Modified pattern sequence-based forecasting for electric vehicle charging stations

Majidpour, Mostafa; Qiu, Charlie; Chu, Pao-Hsien; Gadh, Rajit; Pota, H. R.

doi:10.1109/smartgridcomm.2014.7007731

Cited by 32 publications

(19 citation statements)

References 11 publications

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“…It means that cluster size will be finalized with a number returned by more than one index. [49,60,61] suggested and used a single index, which leads to simplification of computation for the clustering process. Then, prediction procedure is executed labels series.…”

Section: Pattern Sequence Based Forecasting (Psf) Methodsmentioning

confidence: 99%

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The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

et al. 2020

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In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

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Section: Pattern Sequence Based Forecasting (Psf) Methodsmentioning

confidence: 99%

“…Those studies concluded the more accurate performance of the PSF method. It outperformed ARIMA and kNN in electric vehicle charging energy consumption forecasting [49]. Further, the hybridization of NN with PSF methods was exercised in [50].…”

Section: Introductionmentioning

confidence: 99%

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

et al. 2020

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“…In this policy, the cluster size is finalized with the use of multiple statistical tests to ensure efficiency in the clustering process. Further, References [18][19][20] used a single index (Silhouette index [15]) to simplify computation complexity in the clustering process. Then, with respect to cluster heads (K) generated with the k-means clustering method, the values in the original time series are transformed into label series.…”

Section: Conventional Psf Methodologymentioning

confidence: 99%

A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

et al. 2018

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Wind energy is a variable energy source with a growing presence in many electrical networks across the world. Wind-speed prediction has become an important tool for many agents involved in energy markets. In this paper, an approach to this problem is proposed by means of a novel method that outperforms results obtained by current direct and indirect wind-power prediction procedures. The first difference is that it is not strictly a direct or indirect method in the conventional sense because it uses information from both wind-speed and wind-power data series to obtain a wind-power series. The second difference is that it smooths down the wind-power series obtained in the first stage, and uses the resulting series for predicting new wind-power values. The process of smoothing is based on the label sequence generation process discussed in the pattern sequence forecasting algorithm and the Naive Bayesian method-based matching process. The result is a less chaotic way to predict wind speed than those offered by other existing methods. It has been assessed in multiple simulations, for which three different error measures have been used.

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“…They suggested that, instead of using multiple indexes, a single index could make computation simpler and they used the Davies Bouldin index to obtain the optimum number of clusters. Majidpour et al (2014) compared PSF to kNN and ARIMA, and observed that PSF can be used for electric vehicle charging energy consumption. It also proposed three modifications in the existing PSF algorithm.…”

Section: Introductionmentioning

confidence: 99%

PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

Bokde¹,

Asencio-Cortés²,

Martínez‐Álvarez³

et al. 2017

The R Journal

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This paper introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its usage with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository. This paper is published in The R Journal. Kindly cite this paper as: @article{RJ-2017-021, author = Neeraj Bokde and Gualberto Asencio-Cortés and Francisco Martínez-Álvarez and Kishore Kulat, title = PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm, year = 2017, journal = The R Journal, url = https://journal.r-project.org/archive/2017/RJ-2017-021/index.html, pages = 324-333, volume = 9, number = 1 }

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Modified pattern sequence-based forecasting for electric vehicle charging stations

Cited by 32 publications

References 11 publications

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

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