Trading strategies modeling in Colombian power market using artificial intelligence techniques

Moreno, Julián

doi:10.1016/j.enpol.2008.10.033

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…Alguns autores postulam que a IA pode substituir a inteligência humana em atividades repetitivas ou apoiar a tomada de decisão (Manita et al, 2020). A IA tem sido usada na análise e implementação de várias iniciativas de energia renovável (Jha et al, 2017), para calcular o risco de um portfólio de produtos (Goli et al, 2020), para analisar ações de comercialização nos mercados de energia (Moreno, 2009) e para alavancar o uso de tecnologias na manufatura (Gershwin, 2018). A Inteligência Artificial (IA) pode simular comportamentos, de forma a auxiliar na análise e resolução de situações complexas (Syam;Sharma, 2018;Fitzsimmons;Hong;Brilakis, 2022).…”

Section: Inteligência Artificial E Machine Learningunclassified

Proposta de modelo de planejamento, projeto e execução para aprimorar a construção civil com apoio das tecnologias digitais

Hartz,

Borchardt,

Jung

2024

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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As falhas nos controles de custos e prazos na construção civil podem ocasionar atrasos na entrega de obras, gastos excedentes, além da insatisfação do cliente e até mesmo prejuízos financeiros incalculáveis. Parte destes problemas se dá a falta de dados históricos, falta de inserção de tecnologia nos processos, muitas vezes pela dificuldade de aceitação e adaptação de novas tecnologias e processo. O presente trabalho investigou as lacunas existentes para que o avanço da chamada Construção 4.0. A pesquisa utilizou o método de estudo qualitativo e investigatório, ocorrendo através de estudo teórico, pesquisa com profissionais da área, além de considerar o conhecimento prático do pesquisador. Os resultados contribuíram para que fosse possível identificar os modelos utilizados por outras construtoras, identificar as necessidades atuais, além de identificar as tecnologias digitais que podem ser inseridas nos processos a fim de resultar na proposta de um modelo de planejamento, projeto e execução para aprimorar a construção civil com o apoio das tecnologias digitais.

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Section: Inteligência Artificial E Machine Learningunclassified

Proposta de modelo de planejamento, projeto e execução para aprimorar a construção civil com apoio das tecnologias digitais

Hartz,

Borchardt,

Jung

2024

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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“…There are very few applications of machine learning techniques for trading in electricity markets described in the international literature [33]. An application for the Colombian electricity market is described in [34], in which a combination of a fuzzy inference system and learning algorithm provides information about the amount of electricity to buy through bilateral contracts, according to the agent's profile, in order to maximize the profit, but less than a month in advance. For the MIBEL market Pinto et al [35] present a decision support scheme, based on a spot price forecasting model, to optimize agents' profits.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Trading Strategy for Physical Electricity Futures

2020

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This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.

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“…On the other hand, nonlinear machine learning have been proven useful for a variety of areas such as classification, regression and curve fitting. Machine learning have been used in economics and econometrics for various investigations such as predicting energy prices [16][17][18], forecasting energy consumption [19][20][21], trading strategies [22][23][24] and policy analysis [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Network for Modelling the Relationship of the Real Gdp, Co2 Emission and Renewable Energy Consumption for Turkiye

Tuncsiper

2023

CJMS

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In this study, the relationship between Türkiye’s real gross domestic product, CO2 emission and renewable energy consumption is modelled using machine learning techniques. The data between 2003-2020, the period when renewable energy production and consumption have accelerated, are included in the modelling. First of all, it was tested whether there is a causal relationship between the real gross domestic product, CO2 emission and renewable energy consumption data. Afterwards, a deep learning model was developed in Python programming language by considering the real gross domestic product as the dependent variable while CO2 emission and renewable energy consumption are considered as independent variables. The developed deep learning model has two input nodes, three hidden layers consisting of 100 neurons each, and an output node. In addition, the rectified unit functions are used as nonlinear activation functions in the deep learning network. Based on the standard usage, seventy percent of the data was used as the training data and the remaining thirty percent were employed as the test data. The results of the developed deep learning network and actual gross domestic product data were compared, and it is shown that the developed deep learning network successfully models the relationship between the real gross domestic product, CO2 emission and the renewable energy consumption. The coefficient of determination of the developed model was calculated in Python environment as R2=0.986. This parameter value also indicates that the developed deep learning network model has a good performance for the modelling of the real gross domestic product dependent on the CO2 emission and renewable energy consumption.

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Trading strategies modeling in Colombian power market using artificial intelligence techniques

Cited by 14 publications

References 7 publications

Proposta de modelo de planejamento, projeto e execução para aprimorar a construção civil com apoio das tecnologias digitais

Proposta de modelo de planejamento, projeto e execução para aprimorar a construção civil com apoio das tecnologias digitais

Predictive Trading Strategy for Physical Electricity Futures

A Deep Learning Network for Modelling the Relationship of the Real Gdp, Co2 Emission and Renewable Energy Consumption for Turkiye

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