Pattern similarity-based methods for short-term load forecasting – Part 2: Models

Dudek, Grzegorz

doi:10.1016/j.asoc.2015.07.035

Cited by 49 publications

(35 citation statements)

References 49 publications

(73 reference statements)

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“…These models construct the regression curve aggregating the forecast patterns from the history with weights dependent on the similarity between input patterns paired with the forecast patterns. Simulation studies reported in [33] were performed on the same datasets as in this work so you can compare results (see Table XI in [33]). Table 2 summarizes results for the above mentioned models.…”

Section: Simulation Studymentioning

confidence: 98%

“…Another group of STLF models using patterns: models based on the similarity between patterns of seasonal cycles are presented in [33]. They include: Nadaraya-Watson estimator, nearest neighbor estimation-based models and pattern clustering-based models such as classical clustering methods and new artificial immune systems.…”

Section: Simulation Studymentioning

confidence: 99%

“…Table 2 summarizes results for the above mentioned models. [22] PLSR -partial least-squares regression [22] N-WE -Nadaraya-Watson estimator [33] FNM -fuzzy neighborhood model [33] FP1+k-means -model based on k-means clustering of concatenated x-and y-patterns (similarly to SOM1) [33] FP2+k-means -model based on k-means clustering of x-and y-patterns independently (similarly to SOM2) [33] AIS1 -artificial immune system working on concatenated x-and y-patterns (similarly to SOM1) [33] AIS 2 -artificial immune system working on separate populations of patterns: type x and y (similarly to SOM2) [33] 5. CONCLUSIONS…”

Section: Simulation Studymentioning

confidence: 99%

See 2 more Smart Citations

Neural networks for pattern-based short-term load forecasting: A comparative study

Dudek

2016

Neurocomputing

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Section: Simulation Studymentioning

confidence: 98%

Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

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Neural networks for pattern-based short-term load forecasting: A comparative study

Dudek

2016

Neurocomputing

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“…Em [9],é proposto um modelo de previsão de carga baseado em similaridade de padrões. Este modelo facilita a utilização de diversos algoritmos de aprendizado de máquina em problemas baseados em séries temporais e provê, de forma intuitiva, a previsão baseada em padrões previamente conhecidos.…”

Section: Introductionunclassified

Modelos de Previsão de Carga: Abordagens imunoinspiradas baseadas em Similaridade de Padrões

Silva¹,

Lisboa²,

Vieira³

et al. 2018

Proceedings XIII Brazilian Congress on Computational Inteligence

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O problema de previsão de carga a curto prazo consiste em estimar a demanda futura de um período de até uma semana e possui fundamental importância nas operações de um sistema elétrico. Tal problema pode ser modelado em termos de similaridades de padrões, permitindo o uso de algoritmos de regressão baseados em aprendizado de máquina aplicados a análise de séries temporais. Neste trabalho, dois modelos imunoinspirados são avaliados e incorporados a um método de transformação. Os experimentos apresentam resultados interessantes, comprovando a aplicabilidade dos modelos e da estratégia adotada.A demanda de carga pode ser considerada como uma das informações mais importantes a serem utilizadas na operação de sistemas de energia. A operação dos sistemas de energiaé bastante dependente de tal informação, da qual decorre o funcionamento bem sucedido de geradores, subestações e linhas, assim como o planejamento de novas unidades no sistema de distribuição de energia.Desta forma, a realização da previsão da carga a curto prazo possui um papel fundamental, sobretudo nas tarefas de controle, agendamento e segurança dos sistemas elétricos. Considerando este fato, um grande número de modelos têm sido propostos, sejam estes baseados em regressão, análise estatística,

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“…In the points where the set of the nearest neighbours changes, the jumps on the function graph are observed. To avoid this inconvenience, a fuzzy membership of the training points to the neighbourhood of the query point was introduced [13]. In this approach, each training point belongs to the query point neighbourhood with a degree depending on the distance between these points.…”

Section: Fuzzy Nearest Neighbour Regressionmentioning

confidence: 99%

Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

Pełka

Dudek

2017

ITM Web Conf.

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Abstract. Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.

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Pattern similarity-based methods for short-term load forecasting – Part 2: Models

Cited by 49 publications

References 49 publications

Neural networks for pattern-based short-term load forecasting: A comparative study

Neural networks for pattern-based short-term load forecasting: A comparative study

Modelos de Previsão de Carga: Abordagens imunoinspiradas baseadas em Similaridade de Padrões

Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

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