Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

López-Gonzales, Javier Linkolk; Souza, Reinaldo Castro; Silva, Felipe Leite Coelho da; Carbo-Bustinza, Natalí; Ibacache-Pulgar, Germán; Calili, Rodrigo Flora

doi:10.3390/en13174544

Cited by 11 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In recent years, projections of electricity consumption for the Brazilian industrial sector have been studied, both for short and long term [1]. This interest is related to the development of the sector, energy planning, and energy efficiency [2]. Furthermore, electricity has economic and social importance for a country or region.…”

Section: Introductionmentioning

confidence: 99%

Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

et al. 2022

Self Cite

View full text Add to dashboard Cite

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

show abstract

Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

Cited by 11 publications

References 40 publications

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

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

Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Contact Info

Product

Resources

About