Discrete Fourier Transform Based Approach to Forecast Monthly Peak Load

Beiraghi, Mojtaba; Ranjbar, A.M.

doi:10.1109/appeec.2011.5748585

Cited by 15 publications

(3 citation statements)

References 4 publications

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“…In Table 1, we present a summary of studies which involve Fourier transform for forecasting purposes. Most of these cases deal with data sets with obvious trend and/or seasonal structures (ie, González-Romera et.al., 2008;Beiraghi, Ranjbar, 2011, Dong et.al., 2011Ichinose et. al., 2012, Zong-chang, 2013 and use FT along with other methods as noted.…”

Section: Literature Review and Methodologymentioning

confidence: 99%

Hizli Fourier Dönüşümü Algori̇tmasi İle Düzensi̇z Talep Altinda İş Yükü Öngörüsü

Koyuncu

Turfan²

2020

Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Forecasting process may become cumbersome under erratic demand structures where no conventional methodology seems to provide plausible outcomes. The company investigated in this paper is subject to this condition and needs to forecast the workload in order to plan the indispensable process enhancement activities. Two parameters to be forecasted are the total production volume and the ratio of in-house production ratio with respect to the total production. The required forecasts for relatively regular structured in-house production to total production ratio are computed via static forecasting. The more important erratic structured total production volume forecasts are computed using Fast Fourier Transform algorithm. Total production is further observed during the next year to check the reliability of the forecasts. The resulting respective Mean Absolute Percentage Errors of approximately 11% (in-house ratio) and 14-22% (total production) are considered acceptable under the erratic demand structure.

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Section: Literature Review and Methodologymentioning

confidence: 99%

Hizli Fourier Dönüşümü Algori̇tmasi İle Düzensi̇z Talep Altinda İş Yükü Öngörüsü

Koyuncu

Turfan²

2020

Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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“…Hybrid methods are constructed based on combining different methods. Most of the hybrid methods are defined based on combination of feature extraction techniques like frequency spectral analysis, for example, wavelet transformation [16], Fourier transformation [17]. Furthermore, hybrid methods are also implemented in the other forms of combinational methods such metaheuristic algorithm (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial‐temporal learning structure for short‐term load forecasting

Ganjouri

Moattari

Forouzantabar

et al. 2022

IET Generation Trans & Dist

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In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers’ consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data.

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“…Accurate modeling of a certain time-series waveform function by Fourier analysis approach can be extended to predict the future outcome. Fourier-based forecasting has been applied for several time-series condition, for example in market demands [11] [12], electrical peak load [13], stock market [14][15], and many more. As far as the authors concern, broadband demand forecasting based on fourier analysis in Indonesia has not been found in any literature.…”

Section: Introductionmentioning

confidence: 99%

Broadband user demand forecasting in Indonesia based on Fourier analysis

Siswanto

Haryadi

2015

2015 1st International Conference on Wireless and Telematics (ICWT)

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In this paper, a broadband user demand forecasting in Indonesia based on Fourier analysis is presented. In this approach, the historical data of broadband user demand is considered as time-series data. To obtain trend of the time-series data, polynomial expression is incorporated in the Fourier series representation instead of constant. The presented forecasting model is then confirmed its validity by using hypotethical daily price fluctuation data. It can be shown that the presented forecasting model gives good agreement between forecasted data and actual data. The same forecasting model then is applied to forecast broadband user demand in Indonesia. Several cases of fixed and mobile broadband are presented. It can be shown that the forecasting model based on Fourier analysis gives good result with the error of the estimated value is less than 10%.

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Discrete Fourier Transform Based Approach to Forecast Monthly Peak Load

Cited by 15 publications

References 4 publications

Hizli Fourier Dönüşümü Algori̇tmasi İle Düzensi̇z Talep Altinda İş Yükü Öngörüsü

Hizli Fourier Dönüşümü Algori̇tmasi İle Düzensi̇z Talep Altinda İş Yükü Öngörüsü

Spatial‐temporal learning structure for short‐term load forecasting

Broadband user demand forecasting in Indonesia based on Fourier analysis

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