Wind power forecasting errors modelling approach considering temporal and spatial dependence

Hu, Wei; Min, Yong; Zhou, Yifan; Lu, Qiuyu

doi:10.1007/s40565-016-0263-y

Cited by 44 publications

(23 citation statements)

References 34 publications

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“…In this context, pair copula theory is proposed to decompose a multivariate copula into multiple conditional bivariate copulas [37]. The concept is introduced as follows.…”

Section: Pair Copula Theorymentioning

confidence: 99%

Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Cai

Cao

et al. 2019

Applied Sciences

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This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is used to model the marginal distribution and the correlation structure, respectively. Then a cross entropy based importance sampling (CE-IS) is improved to enhance the efficiency of the power system reliability assessment, which is a crucial part of the CC evaluation. After that, the proposed methods are adopted to combine with the secant method to develop a complete algorithm to calculate the CC of wind energy. Numerical tests are designed and carried out based on the IEEE-RTS 79 system and wind speed data obtained from four wind farms in Northwest China. In order to show the superiority of SNMM and D-vine copula, the goodness-of-fit is quantified by different statistics. Besides, the improved CE-IS method is validated by comparison with Monte Carlo sampling (MCS) and traditional CE-IS in the efficiency of reliability assessment. Finally, the proved methods are combined with the secant method to calculate the CC of four wind farms, which can provide information for wind farm planning.

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“…In this context, pair copula theory is proposed to decompose a multivariate copula into multiple conditional bivariate copulas [37]. The concept is introduced as follows.…”

Section: Pair Copula Theorymentioning

confidence: 99%

Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Cai

Cao

et al. 2019

Applied Sciences

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“…Copulas have also been used in the past to capture the relation between different stochastic attributes in the context of electrical energy system operation [14] and planning [15]. Authors in [16] and [17] use vine copulas to capture the dependency between the outputs of different wind farms. The same principle is applied in [18] to electric vehicle usage data to generate loading scenarios.…”

Section: Introductionmentioning

confidence: 99%

Using Vine Copulas to Generate Representative System States for Machine Learning

Konstantelos

Sun

Tindemans

et al. 2019

IEEE Trans. Power Syst.

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Abstract--The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the non-linear dependence structure they exhibit. In this paper we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modelling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches.

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“…ARIMAX and ANN forecasting concepts have been applied widely in different energy applications such as buildings, industrial loads and renewable energy [13,14]. It should also be beneficial to apply these techniques to forecasting the RTG crane demand in order to improve the understanding of load behaviour which can help to reduce peak demand and gas emissions.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead industrial load forecasting for electric RTG cranes

Alasali

Haben

Becerra

et al. 2018

J. Mod. Power Syst. Clean Energy

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Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.

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Wind power forecasting errors modelling approach considering temporal and spatial dependence

Cited by 44 publications

References 34 publications

Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Using Vine Copulas to Generate Representative System States for Machine Learning

Day-ahead industrial load forecasting for electric RTG cranes

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