Estimation of Transfer Entropy between Discrete and Continuous Random Processes

Assis, Juliana Martins de; Assis, Francisco Marcos de

doi:10.14209/jcis.2018.1

Cited by 2 publications

(1 citation statement)

References 24 publications

(33 reference statements)

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“…The allocation of data points to equally-spaced bins is less time consuming than other methods to estimate TE as the Nearest Neighbours method but has the drawback of detecting more false positives than the latter (Assis and de Assis 2018). In this paper we employ a q = 3-quantile binning, partitioning the data into three bins through the 5% and 95% empirical quantiles of the data distribution as suggested by Behrendt et al (2019); Dimpfl and Peter (2018).…”

Section: Entropy Estimationmentioning

confidence: 99%

The impact of Clean Spark Spread expectations on storage hydropower generation

Condemi

Mastroeni

Vellucci

2021

Decisions Econ Finan

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Storage hydropower generation plays a crucial role in the electric power system and energy transition because it is the most widespread power generation with low greenhouse gas emissions and, moreover, it is relatively cheap to ramp up and down. As a result, it provides flexibility to the grid and helps mitigate the short-term production uncertainty that affects most green energy technologies. However, using water in reservoirs represents an opportunity cost, which is related to the evolution of plant production capacity and production profitability. As the latter is related to a wide range of types of variables, in order to incorporate it in a large-scale prediction model it is important to select the variables that impact most on storage hydropower generation. In this paper, we investigate the impact of the variables influencing the choices of price maker producers, and, in particular we study the impact of Clean Spark Spread expectations on storage hydroelectric generation. In this connection, using entropy and machine learning tools, we present a method for embedding this expectations in a model to predict storage hydropower generation, showing that, for some time horizon, expectations on CSS have a greater impact than expectations on power prices. It is shown that, if the right mix of power price and CSS expectations is considered, the prediction error of the model is drastically reduced. This implies that it is important to incorporate CSS expectations into the storage hydropower model.

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Section: Entropy Estimationmentioning

confidence: 99%