Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting

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

doi:10.1109/pes.2011.6038968

Cited by 57 publications

(88 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the Db4 is chosen as the motherwavelet function due to a better trade-off between smoothness and length [19]. Also, the DWT used in this paper was created on four filters divided into two groups: the decomposition group, composed of low-pass and high-pass filters, and the reconstruction group, composed of low-pass and high-pass filters as described in [44,45].…”

Section: Wavelet Transformmentioning

confidence: 99%

“…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].…”

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%

See 3 more Smart Citations

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Wavelet Transformmentioning

confidence: 99%

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%

See 2 more Smart Citations

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Another approach in electricity price forecasting is the use of hybrid neurofuzzy systems. An adaptive-network-based fuzzy inference system (ANFIS) has been investigated and results proved that such scheme is superior to MLP approaches [9].…”

Section: Introductionmentioning

confidence: 99%

Electricity price forecasting using asymmetric fuzzy neural network systems

Alshejari

Kodogiannis

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Abstract-Electricity price forecasting is considered as an important tool for energy-related utilities and power generation industries. The deregulation of power market, as well as the competitive financial environment, which have introduced new market players in this field, makes the electricity price forecasting problem a demanding mission. The main focus of this paper is to investigate the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting. The proposed model has been developed from existing Takagi-Sugeno-Kang fuzzy systems by substituting the IF part of fuzzy rules with an asymmetric Gaussian function. In addition, a clustering method is utilised as a pre-processing scheme to identify the initial set and adequate number of clusters and eventually the number of rules in the proposed model. The results corresponding to the minimum and maximum electricity price have indicated that the proposed forecasting scheme could be considered as an improved tool for the forecasting accuracy.

show abstract

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

Mandal

Zareipour

Rosehart

2014

Int. J. Energy Res.

View full text Add to dashboard Cite

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.

show abstract

Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting

Cited by 57 publications

References 0 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

Electricity price forecasting using asymmetric fuzzy neural network systems

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

Contact Info

Product

Resources

About