Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

2017 International Conference on Environmental and Energy Engineering

生态环境科学;能源学

Mohammed, E.^1 ; Wang, S.^1 ; Yu, J.^1

School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin

150001, China^1

关键词: Auto-regressive integrated moving average;    Autoregressive moving average;    Correlation coefficient;    Electricity demands;    Forecasting error;    Historical records;    Multiple linear regressions;    Short-Term wind power predictions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/63/1/012005/pdf
DOI  :  10.1088/1755-1315/63/1/012005

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.