Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods

Meira, Erick; Oliveira, Fernando Luiz Cyrino

doi:10.1016/j.energy.2017.12.049

Cited by 327 publications

(130 citation statements)

References 50 publications

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“…To establish prediction on publications, citations and altmetrics growth (with particular interest on OA) we address the proble as a one one of time series prediction. To do so, we need adequate tools to analyze historical data and thus, making predictions Hassan (2014); de Oliveira and Oliveira (2018). There are several types of models that can be used for time-series forecasting Siami-Namini et al (2018).…”

Section: Methodsmentioning

confidence: 99%

Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature

Torres‐Salinas¹,

Robinson-García

Valdivieso

2020

Preprint

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We present an analysis on the uptake of open access on COVID-8 19 related literature as well as the social media attention they gather when 9 compared with non OA papers. We use a dataset of publications curated by 10 Dimensions and analyze articles and preprints. Our sample includes 11,686 11 publications of which 67.5% are openly accessible. OA publications tend to re-12 ceive the largest share of social media attention as measured by the Altmetric 13Attention Score. 37.6% of OA publications are bronze, which means toll jour-14 nals are providing free access. MedRxiv contributes to 36.3% of documents in 15 repositories but papers in BiorXiv exhibit on average higher AAS. We predict 16 the growth of COVID-19 literature in the following 30 days estimating ARIMA 17 models for the overall publications set, OA vs. non OA and by location of the 18 document (repository vs. journal). We estimate that COVID-19 publications 19 will double in the next 20 days, but non OA publications will grow at a higher 20 rate than OA publications. We conclude by discussing the implications of such 21 findings on the dissemination and communication of research findings to mit-22 igate the coronavirus outbreak.23

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Section: Methodsmentioning

confidence: 99%

Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature

Torres‐Salinas¹,

Robinson-García

Valdivieso

2020

Preprint

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“…Then, R is always greater than or equal to R A [25]. The bagging model can be applied not only in PV forecasting but also in various fields [26][27][28][29].…”

Section: Of 16mentioning

confidence: 99%

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Choi¹,

Hur

2020

Energies

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As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

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“…Hossain et al [34] used artificial neural network models to simultaneously predict new solar and wind energy and applies them to the climate of Queensland. In the application of the ARIMA model, Oliveira et al [35] used the bagging ARIMA model to predict medium-and long-term power consumption. Wang et al [36] applied hybrid ARIMA and the metabolic grey technique to forecast shale gas output in the United States.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

Yang

2019

Sustainability

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India's coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India's coal consumption during 1995-2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018-2030.

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Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods

Cited by 327 publications

References 50 publications

Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature

Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

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