Load forecasting in the user side using wavelet-ANFIS

Giacometto, F.; Cárdenas, Juan José; Kampouropoulos, Konstantinos; Romeral, J.L.

doi:10.1109/iecon.2012.6388575

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…The period under review belongs to winter of 2014 and was: One day (4 th of February), one week (3 rd to 9 th of February) and two weeks (3 rd to 16 th of February). The results are satisfactory considering that the maximum value of MAPE in the forecast studies of this type should not exceed 5% (Giacometto et al, 2012;Hippert et al, 2001). Figure 7 illustrates the evolution of the error percentage error for the two-week period of February 2014.…”

Section: Resultsmentioning

confidence: 63%

“…The prediction based on the artificial neural networks, was widely accepted by the scientific and engineering spheres, becoming the most widespread technique for the load forecasting. There are several scientific publications that prove the quality and robustness of predictions based on neural networks (Chen et al, 1996;Giacometto et al, 2012;Hippert et al, 2001;Lino et al, 2016). Figure 4 shows the artificial neuron model scheme.…”

Section: General Analysismentioning

confidence: 99%

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Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora

Chemetova

Santos

Neves

2017

Journal of Information Systems Engineering & Management

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Currently, load forecasting is a fundamental task for planning, operation and exploration of the electric power systems. The importance of forecasting has become more evident with the restructuring of the national energy sector, thus, promoting projects linked to smart grids, namely in Portugal -InovGrid. This study proposes the computational forecast model of the load diagram based on the Levenberg-Marquardt algorithm of Artificial Neural Networks. The used data are the time series of active power, recorded by EDP Distribution Telemetry System, and the climatic time series of the Portuguese Institute of the Sea and Atmosphere, collected on the city of Évora. The forecast horizon is short term: from one hour to a week. The results showed that main statistical error parameter (mean absolute percentage error) was not exceed 5%.

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Section: Resultsmentioning

confidence: 63%

Section: General Analysismentioning

confidence: 99%

Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora

Chemetova

Santos

Neves

2017

Journal of Information Systems Engineering & Management

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“…The Daubechies wavelet of order 10(db10), one of the most widely used wavelet families, is chosen as the wavelet function. This wavelet offers an appropriate balance between wave length and smoothness [15][16]. In this study, three wavelet decomposition levels (2-4-8) were employed, as well as in similar studies by Kisi [35] and Nourani et al [13].…”

Section: Resultsmentioning

confidence: 99%

A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting

Shabri¹

2014

ams

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Drought forecasting plays an important role in the planning and management of water resources systems. In this paper, a hybrid wavelet and adaptive neuro-fuzzy inference system (WANFIS) is proposed for drought forecasting. The WANFIS model was developed by combining two methods, namely a discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) model. To assess the effectiveness of this model, the standardised precipitation index (SPI) was applied for meteorological drought analysis at five rainfalls gauging stations located around the Klang River basin, Malaysia. The SPI drought forecasting capability performance of the WANFIS model is compared with the autoregressive integrated moving average (ARIMA) and ANFIS models using various statistical measures. Comparison of the results reveals that the WANFIS model performs better than the traditional ANFIS and ARIMA models.

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“…This data provide us a great convenience to estimate the load consumption. Hence the historical data is selected as an input like previous studies [8,11] in literature.…”

Section: Load Characteristicsmentioning

confidence: 99%

“…ANFIS is proposed nference system of the hod adjusts the inputparameters of an ng training data. This ANN and so ANFIS m historical data [11]. ucture m has three inputs and cal load, temperature put is forecasted load -2011 data is selected is selected for testing.…”

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confidence: 99%

A comparative study of artificial neural network and ANFIS for short term load forecasting

Çevik

Çunkaş

2014

Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

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Short term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012. Keywords-short term load forecasting; artificial neural networks; ANFIS.I.

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Load forecasting in the user side using wavelet-ANFIS

Cited by 11 publications

References 21 publications

Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora

Short Term Load Forecasting in Smart Grids: Case Study of the City of Évora

A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting

A comparative study of artificial neural network and ANFIS for short term load forecasting

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