Interrelationship simulations among Brazilian electric matrix sources

Reichert, Bianca; Souza, Adriano Mendonça

doi:10.1016/j.epsr.2020.107019

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…This more stable behavior could be justified by government incentives and private investments in renewable area, such as PROINFA; in addition to the worldwide movement to make the energy matrix more sustainable (ANEEL, 2017;Aquila et al, 2017;Maciel et al, 2018;Medina, 2020). Yet, much more government efforts are needed to expand renewable generation in the country, as well as to maintain the electrical system stability, either by hybrid generation or by energy storage devices (Noronha et al, 2019;Reichert & Souza, 2021).…”

Section: Discussionmentioning

confidence: 99%

Forecasting electricity generation from renewable sources during a pandemic

2022

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Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.

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Section: Discussionmentioning

confidence: 99%

Forecasting electricity generation from renewable sources during a pandemic

2022

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“…Based on these studies results, the possibility of using pricing models to predict energy generation is now considered; because, with the increase of renewable generation, the energy supply becomes more unstable and difficult to predict and control (Park and Baldick, 2020;Reichert and Souza, 2021). For this reason, the aim of this study was to verify whether the Heston model can be used to predict energy generation, through a systematic literature review.…”

Section: Introductionmentioning

confidence: 99%

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Reichert

Souza

2022

IJEEP

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The ability to predict the price of stock exchange assets has attracted the attention of economists and physicists around the world, as physical models are useful to predict volatility behaviors. Knowing that volatility is crucial for energy sector planning, the research aim was to investigate whether the Heston pricing model is useful to predict energy generation, trough the steps established by the systematic review protocol. In a corpus of 25 documents, it was possible to identify: lots of financial studies, energy and demography researches; a low level of interaction among universities; the largest number of publications from Australia and China; the most important journal; and the advantages of applying Econophysics models to solve volatility problems. In conclusion, the Heston model can be applied to predict energy generation, since it is a closed-form model and capable of modeling the stochastic volatility, reversing it to the predicted value of average energy generation.

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“…Hydroelectric power is the primary generator of electric energy in Brazil. However, thermal energy is still needed to supply domestic demand in periods of prolonged drought [9][10][11][12]. Therefore, the challenge is to increase renewable energy production to supply the growing energy demand due to population growth and new technologies.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

et al. 2021

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Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach.

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Interrelationship simulations among Brazilian electric matrix sources

Cited by 6 publications

References 28 publications

Forecasting electricity generation from renewable sources during a pandemic

Forecasting electricity generation from renewable sources during a pandemic

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

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