An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries

Safarinejadian, Behrouz; Gharibzadeh, Masihollah; Rakhshan, Mohsen

doi:10.1080/21642583.2014.970733

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…The TLBO is algorithm having the two phases based on teaching-learning procedure. Advantages of the TLBO algorithm are simplicity, low computational complexity, high searching power to find the global optimum, and lack of tuning parameters, except for the initial population [36].…”

Section: Tlbo Algorithmmentioning

confidence: 99%

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

Kankal

Uzlu

2016

Neural Comput & Applic

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This paper studies the performance of an artificial neural network (ANN) with teaching-learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey's EED. The results of the proposed model give excellent clues with regards to its use in future energy studies.

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Section: Tlbo Algorithmmentioning

confidence: 99%

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

Kankal

Uzlu

2016

Neural Comput & Applic

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“…with respect to (1)- (4). We assume that the ISO is not aware of the start up cost, no-load cost and minimum up and down time of the generators.…”

Section: Day-ahead Marketmentioning

confidence: 99%

Pricing Energy in the Presence of Renewables

Zeinalzadeh

Ghavidel

Gupta

2018

2018 Annual American Control Conference (ACC)

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At present, electricity markets largely ignore the fact that renewable power producers impose significant externalities on non-renewable energy producers. This is because consumers are generally guaranteed electricity within certain load parameters. The intermittent nature of production by renewable energy producers implies that they rely on non-renewable producers so that the aggregate power delivered meets the promised quality of service. This implicit insurance provided by the non-renewable power sector to consumers is not currently priced and leads to an often ignored, hidden monetary transfer from non-renewable producers to renewable producers. As the fraction of energy supplied by renewable resources increases, these externalities also increase. In this paper, we quantify these externalities by developing the market clearing price of energy in the presence of renewable energy. We consider a day-ahead electricity market where renewable and non-renewable generators bid by proposing their asking price per unit of energy to an independent system operator (ISO). The ISO's problem is a multi-stage stochastic optimization problem to dispatch energy from each generator to minimize the cost of purchased energy on behalf of the consumers. We incorporate the notion of load variance using the Conditional Value-at-Risk (CVAR) measure in the day-ahead electricity market to ensure that the generators are able to meet the load within a desired confidence level. We analytically derive the market clearing price of energy as a function of CVAR. It is shown that a higher penetration level of the renewable energies may increase the market clearing price of energy.

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“…Fuzzy regression models are suitable to address such uncertainties, however, these models do not map the function with nonlinear behavior. A number of studies have used fuzzy time series to forecast energy prices in Australia and Singapore electricity markets [29], and Ontario and New England markets [30]; however, recent studies indicate that hybrid models of ANN and fuzzy systems outperform standalone fuzzy regression models [25], [31], [32]. These models of ANNs and fuzzy systems correspond to a fuzzy model of Takagi-Sugeno, wherein the weights of the neural network model are similar to the parameters of the fuzzy system, and hence, they behave as black box models.…”

Section: Introductionmentioning

confidence: 99%

A New ANN Approach for Time Series Analysis

2022

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Time series analysis and accurate forecasting of energy prices are critical for both policymakers and market participants. In the practical analysis of price time series, the coefficients play vital roles; however, their accurate estimation is a challenging issue as they are affected by external factors. This study proposes a new modeling approach for Artificial Neural Networks (ANNs) models based on fuzzy logic. For this purpose, we reformulated an ANN model as a fuzzy nonlinear regression model to capture the advantages of both fuzzy regression and ANN methodologies. This clear-box model can be applied to not only uncertain, ambiguous, and complex environments, but it is also capable of modeling nonlinear patterns. To illustrate the capability of the proposed approach, we report a case study of liquefied natural gas (LNG) prices in Japan's market (the world's largest natural gas importer). The results support that the performance of the proposed approach is acceptable; moreover, it can deal with uncertain and complex environments as a clear-box model.

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An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries

Cited by 9 publications

References 28 publications

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

Pricing Energy in the Presence of Renewables

A New ANN Approach for Time Series Analysis

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