Zhang, Jie
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6880
Jie Zhang is an Assistant Professor of Mechanical Engineering. Dr. Zhang's research interests include:
- Multidisciplinary design optimization,
- Complex engineered systems,
- Power & energy systems,
- Renewable energy,
- Grid modernization,
- Big data analytics,
- Probabilistic design
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Browsing Zhang, Jie by Author "Hodge, B. -M"
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Item Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization(Institute of Electrical and Electronics Engineers Inc.) Sun, Mucun; Feng, Cong; Zhang, Jie; Chartan, E. K.; Hodge, B. -M; Sun, Mucun; Feng, Cong; Zhang, JieProbabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model. © 2018 IEEE.Item Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures(MDPI Ag, 2016-02-02) G©mez-L©zaro, E.; Bueso, M. C.; Kessler, M.; Mart©n-Mart©nez, S.; Zhang, Jie; Hodge, B. -M; Molina-Garc©a, A.; Zhang, JieThe Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.