Application of a control algorithm for wind speed prediction and active power generation

Flores, Pedro; Tapia, A.; Tapia, G.

doi:10.1016/j.renene.2004.07.015

Cited by 144 publications

(68 citation statements)

References 8 publications

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“…The authors of [7] compared Autoregressive-moving-average model (ARMA) models, which perform linear mapping between inputs and outputs, with Artificial Neural Network (ANN) models and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform non-linear mapping. The results underline that high accuracy for long time horizon in the wind power forecasting is given by non-linear models as the ANN, as also shown in [8][9][10][11][12][13][14][15][16][17]. A review of previous studies, which report the application of ANN to short-term load forecasting, is given in [18].…”

Section: Introductionmentioning

confidence: 58%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

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Section: Introductionmentioning

confidence: 58%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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“…Many case studies indicate ANN is an effective tool to simplify the forecasting problem (Alexiadis, Dokopoulos, Sahsamanoglou, & Manousaridis, 1998;Carolin Mabel & Fernandez, 2008;Flores, Tapia, & Tapia, 2005;Kaur, Kumar, & Segal, 2016;Mohandes, Rehman, & Halawani, 1998;Li et al, 2001). The biggest challenge for ANN application in wind power prediction is to select appropriate input variables.…”

Section: Intelligent Algorithmsmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed. ARTICLE HISTORY

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“…Na literatura é possível destacar trabalhos sobre a utilização de RNA para previsão de série temporal da velocidade do vento. Flores et al (2005) realizaram a previsão da velocidade dos ventos em uma fazenda eólica usando RNA, sendo baseada no algoritmo de aprendizagem backpropagation, cuja avaliação foi realizada com medição de dados reais de duas diferentes localizações.…”

Section: Redes Neurais Artificiais (Rna)unclassified

Métodos de Previsão de Séries Temporais e Modelagem Híbrida ambos Aplicados em Médias Mensais de Velocidade do Vento para Regiões do Nordeste do Brasil

Camelo

Lúcio

Leal

et al. 2017

Rev. bras. meteorol.

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ResumoEsse trabalho tem como objetivo realizar previsões de séries temporais da velocidade do vento em termos de médias mensais no nordeste brasileiro. Foram testados os seguintes modelos de séries temporais, Auto-Regressivo Integrado de Médias Móveis (ARIMA) e Holt-Winters (HW), e também inteligência artificial computacional com o uso de Redes Neurais Artificiais (RNA). E ainda foram testados dois modelos híbridos, o primeiro com combinação dos modelos ARIMA e RNA, o qual existem trabalhos na literatura, e o segundo trata-se de uma tentativa de combinação dos modelos HW e RNA. As séries ajustadas obtidas pelos modelos híbridos, mostram-se eficientes em acompanhar o perfil das séries observadas das regiões de estudo, com semelhanças aos dados observados em termos de máximos e mínimos, indicando assim, a capacidade dos modelos em representar sazonalidades. O cálculo da estatística de erros envolvendo o modelo híbrido (HW e RNA) obteve os menores valores em Fortaleza, São Luís e Natal, por exemplo, com valores de erros percentuais de 3,80%, 4,91% e 2,85%, respectivamente. A redução das variáveis da estatística de erros por parte dos modelos híbridos quando comparado separadamente a utilização dos modelos (ARIMA, HW e RNA) poderá influenciar nas precisões das velocidades dos ventos previstas. Palavras-chaves: energias renováveis, ARIMA, RNA, Holt-Winters. Time Series Forecasting Methods and Hybrid Modeling both Applied onMonthly Average Wind Speed for Regions of Northeastern Brazil. AbstractThe objective of this work is to perform time series forecasts of wind speed in terms of monthly averages in the Brazilian Northeast. The following time-series models, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters (HW) were tested, as well as computational artificial intelligence with the use of Artificial Neural Networks (ANN). In addition, two hybrid models were tested, the first with a combination of the ARIMA and ANN models, which work in the literature, and the second is an attempt to combine HW and ANN models. The adjusted series obtained by the hybrid models, are efficient to follow the profile of the observed series of the study regions, with similarities to the data observed in terms of maxima and minima, thus indicating the capacity of the models to represent seasonalities. The calculation of error statistics involving the hybrid (HW and ANN) model obtained the lowest values in Fortaleza, São Luís and Natal, for example, with percent error values of 3.80%, 4.91% and 2.85%, respectively. The reduction of the statistical variables of errors by the hybrid models when comparing the use of the models (ARIMA, HW and ANN) separately may influence the predictions of expected wind velocities.

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Application of a control algorithm for wind speed prediction and active power generation

Cited by 144 publications

References 8 publications

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

A review of forecasting algorithms and energy management strategies for microgrids

Métodos de Previsão de Séries Temporais e Modelagem Híbrida ambos Aplicados em Médias Mensais de Velocidade do Vento para Regiões do Nordeste do Brasil

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