Wind Power Interval Forecasting Based on Confidence Interval Optimization

Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.

[1]  Zhe Chen.  Wind power in modern power systems , 2013 .

[2]  W.J. Lee,et al.  An integration of ANN wind power estimation into UC considering the forecasting uncertainty , 2005, IEEE Systems Technical Conference on Industrial and Commercial Power 2005..

[3]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[4]  Gaurav Khandelwal,et al.  Battery-Free Electronic Smart Toys: A Step toward the Commercialization of Sustainable Triboelectric Nanogenerators , 2018 .

[5]  Wei-Jen Lee,et al.  An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty , 2007, IEEE Transactions on Industry Applications.

[6]  Lu Zongxiang.  A Comprehensive Error Evaluation Method for Short-term Wind Power Prediction , 2011 .

[7]  Jung Hwan Park,et al.  Sustainable Biomechanical Energy Scavenger toward Self-Reliant Kids’ Interactive Battery-Free Smart Puzzle , 2017 .

[8]  Young Sun Mok,et al.  A smart mobile pouch as a biomechanical energy harvester towards self-powered smart wireless power transfer applications. , 2017, Nanoscale.

[9]  Yanbin Yuan,et al.  Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine , 2017 .

[10]  Kristin Dietrich,et al.  Stochastic Unit Commitment Considering Uncertain Wind Production: Application to an isolated system , 2009 .

[11]  Xue Yushen.  A Review on Impacts of Wind Power Uncertainties on Power Systems , 2014 .

[12]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[13]  Sven Leyffer,et al.  On the Global Convergence of a Filter--SQP Algorithm , 2002, SIAM J. Optim..

[14]  Yusheng Xue,et al.  A review on short-term and ultra-short-term wind power prediction , 2015 .

[15]  Jianzhou Wang,et al.  Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models , 2016 .

[16]  Sven Leyffer,et al.  Nonlinear programming without a penalty function , 2002, Math. Program..

[17]  I. Erlich,et al.  A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System , 2009, IEEE Transactions on Power Systems.

[18]  Venkata Dinavahi,et al.  Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks , 2018, IEEE Transactions on Sustainable Energy.

[19]  Guoyong Zhang,et al.  A simple model for probabilistic interval forecasts of wind power chaotic time series , 2014 .

[20]  Zhou Shuang-xi.  Short-term Wind Power Forecast Based on Non-parametric Regression Model , 2010 .

[21]  Su Jianhui.  Short-term Forecasting of Wind Power and Non-parametric Confidence Interval Estimation , 2011 .

[22]  Ya-Xiang Yuan,et al.  Optimization theory and methods , 2006 .