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

Silva, Felipe Leite Coelho da; Costa, Kleyton da; Rodrigues, Paulo Canas; Salas, Rodrigo; López-Gonzales, Javier Linkolk

doi:10.3390/en15020588

Cited by 40 publications

(20 citation statements)

References 35 publications

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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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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

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“…Результати експерименту свідчать, що нейронні мережі MLP ефективніші за нейронні мережі RBF. Науковці в роботі [5] порівняли два класи моделей для прогнозування часових рядів (статистичні та штучні нейронні мережами), які використовуються для аналізу споживання електроенергії в промисловості Бразилії. Було показано, що модель MLP має найкращі показники прогнозування споживання електроенергії.…”

Section: Application Of Artificial Neural Network Based On Perceptron...unclassified

Застосування Штучних Нейронних Мереж На Основі Персептрону В Електроенергетичних Системах

Єгоров,

Шкварницька,

Яреміч

2024

НТС

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“…There are also works on industry consumption forecasting. Leite Coelho da Silva et al [26] applied statistical approaches and ANN to industrial electricity consumption in the Brazilian system.…”

Section: Electricity Consumption Forecastingmentioning

confidence: 99%

Entropy-Based Anomaly Detection in Household Electricity Consumption

2022

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Energy efficiency is one of the most important current challenges, and its impact at a global level is considerable. To solve current challenges, it is critical that consumers are able to control their energy consumption. In this paper, we propose using a time series of window-based entropy to detect anomalies in the electricity consumption of a household when the pattern of consumption behavior exhibits a change. We compare the accuracy of this approach with two machine learning approaches, random forest and neural networks, and with a statistical approach, the ARIMA model. We study whether these approaches detect the same anomalous periods. These different techniques have been evaluated using a real dataset obtained from different households with different consumption profiles from the Madrid Region. The entropy-based algorithm detects more days classified as anomalous according to context information compared to the other algorithms. This approach has the advantages that it does not require a training period and that it adapts dynamically to changes, except in vacation periods when consumption drops drastically and requires some time for adapting to the new situation.

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

Cited by 40 publications

References 35 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

Застосування Штучних Нейронних Мереж На Основі Персептрону В Електроенергетичних Системах

Entropy-Based Anomaly Detection in Household Electricity Consumption

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