Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting

Pandey, Ajay Shekhar; Singh, Devender; Sinha, Sunil K.

doi:10.1109/tpwrs.2010.2042471

Cited by 109 publications

(47 citation statements)

References 13 publications

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“…CWT is obtained by the convolution of the signal to be analyzed and the scaled (a) and shifted (b) versions of the mother wavelet function (Ψ) , as shown in Eq. (1).…”

Section: Wavelet Transformmentioning

confidence: 99%

“…As the first step of system planning, load forecasting should be effective, since it determines the entire planning process. Power system planning based on load forecasting that is lower than required leads to restriction of energy to consumers, while planning based on the estimation of excess load results in uneconomical operating conditions [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term load forecasting without meteorological data using AI-based structures

Esener¹,

Yüksel²,

Kurban³

2015

Turk J Elec Eng & Comp Sci

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STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey's 24-hourahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedures. Local holidays' historical load data are changed into data with normal day characteristics, and the estimation results of these days are not included in error computation. To obtain more accurate results, a regulation on forecasted loads is proposed. Regulated and unregulated forecasting error percentages are computed as daily average MAPE and maximum daily MAPE, and compared between the proposed structures. A simulation is performed for the years 2009-2010 via the user interface created using MATLAB GUI.

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“…CWT is obtained by the convolution of the signal to be analyzed and the scaled (a) and shifted (b) versions of the mother wavelet function (Ψ) , as shown in Eq. (1).…”

Section: Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting without meteorological data using AI-based structures

Esener¹,

Yüksel²,

Kurban³

2015

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…As a result, to improve the prediction power, nonlinear models have been proposed. Neural Network (NN) (Benaouda et al, 2006;Rocha et al, 2005;Zhang et al, 2001;Pandey et al, 2010;Bashir et al, 2009;Amjady et al, 2009) based methods have been applied and shown to effectively learn the time dependent load series and capture the nonlinearity characteristics. Recently, Support Vector Machine (SVM), a machine learning technique, has also been used for load forecasting (Dongxiao et al, 2009;Che, 2012;Ping-Feng and Wei-Chiang, 2005).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on Sequential Relevance Vector Machine

Jang¹

2015

Industrial Engineering and Management Systems

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This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

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“…Although it has better performance relative to the ANN, the large amount of training data limits its performance. This weakness leads to using a combination of ANFIS with other methods to construct hybrid method [10][11][12]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, in Refs. [11,12], a combination of the fuzzy method and the ANN method is used for load forecasting.…”

Section: Introductionmentioning

confidence: 99%