A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

Abstract

Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

“…It can be seen from Figure 4 that there is a strong mapping relationship between wind speed and power. In [18], the historical wind speed and the historical wind farm power are used as inputs of probabilistic prediction model. However, when the multi-step prediction is performed, the cumulative error will occur because the model input contains the wind farm power (which cannot be obtained from the weather forecast).…”

“…According to the modeling methods, they can also be divided into parametric and nonparametric methods. Wan et al [11] used the bootstrap method to resample the wind power, and assumed that the outputs obeyed the normal distribution to get the final prediction intervals; in [12], the multi-distribution ensemble method was used for probabilistic wind power forecasting, three probabilistic forecasting models based on Gaussian, gamma, and Laplace predictive distributions were adopted to form the ensemble model; [13][14][15][16] used direct quantile regression, joint quantile regression, quantile regression based on gradient boosting decision trees and support vector quantile regression respectively to establish the quantile regression models of wind power, and obtained the nonparametric probabilistic prediction results; in [17], decomposition-based quantile regression forest was applied to day-ahead short-term load probability density forecasting; in [18], the Naive Bayesian Classifier was established to obtain the classification of wind power, and the particle swarm optimization (PSO) algorithm was used to optimize the weighting coefficients corresponding to the prediction intervals; in [19], the lower and upper bounds of the prediction intervals were directly treated as the outputs of extreme learning machine (ELM), and the output weights of ELM were obtained through PSO. Since the nonparametric methods can avoid unreasonable distribution assumptions in the parametric methods, the results obtained are more reasonable, but the calculation is often more complicated.…”

“…For the comprehensive evaluation indices, Shrivastava et al [22] proposed the coverage width criterion (CWC), which combines PICP and PINAW in sections, but practical applications showed that this indicator could not scientifically evaluate the global performance of probabilistic prediction methods [19]. In [18], PICP and PINAW were simply weighted and summed, and the structure was simple, but it failed to achieve the adjustment of assessment keypoints under different conditions. Therefore, it is necessary to find a more reasonable and effective comprehensive performance evaluation index.…”

Bayesian Network Modelling for the Wind Energy Industry: An Overview

“…In Otero-Casal et al (2019), the authors used a hybrid filter (Kalman-Bayesian) for improved wind forecasting, useful for reliable wind production forecasts. Other papers on this subject area include: Ciobanu et al (2017); Yang et al (2017); Afshari-Igder et al (2018); and Wang et al (2019a).…”

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

“…On the other hand, interval prediction can provide the prediction intervals at a certain confidence level, and this can quantify the uncertainty of the prediction value [29][30][31]. Traditional methods for the construction of prediction intervals include Bayesian [32][33][34] and bootstrap [35][36][37]. However, these prediction interval methods are constructed based on the point prediction error and require massive computational requirements.…”