Two-Stage Bootstrap Sampling for Probabilistic Load Forecasting

Zhang, Jiawei; Wang, Yi; Sun, Mingyang; Zhang, Ning

doi:10.1109/tem.2020.2967352

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…Compared with the single point prediction method, the interval prediction method can obtain the change range of the predicted object in a period of time in the future by means of interval expression, and quantify the reliability of the prediction model according to the interval width and coverage probability, so as to make the prediction results of the model with high reliability [54]. At present, the commonly used interval prediction methods mainly include mean variance estimation method, upper and lower limit estimation method, delta method, Bayesian method and Bootstrap method [55][56][57][58][59][60][61][62]. The Bootstrap method can approximate the sample distribution characteristics without subjective assumption, but only through repeated sampling, and then can effectively construct the prediction interval, which has the advantages of high accuracy and reliability [61].…”

Section: Literature Reviewmentioning

confidence: 99%

“…At present, the commonly used interval prediction methods mainly include mean variance estimation method, upper and lower limit estimation method, delta method, Bayesian method and Bootstrap method [55][56][57][58][59][60][61][62]. The Bootstrap method can approximate the sample distribution characteristics without subjective assumption, but only through repeated sampling, and then can effectively construct the prediction interval, which has the advantages of high accuracy and reliability [61]. Therefore, in this paper, the Bootstrap method is used to establish the electricity-heat-cooling-gas load uncertain interval prediction model.…”

Section: Literature Reviewmentioning

confidence: 99%

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Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Zhao

Guo

2021

Mathematics

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The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Zhao

Guo

2021

Mathematics

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“…Traditionally, parametric approach with distribution assumption [12], forecasting residual resampling [13], and quantile regression (QR) [14] are three popular approaches to constructing PI. In the first category, methods prescribe a certain probability distribution, the parameters of which are estimated by data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [13] proposed a two-stage bootstrap sampling method for PSTLF. The first stage used bootstrap to characterize uncertainties from forecast models, and the second stage applied bootstrap to resample from the regression error.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Zhang¹,

Wen²,

Wu³

et al. 2022

Preprint

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Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.

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A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction

Wang,

Chudhery,

et al. 2023

Environ Sci Pollut Res

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Two-Stage Bootstrap Sampling for Probabilistic Load Forecasting

Cited by 15 publications

References 29 publications

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction

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