Development of a Novel Weighted Maximum Likelihood-Based Parameter Estimation Technique for Improved Annual Energy Production Estimation of Wind Turbines

Woobeom Han, Kanghee Lee, Jonghwa Kim and Seungjae Lee ()
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Woobeom Han: Marine Engineering Support Center, Korea Marine Equipment Research Institute, Busan 46754, Republic of Korea
Kanghee Lee: Marine Engineering Support Center, Korea Marine Equipment Research Institute, Busan 46754, Republic of Korea
Jonghwa Kim: Center for Green Energy Industry Intelligence, Institute for Advanced Engineering, Yongin-si 17035, Republic of Korea
Seungjae Lee: Division of Naval Architecture and Ocean Systems Engineering, Korean Maritime and Ocean University, Busan 46252, Republic of Korea

Energies, 2025, vol. 18, issue 19, 1-24

Abstract: Conventional statistical models consider all wind speed ranges as equally important, causing significant prediction errors, particularly in wind speed intervals that contribute the most to wind turbine power generation. To overcome this limitation, this study proposes a novel parameter estimation method—Weighted Maximum Likelihood Estimation (WMLE)—to improve the accuracy of annual energy production (AEP) predictions for wind turbine systems. The proposed WMLE incorporates wind-speed-specific weights based on power generation contribution, along with a weighting amplification factor ( β ), to construct a power-oriented wind distribution model. WMLE performance was validated by comparing four offshore wind farm candidate sites in Korea—each exhibiting distinct wind characteristics. Goodness-of-fit evaluations against conventional wind statistical models demonstrated the improved distribution fitting performance of WMLE. Furthermore, WMLE consistently achieved relative AEP errors within ±2% compared to those of time-series-based methods. A sensitivity analysis identified the optimal β value, which narrowed the distribution fit around high-energy-contributing wind speeds, thereby enhancing the reliability of AEP predictions. In conclusion, WMLE provides a practical and robust statistical framework that bridges the gap between statistical distribution fitting and time-series-based methods for AEP. Moreover, the improved accuracy of AEP predictions enhances the reliability of wind farm feasibility assessments, reduces investment risk, and strengthens financial bankability.

Keywords: weighted maximum likelihood estimation; annual energy production; Weibull distribution; parameter estimation; weight exponent (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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