Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

Kong, Zhengmin; Zhou, Xia; Cui, Yande; Lv, He

doi:10.3390/app9204215

Cited by 9 publications

(8 citation statements)

References 44 publications

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“…These technique outputs are much more balanced compared to that of WT, and this can easily identify weak and singular component signals. If θ(t) is the scaling function, and ψ(t) is the wavelet function, then both can be related, as [31,34]: In Equation ( 4), l im and h im denotes the low-and high-pass frequency coefficients of the signal, respectively. The full wavelet packet {δ n (t)}(n ∈ Z + ) is on the basis of δ o (t) = θ(t), which can be derived as:…”

Section: Wavelet Packet Based Decomposition (Wpd)mentioning

confidence: 99%

“…Advanced WT has been presented in which the entropy cost function is used to select the best wavelet basis for data decomposition, mutual information for feature selection, and neural networks for prediction of electricity load with a one and multi-step-ahead basis [33]. In order to deal with the data noise of WPD, decomposed series correlation analysis has been deployed, and data with all the features has been trained through an improved weighted extreme learning machine [34]. In this paper, to extract the maximum features of the input signal, the data was decomposed using the proposed signal processing technique, i.e., WPD.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

et al. 2021

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In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression (R2) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between −492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.

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Section: Wavelet Packet Based Decomposition (Wpd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

et al. 2021

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show abstract

“…This is why a probabilistic approach is useful when analyzing the uncertainty of the energy demand/consumption obtained by using weather forecast, the probabilistic load forecast (PLF) [ 44 ]. Conventional load forecasting provides a specific value of the demand/consumption of the building, which is called point forecast.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of the importance and influence that weather forecast has on the simulation, the effect of its uncertainty has not been fully studied in the literature [38][39][40], and few research works have directly investigated its effect [35,37,[41][42][43]. This is why a probabilistic approach is useful when analyzing the uncertainty of the energy demand/consumption obtained by using weather forecast, the probabilistic load forecast (PLF) [44]. Conventional load forecasting provides a specific value of the demand/consumption of the building, which is called point forecast.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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“…The wavelet packet transform (WPT) is an extension of the WT, which provides a complete level-by-level decomposition of the signal [9]. WPT has become a popular signal processing method, which has been applied in various fields, such as in the medical industry [10], wind industry [11,12], electric power industry [12], condition monitoring and fault diagnosis of mechanical systems and structures [13][14][15][16][17][18], fatigue damage analysis of composites [19], and many more. The WPT is a signal analysis tool that has the feature of time-frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

Guidelines for Impact Echo Test Signal Interpretation Based on Wavelet Packet Transform for the Detection of Pile Defects

Kim¹,

Mission²,

Dinoy³

et al. 2020

Applied Sciences

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Nonlinear amplification is typically done on velocity signals from low-strain pile integrity tests to enhance weak echoes and superimpose any peak reflections. This conventional method may sometimes fail to untangle the hidden information within the signal that is obscured by the presence of noise. In this study, a pile defect identification system based on the conventional nonlinear amplification method and the wavelet packet transform (WPT) was proposed to easily detect the presence of any geometric or material defects by identifying feature parameters. Diagnostic rules, which have been lacking in the literature, were presented to serve as a guide in interpreting decomposed signals and in analyzing various characteristics of peak waveforms that are associated with certain types of defects. In this study, the finite element method was used to simulate the impact echo test of nine cases of defective piles. To verify the proposed scheme, six data sets of the nine cases of defective piles were made, in which a total of 54 piles were analyzed. The results of the study showed that the identification method based on WPT could detect defects 87.04% of the time compared to the conventional method, which only detected defects 64.81% of the time.

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Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

Cited by 9 publications

References 44 publications

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Guidelines for Impact Echo Test Signal Interpretation Based on Wavelet Packet Transform for the Detection of Pile Defects

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