Outlier data mining method considering the output distribution characteristics for photovoltaic arrays and its application

Li, Gengda; Duan, Zhenqing; Liang, Ling; Zhu, Honglu; Hu, Aoyu; Cui, Qingru; Chen, Baowei; Hu, Wensen

doi:10.1016/j.egyr.2020.08.034

Cited by 17 publications

(5 citation statements)

References 23 publications

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“…In the traditional recommendation system, the recommendation algorithm is the core part of the project recommendation system and the focus of research. The accuracy of the recommendation result of a project recommendation system is directly related to the selection of recommendation algorithm [ 25 , 26 ]. The commonly used recommendation algorithms in related research fields can be roughly divided into four types, i.e., the content-based recommendation algorithm, memory collaborative filtering recommendation algorithm, model collaborative filtering recommendation algorithm, and hybrid recommendation algorithm.…”

Section: Educational Theories and Personalized Recommendation Systemmentioning

confidence: 99%

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Yang

2022

PLoS ONE

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To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.

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Section: Educational Theories and Personalized Recommendation Systemmentioning

confidence: 99%

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Yang

2022

PLoS ONE

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“…, 424. PV time-series data are often corrupted with spiky signal noise, sensor failure, communication equipment failure, maximum power tracking abnormalities, array shutdown, and power limitation, to name a few (see, Li et al (2020)), while WG time-series data are frequently corrupted by communication errors, unit outages, and curtailment (see, Ye et al (2016)). These abnormal conditions are represented by manually added outliers in the measurements, for instance, as shown in Figure 11 for the RES real output powers.…”

Section: Case Study 3: Stochastic Power Flowmentioning

confidence: 99%

Robust Hyperparameter Estimation in Gaussian Process Regression Model

Algikar

Mili

2023

Preprint

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A stochastic process mathematically models the random behavior of systems and naturally occurring phenomena. A popular process is a Gaussian process which assumes a multivariate Gaussian distribution for the response variables. The predictive distributions of target variables can be explicitly obtained by using conditioning and inference techniques. The mean function and covariance function of the predictive distributions are parameterized whose estimates can be obtained by maximizing the marginal likelihood of the training responses or their posterior distribution. Unfortunately, these estimates are not robust to outliers that are often present in the observational training data sets. To overcome this problem, we develop a robust data-driven process based on the Schweppe-type Huber-generalized maximum likelihood (SHGM) estimator to bound the influence of all types of outliers on the estimates while exhibiting a high statistical efficiency at the Gaussian and thick-tail distributions. The weights utilized by the SHGM estimator are calculated using projection statistics, which are robust distances of the data points determined by the row vectors of the design matrix. These weights make the estimator robust to both vertical outliers and bad leverage points while not downweighting good leverage points. We show that the proposed robust model is able to handle up to 25 percent of outliers among the data set. The proposed method is illustrated by two real-world examples in which the performance of the proposed method is assessed against the standard Gaussian process regression.

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“…The assumed probability distributions typically are the Gaussian distribution for the load, the Weibull distribution for the wind speed, and the Beta distribution for the solar irradiance, among others. However, in practice, these distributions may not represent the actual data [20], [21], yielding inaccurate uncertainty quantification results. The conventional meta-modeling methods are not designed to handle the misrepresentations of power curve distribution, yielding biased results.…”

Section: Introductionmentioning

confidence: 99%

“…However, all these methods are relying on raw data without considering outliers. It is well known that wind generation (WG) time series data are frequently contaminated with communication errors, wind turbine outages, and curtailments [20] while PV time series data are contaminated with large signal noise, sensor failures, communication equipment failures, maximum power tracking abnormalities, array shutdowns, and power limitations, to name a few [21].…”

Section: Introductionmentioning

confidence: 99%

A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

Algikar¹,

Xu²,

Yarahmadi³

et al. 2023

Preprint

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In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.

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Outlier data mining method considering the output distribution characteristics for photovoltaic arrays and its application

Cited by 17 publications

References 23 publications

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Robust Hyperparameter Estimation in Gaussian Process Regression Model

A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

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