Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR

Abstract

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid dispatching and grid planning. To address the problem of the low accuracy of traditional wind power interval prediction, a new interval prediction method of wind power is proposed based on PSR-BLS-QR with adaptive rolling error correction. First, one-dimensional wind power data are mapped to high-dimensional space by phase space reconstruction (PSR) to achieve data reconstruction and the input and output of the broad learning system (BLS) model are constructed. Second, the training set and the test set are divided according to the input and output data. The BLS model is trained by the training set and the initial power interval of training data is constructed by quantile regression (QR). Then, the error distribution of nonparametric kernel density estimation is constructed at different power interval segments of the interval upper and lower boundaries, respectively, and the corresponding error-corrected power is found. Next, the optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries. Finally, a test set is used for testing the performance of the proposed method. Three wind power datasets from different regions are used to prove that the proposed method can improve the average prediction accuracy by about 6–14% with the narrower interval width compared with the traditional interval prediction methods.

Bayesian Optimization - LSTM Modeling and Time Frequency Correlation Mapping Based Probabilistic Forecasting of Ultra-short-term Photovoltaic Power Outputs

“…Aiming at the uncertainty of power and kernel density estimation, a power interval forecasting method based on hybrid semi-cloud model and non-parametric kernel density estimation is proposed [14]. To address the problem of low traditional power interval forecasting accuracy, a new interval method is proposed based on PSR-BLS-QR with adaptive rolling error correction [15]. The optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries.…”

“…Suppose the daily PV power data series in the forecasting day is r0,0, and the daily PV power data series in the j th day before (negative) or after (positive) forecast moment of the previous i th year is ri,j. The correlation coefficient between r0,0 and ri,j, can be calculated according to the following formula [15].…”

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

“…By processing and analyzing historical time series data, complex system models can be established [1]. These models can predict the development trends of systems and effectively solve problems where the operating mechanisms of complex systems are difficult to analyze [2][3][4]. Based on the nature of time series data, the requirements of the system problem, and the suitability of algorithms, common processing algorithms for time series data include univariate processing algorithms, multivariate processing algorithms [5], flat processing algorithms, linear processing algorithms, nonlinear processing algorithms, and algorithms for handling different time scales [6].…”