Long-term Forecasting of Intermittent Wind and Photovoltaic Resources by using Adaptive Neuro Fuzzy Inference System (ANFIS)

Makhloufi, Saida; Debbache, Mohammed; Boulahchiche, Saliha

doi:10.1109/icweaa.2018.8605102

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…O modelo proposto possui uma melhoria para a função de ativação da rede de previsão, onde a não linearidade produz efeitos diretamente na janela temporal. Comparandose com os trabalhos feitos por Liu and Lu (2011), Makhloufi et al (2018), Mitkov et al (2019) e Lukhyswara et al (2019), a estimação dos parâmetros de histerese na metodologia realizada neste trabalhoé feita diretamente nos dados. Desta forma,é apresentado uma sensibilidade no aprendizado, o que beneficia o reconhecimento das amplitudes de dados, com exceção de picos.…”

Section: Conclusãounclassified

“…Todavia, a prediçãoé fundamentada em amostragem de dados de longo prazo, e nesse conjunto, existe a possibilidade de serem corrompidos, devido a erros ocorridos em algum intervalo, ausência de dados, flutuações e um componente de caos na formação da série. O estudo de obtenção de dados de previsão de energias renováveisé descrito por diversos autores como Lukhyswara et al (2019) e Makhloufi et al (2018), que descrevem técnicas de previsão baseadas em series temporais para previsão Solar e eólica. A diferença desta abordagem em relação as anteriores está na inclusão do neurônio baseado no modelo de histerese comoé abordado em de .…”

Section: Introductionunclassified

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Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

Jesus

Pontes

Borges

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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This paper focuses on the study of the strategic prediction of renewable sources of intermittent energy, using bio-inspired computational models, developed in Python, with the gaim of providing mechanisms that help in the monitoring and control of smart grids. To perform the learning of the neural network, we used the Backpropagation and Feedforwad algorithms. This neural network makes use of hysteresis neurons through the L 2 P model that, therefore, iterates the data to reproduce the prediction curves. To evaluate the model, real data obtained from the National Institute of Meteorology (INMET) was used. Results are presented through the application of the L 2 P model as a neural network and compared to existing structures in neural networks such as the ARIMA method, showing the good performance of the L 2 P neural network. Resumo: Este artigo concentra-se no estudo da predição estratégica de fontes renováveis de energia intermitente, utilizando modelos computacionais baseados em redes neurais artificiais (RNAs), desenvolvidos em linguagem Python, com o intuito de fornecer mecanismos que auxiliem no monitoramento e controle das redes elétricas inteligentes. Para a realização do aprendizado da rede neural, utilizou-se os algoritmos Backpropagation e Feedforwad. A rede neural em questão, faz o uso de neurônios de histerese por meio do modelo L 2 P (Limity Loop Proximity) que, portanto, realiza a iteração dos dados para reproduzir as curvas de previsão de radiação solar e velocidade do vento. Para avaliar o modelo, empregou-se dados reais obtidos no Instituto Nacional de Meteorologia (INMET). Resultados são apresentados por meio da aplicação do modelo L 2 P como rede neural e comparado a estruturas já existentes em redes neurais como o método ARIMA, evidenciando o bom desempenho da rede L 2 P .

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Section: Conclusãounclassified

Section: Introductionunclassified

Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

Jesus

Pontes

Borges

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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“…In [10], a short-term forecast method based on cuckoo search and differential evolution algorithms was proposed. In [11], the authors provided the method for long-term forecasting of intermittent wind and photovoltaic resources, which depended on an adaptive neuro fuzzy inference system. Although the current forecasting technology for wind turbines (WTs) and photovoltaic units (PVs) has made great progress, certain deviations still exist in the forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Evolutionary Dispatch for Integrated Community Energy Systems Considering Uncertainties of Renewable Energy Sources and Internal Loads

et al. 2021

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For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long-term forecast data of renewable energy sources and internal loads are depicted by multi-interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short-term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win-win situation for both energy companies and users.

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A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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Long-term Forecasting of Intermittent Wind and Photovoltaic Resources by using Adaptive Neuro Fuzzy Inference System (ANFIS)

Cited by 10 publications

References 23 publications

Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

Optimal Evolutionary Dispatch for Integrated Community Energy Systems Considering Uncertainties of Renewable Energy Sources and Internal Loads

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

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