post","link":"https:\/\/www.etran.rs\/2026\/en\/gokhan-yuksek-operational-implications-of-renewable-power-forecasting-in-modern-grids\/","title":{"rendered":"G\u00f6khan Y\u00fcksek: Operational Implications of Renewable Power Forecasting in Modern Grids"},"content":{"rendered":"\n<p>The rapid rise in the share of renewable energy generation in power systems has made accurate short- and medium-term power forecasting a fundamental requirement for modern grids. Wind and photovoltaic (PV) power production exhibit strong variability driven by meteorological conditions and temporal patterns, creating significant challenges for generation scheduling, reserve allocation, grid stability, and energy market operations. Forecast uncertainty, therefore, directly affects operational costs and planning limitations, driving growing interest in data-driven forecasting approaches.<\/p>\n\n\n\n<p>This study offers a unified perspective on renewable power forecasting through two complementary case studies of wind and solar energy, using optimization-assisted regression modeling. The central view is that forecasting performance depends not only on the selected regression model but also on how effectively the model is configured for a specific site and data regime. Hyperparameter selection and model calibration strongly shape the bias\u2013variance trade-off, sensitivity to rare meteorological events, and prediction robustness under changing operating conditions. In this context, approaches that combine regression-based forecasting models with metaheuristic optimization techniques have been widely reported in the literature as effective for model parameter tuning and generalization. For the wind energy case study, power forecasting is formulated as a supervised regression problem using meteorological and temporal predictors, and optimization-assisted tuning consistently improves accuracy over default model configurations. For the solar PV case study, irradiance-driven power forecasting is investigated under the same methodological framework, revealing distinct drivers and error characteristics compared with wind power, while still delivering similar performance gains. The study also discusses common challenges in renewable power forecasting, including data quality limitations, diurnal and seasonal patterns, and the impact of meteorological feature selection on model stability and interpretability. Forecasting performance is assessed using standard regression metrics, and practical considerations for optimization-based tuning, such as parameter sensitivity and reproducibility, are briefly discussed. Overall, this work indicates that optimization-assisted model tuning is a practical and promising approach for improving forecasting accuracy and supporting more reliable integration of wind and solar generation into modern power grids. The presented perspective is supported by an implemented software environment that enables comparative evaluation of regression models and optimization-assisted tuning strategies.<\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile is-vertically-aligned-top has-very-light-gray-to-cyan-bluish-gray-gradient-background has-background\" style=\"grid-template-columns:30% auto\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"841\" src=\"https:\/\/www.etran.rs\/2026\/wp-content\/uploads\/2026\/03\/gokh.png\" alt=\"\" class=\"wp-image-7611 size-full\" srcset=\"https:\/\/www.etran.rs\/2026\/wp-content\/uploads\/2026\/03\/gokh.png 700w, https:\/\/www.etran.rs\/2026\/wp-content\/uploads\/2026\/03\/gokh-250x300.png 250w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p><img decoding=\"async\" src=\"\" alt=\"\" style=\"\">Dr. G\u00f6khan Y\u00fcksek is an Assistant Professor in the Department of Electrical and Electronics Engineering at Batman University, Turkey, and currently serves as the university-level Research and Development (R&amp;D) Coordinator. He received his B.Sc. degree in Electrical and Electronics Engineering from Ni\u011fde University in 2014, and his M.Sc. and Ph.D. degrees from Mersin University, Turkey, in 2017 and 2024, respectively. His research interests broadly cover renewable energy systems, data-driven modeling, optimization methods, and advanced control of energy-related systems. Within this scope, he has conducted studies on wind and photovoltaic power modeling, optimization-assisted machine learning, and nonlinear system identification and control. He has also developed and applied optimization-oriented modeling and control approaches to a variety of engineering problems. His research has resulted in multiple publications in internationally recognized Q1 and Q2 journals in energy systems, control engineering, and computational intelligence. In addition to these activities, Dr. Y\u00fcksek is actively involved in nationally funded research projects focused on battery systems, energy storage technologies, and the development of laboratory infrastructure. He has authored and co-authored numerous journal and conference papers, serves as a reviewer for several international journals, and actively participates in national and international research projects. His current work focuses on practical, reliable, and reproducible methodologies for modern power engineering and energy system applications.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The rapid rise in the share of renewable energy generation in power systems has made accurate short- and medium-term power forecasting a fundamental requirement for modern grids. Wind and photovoltaic (PV) power production exhibit strong variability driven by meteorological conditions and temporal patterns, creating significant challenges for generation scheduling, reserve allocation, grid stability, and energy &hellip; <\/p>\n","protected":false},"author":1,"featured_media":7613,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[113],"tags":[],"class_list":["post-7610","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-invited-lectures"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>G\u00f6khan Y\u00fcksek: Operational Implications of Renewable Power Forecasting in Modern Grids -<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.etran.rs\/2026\/en\/gokhan-yuksek-operational-implications-of-renewable-power-forecasting-in-modern-grids\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"G\u00f6khan Y\u00fcksek: Operational Implications of Renewable Power Forecasting in Modern Grids -\" \/>\n<meta property=\"og:description\" content=\"The rapid rise in the share of renewable energy generation in power systems has made accurate short- and medium-term power forecasting a fundamental requirement for modern grids. Wind and photovoltaic (PV) power production exhibit strong variability driven by meteorological conditions and temporal patterns, creating significant challenges for generation scheduling, reserve allocation, grid stability, and energy &hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.etran.rs\/2026\/en\/gokhan-yuksek-operational-implications-of-renewable-power-forecasting-in-modern-grids\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-10T18:37:51+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-04-02T18:48:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.etran.rs\/2026\/wp-content\/uploads\/2026\/03\/gokh-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"700\" \/>\n\t<meta property=\"og:image:height\" content=\"841\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Uzi Ramadani\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" 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