Simulation of the energy efficiency auction prices in Brazil

Conzales, Javier L. L.

doi:10.24084/repqj14.396

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…First, the technique is developed to analyze environmental pollution data based on univariate time series from various sources. Further studies are required for its validation in other contexts (for example, in data related to air quality [59][60][61], solid waste [62], also in academic performance data [63], data related to digital marketing [64] or those based on energy efficiency [65,66]). One of the methodology's significant limitations is that it does not preserve the time series structure since it assumes an auto-regressive model with a predefined lag size.…”

Section: Discussionmentioning

confidence: 99%

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

López-Gonzales,

Gómez Lamus,

Torres

et al. 2023

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Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM2.5.

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Section: Discussionmentioning

confidence: 99%

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

López-Gonzales,

Gómez Lamus,

Torres

et al. 2023

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“…They can also be considered in the current hybrid time series forecasting framework. It can also be extended and applied to other approaches and datasets (for example, energy [42][43][44], air pollution [45,46], solid waste [47], and academic performance [48]).…”

Section: Discussionmentioning

confidence: 99%

Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models

Carbo-Bustinza,

Iftikhar,

Belmonte

et al. 2023

Applied Sciences

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In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.

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“…Also, for the estimation model, we assign numbers to each model, e.g., parametric autoregressive (1), nonparametric autoregressive (2), autoregressive moving average (3), and vector autoregressive (4). For example, 1 RSD 3 2 , indicates that the long run (t) by an autoregressive seasonal series (s) is estimated using the nonparametric autoregressive model, and the residual (r) is estimated using the autoregressive moving average model. To get the final day-ahead price forecast, the individual forecast models are linked directly to each other as follows:…”

Section: Autoregressive Moving Average Modelmentioning

confidence: 99%

“…Machine learning models such as deep learning and artificial neural networks can also be considered part of the current forecasting decompositioncombination technique. It can also be extended and applied to other approaches and datasets (for example, energy [1,3], air pollution [67][68][69][70], solid waste [71] and academic performance [72]).…”

Section: Conclusion and Future Work Directionsmentioning

confidence: 99%

“…For example, spot electricity prices exhibit long-term trends (linear or nonlinear), seasonality (daily, weekly, seasonal, and yearly), calendar effects, extreme volatility, and outliers (jumps or spikes). Because of these specific characteristics, the electricity price forecast is more challenging from the three forecast perspectives: the short-term, the medium-term, and the long-term [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

Iftikhar,

Turpo-Chaparro,

Canas Rodrigues

et al. 2023

Energies

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Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.

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Simulation of the energy efficiency auction prices in Brazil

Cited by 7 publications

References 2 publications

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models

Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

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