COMBINAÇÃO LINEAR WAVELET SARIMA-RNA COM ESTÁGIOS MULTIPLOS NA PREVISÃO DE SÉRIES TEMPORAIS

In this paper, we put forward a hybrid methodology for combining forecasts to (stochastic) time series referred to as Wavelet Linear Combination (WLC) SARIMA-RNA with Multiple Stages. Firstly, the wavelet decomposition of level p is performed, generating (approximations of the) p+1 wavelet components (WCs). Then, the WCs are individually modeled by means of a Box and Jenkins’ model and an artificial neural network - in order to capture, respectively, plausible linear and non-linear structures of autodependence - for, then, being linearly combined, providing hybrid forecasts for each one. Finally, all of them are linearly combined by the WLC of forecasts (to be defined). For evaluating it, we used the Box and Jenkins’ (BJ) models, artificial neural networks (ANN), and its traditional Linear Combination (LC1) of forecasts; and ANN integrated with the wavelet decomposition (ANNWAVELET), BJ model integrated with the wavelet decomposition (BJ-WAVELET), and its conventional Linear Combination (LC2) of forecasts. All predictive methods applied to the monthly time series of average flow of tributaries of the Itaipu Dam dam, located in Foz do Iguaçu, Brazil. In all analysis, the proposed hybrid methodology has provided higher predictive performance than the other ones

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large

Método híbrido interativo sarima support vector regression wavelet de múltiplos núcleos na previsão de séries temporais de instrumentos de barragens

Orientador : Prof. Dr. Arinei Carlos Lindbeck da SilvaCo-orientador : Prof. Dr. Luiz Albino Teixeira JúniorTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 22/12/2015Inclui referências : f.82-93Resumo: Nesta tese de Doutorado é apresentado um novo método preditivo híbrido, formado basicamente pela combinação dos métodos SARIMA, Support Vector Regression e Wavelet, denominado como SARIMA Support Vector Regression Wavelet de Múltiplos Núcleos (SSVRWMN), para a predição de valores de leitura de instrumentos de barragens de concreto de usinas hidroelétricas. Tendo as previsões pontuais, estimase o intervalo de confiança por meio da técnica Bootstrap. O método SSVRWMN Bootstrap contempla as seguintes abordagens: os modelos SARIMA (para mapear estruturas de autodependência lineares sazonais e simples); a decomposição Wavelet integrada com modelos Support Vector Regression (SVRs) (que mapeiam estruturas de autodependência não lineares e da frequência espectral inerente aos dados); a programação não linear (utilizada no ajuste numérico dos parâmetros associados às combinações de previsões) e a técnica Bootstrap aplicada aos resíduos do modelo SSVRWMN com a finalidade de se estimar o intervalo de confiança Bootstrap. O objetivo é produzir previsões para as séries temporais provenientes de instrumentos de barragens, agregadoras de informações estocásticas distintas capturadas por diferentes métodos. A fim de avaliar a eficiência do método preditivo SSVRWMN, este foi aplicado a algumas séries temporais provenientes da aferição de instrumentos situados no bloco-chave I10 da barragem de Itaipu (as quais são utilizadas na análise probabilística de risco de tombamento dos blocos no sentido montante-jusante). O desempenho preditivo alcançado pelo método SSVRWMN, em relação aos métodos preditivos SARIMA, SVR e composto SARIMA-SVR, foi notadamente superior, na presente tese. Palavras-chave: Séries temporais, Instrumentação de barragens, SARIMA, Wavelet, Support Vector Regression, Programação matemática, Técnica Bootstrap.Abstract: In this doctoral thesis is presented a new hybrid predictive method, formed by the combination of the methods SARIMA, Support Vector Regression and Wavelet referred as: SARIMA Support Vector Regression Wavelet of multiple kernels (SSVRWMN), for the prediction of reading values of concrete dams of hydroelectric plants. With the forecasts, it is estimated the confidence interval by Bootstrap technique. The method SSVRWMN Bootstrap includes the following approaches: SARIMA models (to map linear auto-dependence structures simple and seasonal); Wavelet decomposition integrated with Support Vector Regression models (SVR) (which map non-linear auto-dependence structures and spectral frequency inherent to data); nonlinear programming (used in the numerical adjustment of the parameters associated with combinations of forecasts) and the Bootstrap residual technique applied to residue the model SSVRWMN in order to estimate the Bootstrap confidence interval. The goal is to produce forecasts for the time series from instruments of dams that are aggregators of distinctive stochastic information captured by different methods. In order to evaluate the efficiency of method SSVRWMN predictive , this was applied to some time series from instruments located in block-key I10, of Itaipu Dam (which are used in probabilistic analysis tipover risk of blocks in the downstreamupstream direction). The predictive performance achieved by SSVRWMN concerning the traditional approaches SARIMA, SVR and composed SARIMA-SVR, have been remarkable superior. Keywords: Time series, dam Instrumentation, SARIMA, Wavelet, Support Vector Regression, Mathematical programming, Bootstrap Technique

Projeção de séries temporais por meio de um método híbrido wavelet-neural integrado com bootstrap

Orientador : Prof. Dr. Paulo Henrique SiqueiraCoorientador : Prof. Dr. Luiz Albino Teixeira JuniorTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 27/08/2015Inclui referências : f. 98-107Área de concentração : Programação matemáticaResumo: Nesta tese de doutorado, é proposto um novo método híbrido wavelet-neural integrado com um amostrador bootstrap para projeção pontual e intervalar de séries temporais estocásticas. Basicamente, combinam-se os métodos de encolhimento e de decomposição Wavelet no pré-processamento dos dados e, em seguida, uma Rede Neural Artificial (RNA) é usada para produzir as de previsões pontuais. A medida de incerteza do modelo RNA é obtida com a utilização de uma amostragem bootstrap dos resíduos do modelo RNA ajustado à série temporal subjacente. A fim de se obter o intervalo de confiança, calculou-se a média ponderada das previsões de B séries temporais oriundas do processo bootstrap, sendo os pesos determinados via otimização de um problema de programação não linear cuja função objetivo é a minimização da raiz quadrada do erro quadrático médio entre a combinação linear das B previsões e a série temporal subjacente. Além do intervalo de confiança, obtém-se também a estimativa do intervalo de previsão, sendo este último mais amplo que o primeiro, pois engloba as variâncias do modelo de regressão RNA e dos ruídos. A estimativa do desvio padrão dos ruídos foi alcançada com o treinamento de uma RNA com função custo oriunda da função de máxima verossimilhança log-normal, otimizada por meio da meta-heurística PSO. Para se averiguar a eficiência do método preditivo proposto, foram realizados experimentos computacionais para previsões pontuais envolvendo séries temporais utilizadas com frequência para este fim, podendo ser encontradas com facilidade em publicações da literatura especializada. As séries temporais referidas são as seguintes: Canadian Lynx, Wolf's Sunspot e Exchange Rate. Os desempenhos preditivos alcançados pelo método proposto, em relação às abordagens de outros autores, são efetivos e consideráveis. Em particular, os intervalos de confiança e previsão foram estimados para uma série temporal de vazão média mensal afluente da hidrelétrica de Itaipu, em Foz do Iguaçu, Brasil. Neste caso, para efeito de comparação, usaram-se os seguintes métodos de previsão: RNA, Box & Jenkins e decomposição wavelet integrada com rede neural artificial e ARIMA (Wavelet-RNA e Wavelet-ARIMA). Comparações com resultados de previsões obtidos através de modelos de Box & Jenkins e RNA, quando usados individualmente, constatam consideráveis ganhos preditivos auferidos com o uso do método proposto, reduzindo o erro preditivo em 62%, aproximadamente. Na comparação com os métodos compostos Wavelet-RNA e Wavelet-ARIMA, a redução do erro foi da ordem de 54%. Palavras-chave: Séries temporais, Wavelet, Redes Neurais Artificiais, Programação matemática, Amostrador Bootstrap.Abstract: In this doctoral thesis, it's proposed a new hybrid integrated wavelet-neural method with a bootstrap sampler for point and interval projection of stochastic time series. Basically, the methods Wavelet shrinkage and decomposition are combined in the pre-processing of data and then an Artificial Neural Network (ANN) is used to produce the point predictions. The ANN template uncertainty measurement is achieved with the use of a bootstrap sample of ANN template's waste adjusted to the underlying time series. In order to obtain the confidence interval, it was calculated the weighted average forecast of B time series derived from the bootstrap process, and these weights were determined by optimization of a nonlinear programming problem whose objective function is the minimization of square root of the medium squared error between the linear combination of N forecasts and the underlying time series. Beyond the confidence interval it's also obtained the estimate of prediction interval, the latter is wider than the first, because it includes the variances ANN regression model and the noises. The estimate standard deviation of the noises was achieved with the training of an ANN with cost function derived of the lognormal likelihood maximum function optimized by PSO meta-heuristics. To ascertain the efficiency of the predictive method proposed, computational experiments were performed for point predictions involving time series frequently used for this purpose and may be easily found in specialized literature publications. The time series mentioned are: Canadian Lynx, Wolf's Sunspot and Exchange Rate. The predictive performances achieved by the proposed method, in relation to approaches from other authors are effective and substantial. In particular, confidence and prediction intervals were estimated for a time series of monthly average flow tributary of Itaipu dam, in Foz do Iguaçu, Brazil. In this case, for comparison, they used the following prediction methods: ANN, Box & Jenkins, wavelet decomposition integrated with artificial neural networks and ARIMA (Wavelet-ANN and Wavelet-ARIMA). Comparison with results of predictions obtained through model Box and Jenkins and ANN, when used alone, find considerable predictive gains obtained using the proposed method, reducing the predictive error in 62% approximately. In comparison to the compounds methods Wavelet-ANN and Wavelet-ARIMA the reducing of error was approximately 54%. Keywords: Time series, Wavelet, Artificial Neural Networks, Mathematical programming, Bootstrap Sampler