Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method. (1st December 2019)

• Record Type: Journal Article
• Authors: Zhao, Jing; Wang, Jianzhou; Guo, Zhenhai; Guo, Yanling; Lin, Wantao; Lin, Yihua
• Is Part Of: Applied energy. Volume 255(2019)
• Publication Date: 2019-12-01
• Subjects: Wind speed forecast -- Numerical simulation -- Stochastic process -- Induced order weighted average -- Ensemble method -- Evolutionary algorithm; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113833
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Improved forecasting by numerical simulation, stochastic process and optimization. Data utilization and modeling from both local observation and ensemble simulation. Improved ordered weighted average induced by system state and fuzzy membership. Discussion on how the ensemble number influences model performance. Abstract: At present, a single-valued deterministic simulation method is preferred choice for numerical wind speed forecasts. However, it remains difficult to meet the actual needs of both wind farms and grid systems, mainly owing to unavoidable uncertainties. The development of skilled numerical forecasting methods has become a critical issue and major challenge, and new capabilities and strategies for mitigating uncertainties in wind data derived from numerical models are highly sought after. On this topic, our study develops an improved ensemble method for day-ahead forecast of local wind speeds. The proposed method constructs an optimized system based on ensemble simulations of weather research and forecasting model, a Markov stochastic process, and an improved induced ordered weighted average approach that combines gray relationships with an evolutionary algorithm. The original contributions are concluded as: (i) using a Markov stochastic process, the observed information can be transferred to the ensemble system, which contributes to the accuracy improvement; and (ii) the optimized induced ordered weighted average model, with a member selection process, is a new data-driven ensemble method for numerical wind speed forecasting. Simulation indicates that the proposed method effectively reduces the uncertainties of numerical simulations, and performs better than other models. The simulation also shows that an ensemble with fewer members may generate better results than using a combination of all single members. This study is of great significance for both theoretical research and real applications for numerical wind speed forecasts at local sites.