Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal

Catalào, João P. S.; Pousinho, Hugo Miguel Inácio; Mendes, V.M.F.

doi:10.1109/tste.2010.2076359

Cited by 143 publications

(87 citation statements)

References 45 publications

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“…Furthermore, it has self-learning capabilities provided by the NN, which help it to self-adjust its parameters due to fuzzy capabilities [19,45]. The general ANFIS structure is based on several layers, which provide the fuzzification, rules, normalization data, desfuzzification, and data reconstruction process as described in [35,44]. Figure 3 briefly describes the multi-layer feed-forward network ANFIS structure.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…Finally, Equation (15) defines the ANFIS output node, that is, the fifth layer where the summation Σ is made. As reported in [19,35], in this paper, the ANFIS structure follows the least-squares and back-propagation gradient descent method, considering the Takagi-Sugeno approach. …”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…Following the same concept from the MAPE criterion, the uncertainty of the HWDA model is evaluated using the error variance, described as [19,35]:…”

Section: Forecasting Error Evaluationmentioning

confidence: 99%

“…Moreover, for wind power forecasting, the normalized mean absolute error (NMAE) criterion is used [35,45]:…”

Section: Forecasting Error Evaluationmentioning

confidence: 99%

“…For all results, it is possible to observe how the HWDA approach correctly forecasts the unexpected and abrupt changes of the wind power profile, that is, its uncertainty behaviour during the whole day of forecasting. Tables 6 and 7 provide a comparative study between the HWDA approach using MAPE and the daily error variance criterion and five other previously published methodologies, namely NN [29], Tables 6 and 7 provide a comparative study between the HWDA approach using MAPE and the daily error variance criterion and five other previously published methodologies, namely NN [29], Tables 6 and 7 provide a comparative study between the HWDA approach using MAPE and the daily error variance criterion and five other previously published methodologies, namely NN [29], Tables 6 and 7 provide a comparative study between the HWDA approach using MAPE and the daily error variance criterion and five other previously published methodologies, namely NN [29], NF [32], WNF [34], WPA [35], and HEA [45], respectively. When the HWDA approach is used, the MAPE criterion has an average value of 3.37%, representing an enhancement of 11.28% compared to the HEA methodology, which is again significant.…”

Section: Wind Power Forecastingmentioning

confidence: 99%

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Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

et al. 2016

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Abstract:The uncertainty and variability in electricity market price (EMP) signals and players' behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

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Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…Following the same concept from the MAPE criterion, the uncertainty of the HWDA model is evaluated using the error variance, described as [19,35]:…”

Section: Forecasting Error Evaluationmentioning

confidence: 99%

“…Moreover, for wind power forecasting, the normalized mean absolute error (NMAE) criterion is used [35,45]:…”

Section: Forecasting Error Evaluationmentioning

confidence: 99%

Section: Wind Power Forecastingmentioning

confidence: 99%

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Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

et al. 2016

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Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs

Mandal

Zareipour

Rosehart

2014

Int. J. Energy Res.

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SUMMARYThis paper describes the problem of short-term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts.

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A hybrid electricity price forecasting model for the Nordic electricity spot market

Voronin

Partanen

Kauranne

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY A hybrid electricity price forecasting model for the Finnish electricity spot market is proposed. The daily electricity price time series is analyzed in two layers – normal behavior and spiky behavior. Two different data preprocessing techniques are applied to handle trend and seasonality in the time series. An ARMA‐based model is used to catch the linear relationship between the normal range price series and the explanatory variable, a GARCH model is used to unveil the heteroscedastic character of residuals and a neural network is applied to present the nonlinear impact of the explanatory variable on electricity prices and improve predictions based on time series techniques. The probability of a price spike occurrence and the value of a price spike are produced by a Gaussian Mixture model and K‐nearest neighboring model, respectively. Forecasts of normal range prices and price spikes are generated to form an overall price forecast up to one week ahead. The results show that hybridization of the normal range price and price spikes forecasts may provide comprehensive and valuable information for electricity market participants. Copyright © 2013 John Wiley & Sons, Ltd.

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Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal

Cited by 143 publications

References 45 publications

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs

A hybrid electricity price forecasting model for the Nordic electricity spot market

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