Solar radiation synthetic sequences and bidding strategies for energy auctions

Gemignani, Matheus Mingatos Fernandes; Rostegui, Guido; Kagan, Nelson

doi:10.1109/icrera.2017.8191094

Cited by 2 publications

(2 citation statements)

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“…Autoregressive (AR) models can be used in time series modeling, where the current value depends on past values plus a constant and a random error (white noise). You can build the AR model from the following equation [13], [14], [15]:…”

Section: B Stochastic Modelmentioning

confidence: 99%

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Synthetic Series of Electricity Generation through a Photovoltaic System by using Different Panel Temperature Models

Stah¹,

Neto²,

Vidal³

et al. 2021

REPQJ

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The electric energy produced by photovoltaic systems is a function of several variables that present random behavior, such as solar irradiance, ambient temperature, wind speed, and relative humidity. Several models describe the energy produced by these systems, which reflect the various factors that influence the generation and how significant these influences are. In this way, the present work aims to compare some panel temperature models and the electrical energy produced from a photovoltaic system located in the Midwest of Brazil. The applied methodology aims to evaluate the expected averages and standard deviations of the monthly synthetic series of generated energy to identify the main random variables that influence the electric generation. The results indicate the model that best describes the physical system concerning the randomness of the variables involved, the photovoltaic technology, the location, and the measurement period.

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Section: B Stochastic Modelmentioning

confidence: 99%

“…To validate the model with the historical series, the Mean Relative Percentage Error (MPE) was used, which indicates the mean absolute error quotient and the historical value, weighting the individual errors by their representativeness concerning the actual value (the monthly historical data) [13], [20].…”

Section: Model Validationmentioning

confidence: 99%