Price forecasting using wavelet transform and LSE based mixed model in Australian electricity market

Aggarwal, Shubhani; Saini, Lalit Mohan; Kumar, Ashwani

doi:10.1108/17506220810919054

Cited by 22 publications

(18 citation statements)

References 40 publications

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“…Aggarwal et al [106] also forecasted electricity prices. For this purpose, they divided each day into segments and they applied a multiple linear regression (MLR) to the original series or the constitutive series obtained by the wavelet transform depending on the segment.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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“…In addition, the work analyzed the optimal number of samples used to build the prediction models. Aggarwal et al [3] divided each day into segments and they applied a multiple linear regression to the original series or the constitutive series obtained by the wavelet transform depending on the segment. Moreover, the regression model used different input variables for each segment.…”

Section: Electricity Prices Time Series Forecastingmentioning

confidence: 99%

Energy Time Series Forecasting Based on Pattern Sequence Similarity

Martínez‐Álvarez

Troncoso

Riquelme

et al. 2011

IEEE Trans. Knowl. Data Eng.

224

138

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Abstract-This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.

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“…Even if close patterns have close labels, error is not avoidable using f (·) as an inner product (see (1)). That is due to the distribution of patterns.…”

Section: The Effect Of Distribution Of Patternsmentioning

confidence: 99%

“…Optimal domain ith row of matrix X, the Moore-Penrose pseudo inverse of X is evaluated by [1,6,12,41,45,47,51,52,54,57,62,65]: 2…”

mentioning

confidence: 99%

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

et al. 2014

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Classification of data is an important problem which has attracted many researchers to introduce new approaches. In this paper, we propose Mapping to Optimal Regions (MOR) as a new method for multi-class classification task to reduce computational and memory complexities. It requires only one simple mapping from input space to optimal regions. The optimal domain is estimated using a multi objective cost function to increase the region size and the generalization ability of the mapping and to reduce the mapping error. Finally, the centers of optimal regions are determined with respect to the optimal size of the regions and the code assignment process which reduces the effect of inappropriate labeling. A Hierarchical version of MOR (HMOR) is presented for datasets with high number of classes or low dimensional feature spaces. By taking the advantage of MOR, the complexity reduces significantly in comparison to the other classifiers.

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Price forecasting using wavelet transform and LSE based mixed model in Australian electricity market

Cited by 22 publications

References 40 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

Energy Time Series Forecasting Based on Pattern Sequence Similarity

Mapping to optimal regions; A new method for multi-class classification task to reduce complexity

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