Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

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Power Consumption Interval Prediction Based on Quantile Regression Neural Network and Kernel Density Estimation

2018 11th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2018.10169 ◽

2018 ◽

Author(s):

Haohui Lv ◽

Guandi Chen ◽

Mingbin Deng ◽

Zhiyuan Tan ◽

Wen Hu

Keyword(s):

Neural Network ◽

Power Consumption ◽

Quantile Regression ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Interval Prediction

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Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

Sustainability ◽

10.3390/su11246954 ◽

2019 ◽

Vol 11 (24) ◽

pp. 6954

Author(s):

Fuqiang Li ◽

Shiying Zhang ◽

Wenxuan Li ◽

Wei Zhao ◽

Bingkang Li ◽

...

Keyword(s):

Probability Density ◽

Density Estimation ◽

Kernel Density Estimation ◽

Clustering Algorithm ◽

Estimation Method ◽

Kernel Density ◽

Support Vector ◽

Average Width ◽

Power Load ◽

Density Forecasting

In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

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