Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting

Reis, Agnaldo J. Rocha; Silva, A.P.A. da

doi:10.1109/tpwrs.2004.840380

Cited by 244 publications

(69 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The continuous wavelet transform W (a,b) of signal f (x) with respect to a wavelet φ (x) is given by [12], [13]: (1) where φ (x) is the mother wavelet. The value of the wavelet transform W (a,b) is called the wavelet coefficient, a and b are real numbers known as time scale or dilation variable and time shift or translation variable, respectively.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…Since the CWT is achieved by continuously scaling and translating the mother wavelet, substantial redundant information is generated. Therefore, as an alternative, the mother wavelet can be scaled and translated using certain scales and positions usually based on powers of two [12], [14]. This scheme is more efficient and just as accurate as the CWT [12].…”

Section: Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

Areekul¹,

Senjyu²,

Urasaki³

et al. 2011

Journal of International Council on Electrical Engineering

View full text Add to dashboard Cite

-This paper proposes designing a model using artificial neural network (ANN) and wavelet techniques to increase the accuracy of short term price forecast in the electricity market. The prior electricity price data are treated as time series. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Discrete Wavelet Transform (DWT), while the forecast model is based on wavelet multi-resolution (MR) decomposition. The wavelet coefficient series are used to train the artificial neural network and used as the inputs to the ANN for electricity price prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the ANN. To get the final forecast data, the outputs from the ANN are recombined using the same wavelet technique. The model was evaluated with electricity price data of New South Wales Australia for the year 2008. Empirical results indicate that the WT-ANN combination model improves the price forecasting accuracy.

show abstract

Section: Wavelet Transformmentioning

confidence: 99%

Section: Wavelet Transformmentioning

confidence: 99%

Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

Areekul¹,

Senjyu²,

Urasaki³

et al. 2011

Journal of International Council on Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…Because an electric load series have striking characteristics such as nonlinearity and nonstationarity, statistical methods which assume stationary time-series have difficulty in predict-ing future electricity demand satisfactorily. Recently, AI-based methods including artificial neural networks (ANNs) [9][10][11], and support vector regression (SVR) [2][3][4] have been applied successfully for load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Kim

2016

IJFIS

View full text Add to dashboard Cite

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

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

View full text Add to dashboard Cite

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.

show abstract

Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting

Cited by 244 publications

References 17 publications

Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Short-Term Load Forecasting Based on Sequential Relevance Vector Machine

Contact Info

Product

Resources

About