Estimation and forecast accuracy of regional photovoltaic power generation with upscaling method using the large monitoring data in Kyushu, Japan

Umizaki, Mitsuhiro; Uno, Fumichika; Oozeki, Takashi

doi:10.1016/j.ifacol.2018.11.766

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…This paper uses maximum warping to preprocess the input parameters. All data were normalized to the 0.0 to 1.0 range according to formula (1) [10].…”

Section: Wind Pg Power Prediction Methodsmentioning

confidence: 99%

Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis

2020

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With the continuous depletion of fossil energy and the continuous destruction of the ecological environment, wind energy(WE) and photovoltaic PG(PPG) are more and more emerging power generation(PG) technologies, and WE and PPG systems will become the mainstream of PG in my country's power grid in the future industry. PPG will be affected by the weather, resulting in great uncertainty in the PG output of the photovoltaic system(PS). If it is integrated into the large power grid, it will also bring immeasurable impact to the power grid. Therefore, in order to ensure the safe and stable operation of the power system and the coordinated development of power supply and distribution, it is crucial to use big data technology to establish a PG prediction model(PM). For wind PG, this paper establishes a short-term(ST) PG PM based on BP-NN, and proposes several correction models; for PPG, this paper establishes LS-SVM combined with Markov's PM, which can predict both sunny and rainy days. Predict the PPG in different weather. Through the research on wind PG forecasting methods and PPG forecasting methods, the accurate forecast of ST PG can be achieved, which provides certain theoretical and technical support for the PG industry of the power grid to better supply power to users in the future.

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“…This paper uses maximum warping to preprocess the input parameters. All data were normalized to the 0.0 to 1.0 range according to formula (1) [10].…”

Section: Wind Pg Power Prediction Methodsmentioning

confidence: 99%

Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis

2020

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“…So, it is essential to specify the forecast horizon which contributes to the judgmentcreating actions, based on the designers' assumptions and usages, in the smart grids, selecting the suitable strategy for the prediction of solar PV output power [16]. To reduce the prediction errors, the estimation process has a prominent role as considered a prior step for the forecast [17], [18]. Moreover, ML techniques are used to specify and estimate the performance ratio of a solar PV system as one of the reliability indicators.…”

Section: Introductionmentioning

confidence: 99%

Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

et al. 2023

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His research interests include digital signal processing for communications, multimedia, image, and video coding, low-power wireless communications, one-bit ADC multiple-input multiple-output, underwater communication, index and spatial modulation, Li-Fi technology, and visible light communication. He is a Technical Committee Member of many international conferences and a Reviewer of many international conferences, journals, and transactions. Moreover, he was the General Co-Chair of the IEEE ITCE, in 2018. Mohammed Farrag (Member, IEEE) received the B.Sc. degree in electrical engineering and the M.Sc. degree in communication and electronics from Assiut University, Egypt, in 2001 and 2008, respectively, and the Ph.D. degree in communication and electronics engineering from the Egypt-Japan University of Science and Technology (E-JUST), in 2013.

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“…De tal forma, um método preciso de predição de curto prazo de geração de energia elétrica a partir de sistemas fotovoltaicos se torna de grande importância, visto que as concessionárias e permissionárias devem garantir a continuidade da energia, assim como gerenciar as taxas de aumento no sistema, reduzindo os erros e otimizando o processo (Barbieri;Rajakaruna;Ghosh, 2017;Umizaki;Uno;Oozeki, 2018).…”

Section: Introductionunclassified

Desenvolvimento de metodologia para estimativa da geração diária de energia elétrica de sistemas fotovoltaicos a partir de histórico de operação e redes meteorológicas

Zandomenego,

Rampinelli

2022

J. Eng. Exact Sci.

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Há muitos debates acontecendo sobre transição energética e do desenvolvimento de novas tecnologias, a geração de energia elétrica a partir de sistemas fotovoltaicos vem se tornando cada vez mais atrativa e competitiva, sendo um dos principais agentes de transformação para a transição energética. Desta forma, a predição de geração de energia elétrica dos sistemas fotovoltaicos se torna essencial, pois contribui para a mitigação da intermitência e incerteza do recurso solar. Da mesma forma, a predição de geração de energia elétrica é importante para o planejamento e modelagem das futuras usinas fotovoltaicas. De tal modo, o objetivo geral desta dissertação foi desenvolver, modelar e validar uma metodologia para predição da geração diária de energia elétrica de sistemas fotovoltaicos a partir de histórico de operação e redes meteorológicas para os horizontes de 24, 48 e 72 horas. O período de análise foi de 5 meses, compreendido entre agosto e novembro de 2022. Os dados de previsão meteorológica foram obtidos a partir da plataforma da EPAGRI e foram divididos em cinco perfis de previsão: sol, nublado, nublado + chuva, chuva e sol + chuva. O sistema de referência do presente estudo, foi um sistema fotovoltaico de 17,6 kWp instalado no telhado de uma unidade consumidora na área rural de Tubarão (SC). Para analisar e comparar o desempenho da metodologia de predição de geração de sistemas fotovoltaicos proposta nesta dissertação foi utilizado o método da persistência como modelo de referência, além da utilização de indicadores de erros de precisão como o MAE, RMSE e MAPE. Os valores de MAE, RMSE e MAPE para o horizonte de 24 horas obteve os melhores resultados, com destaque para o mês de agosto que apresentou os valores 7,46 kWh, 10,83 kWh e 20,87% respectivamente. A metodologia apresentada se mostrou promissora e com informações relevantes para estudos posteriores.

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Estimation and forecast accuracy of regional photovoltaic power generation with upscaling method using the large monitoring data in Kyushu, Japan

Cited by 6 publications

References 4 publications

Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis

Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis

Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

Desenvolvimento de metodologia para estimativa da geração diária de energia elétrica de sistemas fotovoltaicos a partir de histórico de operação e redes meteorológicas

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