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Shuo Wang - One of the best experts on this subject based on the ideXlab platform.

  • electricity consumption probability density Forecasting Method based on lasso quantile regression neural network
    Applied Energy, 2019
    Co-Authors: Yang Qin, Shuo Wang, Xu Wang, Chao Wang
    Abstract:

    Abstract The electricity consumption Forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption Forecasting. A Method of probability density Forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption Forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption Forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed Method provides better performance for electricity consumption Forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF), quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption Forecasting, but also provide more precise results.

  • short term power load probability density Forecasting Method using kernel based support vector quantile regression and copula theory
    Applied Energy, 2017
    Co-Authors: Rui Liu, Shuo Wang
    Abstract:

    Abstract Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density Forecasting Methods can effectively quantify the uncertainty of power load Forecasting. The paper proposes a short-term power load probability density Forecasting Method using kernel-based support vector quantile regression (KSVQR) and Copula theory. As the kernel function can influence the prediction performance, three kernel functions are compared in this work to select the best one for the learning target. The paper evaluates the accuracy of the prediction intervals considering two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Considering uncertainty factors and the correlation of explanatory variables for power load prediction accuracy are of great importance. A probability density Forecasting Method based on Copula theory is proposed in order to achieve the relational diagram of electrical load and real-time price. The electrical load forecast accuracy of the proposed Method is assessed by means of real datasets from Singapore. The simulation results show that the proposed Method has great potential for power load Forecasting by selecting appropriate kernel function for KSVQR model.

Yuanzhang Sun - One of the best experts on this subject based on the ideXlab platform.

  • statistical scenarios Forecasting Method for wind power ramp events using modified neural networks
    Journal of Modern Power Systems and Clean Energy, 2015
    Co-Authors: Mingjian Cui, K E Deping, Di Gan, Yuanzhang Sun
    Abstract:

    Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years. Several Forecasting techniques for wind power ramp events have been reported. In this paper, the statistical scenarios Forecasting Method is proposed for wind power ramp event probabilistic Forecasting based on the probability generating model. Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics, respectively. Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions. A number of statistical scenarios captured bands are generated accordingly. Eventually, ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event Forecasting Method. A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis. It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions. Moreover, the statistical scenarios Forecasting Method can accurately estimate the characteristics of wind power ramp events. The results verify that the proposed Method can guide the generation Method of statistical scenarios and Forecasting models for ramp events.

  • wind power ramp event Forecasting using a stochastic scenario generation Method
    IEEE Transactions on Sustainable Energy, 2015
    Co-Authors: Mingjian Cui, Di Gan, Yuanzhang Sun, Jie Zhang, Brimathias Hodge
    Abstract:

    Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE Forecasting Method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp Forecasting Method. Results showed that the proposed Method effectively forecasted the probability of ramp events.

Mingjian Cui - One of the best experts on this subject based on the ideXlab platform.

  • statistical scenarios Forecasting Method for wind power ramp events using modified neural networks
    Journal of Modern Power Systems and Clean Energy, 2015
    Co-Authors: Mingjian Cui, K E Deping, Di Gan, Yuanzhang Sun
    Abstract:

    Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years. Several Forecasting techniques for wind power ramp events have been reported. In this paper, the statistical scenarios Forecasting Method is proposed for wind power ramp event probabilistic Forecasting based on the probability generating model. Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics, respectively. Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions. A number of statistical scenarios captured bands are generated accordingly. Eventually, ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event Forecasting Method. A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis. It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions. Moreover, the statistical scenarios Forecasting Method can accurately estimate the characteristics of wind power ramp events. The results verify that the proposed Method can guide the generation Method of statistical scenarios and Forecasting models for ramp events.

  • wind power ramp event Forecasting using a stochastic scenario generation Method
    IEEE Transactions on Sustainable Energy, 2015
    Co-Authors: Mingjian Cui, Di Gan, Yuanzhang Sun, Jie Zhang, Brimathias Hodge
    Abstract:

    Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE Forecasting Method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp Forecasting Method. Results showed that the proposed Method effectively forecasted the probability of ramp events.

Rui Liu - One of the best experts on this subject based on the ideXlab platform.

  • short term power load probability density Forecasting Method using kernel based support vector quantile regression and copula theory
    Applied Energy, 2017
    Co-Authors: Rui Liu, Shuo Wang
    Abstract:

    Abstract Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density Forecasting Methods can effectively quantify the uncertainty of power load Forecasting. The paper proposes a short-term power load probability density Forecasting Method using kernel-based support vector quantile regression (KSVQR) and Copula theory. As the kernel function can influence the prediction performance, three kernel functions are compared in this work to select the best one for the learning target. The paper evaluates the accuracy of the prediction intervals considering two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Considering uncertainty factors and the correlation of explanatory variables for power load prediction accuracy are of great importance. A probability density Forecasting Method based on Copula theory is proposed in order to achieve the relational diagram of electrical load and real-time price. The electrical load forecast accuracy of the proposed Method is assessed by means of real datasets from Singapore. The simulation results show that the proposed Method has great potential for power load Forecasting by selecting appropriate kernel function for KSVQR model.

Mu Yen Chen - One of the best experts on this subject based on the ideXlab platform.

  • Picture fuzzy time series:Defining, modeling and creating a new Forecasting Method
    Engineering Applications of Artificial Intelligence, 2020
    Co-Authors: Erol Egrioglu, Eren Bas, Ufuk Yolcu, Mu Yen Chen
    Abstract:

    Abstract The extant literature has shown that fuzzy sets can be applied to solve Forecasting problems. A fuzzy time series is a kind of time series whose observations are fuzzy sets or fuzzy numbers. A picture fuzzy set is a generalized form of fuzzy and intuitionistic fuzzy sets that is also referred to as a standard neutrosophic set. In this study, a picture fuzzy time series and a single variable high order picture fuzzy time series Forecasting model are defined based on picture fuzzy sets. We also propose a new picture fuzzy time series Forecasting Method. The proposed Method solves the issues inherent in the high order single variable picture fuzzy time series Forecasting model. The proposed Method has three basic steps: (1) picture fuzzification, (2) model construction, and (3) Forecasting. In the proposed Method, picture fuzzification is accomplished via picture fuzzy clustering, and positive, neutral and negative membership values are obtained. The model construction step consists of estimating a function. This study employed a pi-sigma artificial neural network for this estimation. The proposed Method is applied to a meteorological data set with an expanding window approach. The proposed Method outperforms recent fuzzy time series and classical Methods found in the extant literature.