An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

Wang, Huan; Huang, Jikun; Zhou, Han; Zhao, Lixue; Yuan, Yanbin

doi:10.3390/su11154018

Cited by 30 publications

(19 citation statements)

References 38 publications

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“…There is a large plethora of literature available on the issue of energy consumption forecasting. Many studies used methods for forecasting energy consumption, e.g., [5][6][7][8][9][10][11][12][13][14][15], and some studies were forecasted by comparing the approach with some other methods. On the other hand, some studies used the grey methods for energy consumption forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are very few studies that evaluated energy consumption for BRICS by using the model. Some studies forecasted energy consumption by using the forecasting method like [5][6][7][8][9][10]12] for China, [13] for Brazil, and [15] for South Africa. On the other hand, some studies used the grey Markov method with rolling mechanism and singular spectrum analysis for energy consumption forecasting like [16] for India.…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Khan¹,

Osińska²

2021

Preprint

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Khan¹,

Osińska²

2021

Preprint

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“…ARIMA models are the integration of Auto-regressive (AR) models and Moving Average models. ARIMA models are good for forecasting stationary time-series data [31]. Input sets are either DA or DM.…”

Section: Clustered Arima Forecastingmentioning

confidence: 99%

Cluster-Based Prediction for Batteries in Data Centers

Haider

Zhao

2020

Energies

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Prediction of a battery's health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery's degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery's health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology.

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“…ARIMA models are the integration of Autoregressive models (AR) and Moving Average models (MA). ARIMA models give good accuracy in forecasting relatively stationary time-series data [25]. DA or DM either can be input set.…”

Section: Clustered Arima Forecastingmentioning

confidence: 99%

Clustered Based Prediction for Batteries in the Data Centers

Haider¹,

Zhao²,

Li³

2020

Preprint

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This paper proposes an ARIMA approach to battery health forecasting with accuracy improvement by K shape-based clustered predictors. The health prediction of the battery pack is an important function of a battery management system in data centers. Accurate forecasting of battery life turns out to be very difficult without failure data to train a good forecasting model in real life. The conventional ARIMA model is compared with total and clustered predictors for battery health forecasting. Results show that the forecasting accuracy of the ARIMA model significantly improved by utilizing the results of the clustered predictors for 40 batteries in a real data center. One year of actual historical data of 40 batteries of large scale datacenter is presented to validate the effectiveness of the proposed methodology.

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An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

Cited by 30 publications

References 38 publications

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Cluster-Based Prediction for Batteries in Data Centers

Clustered Based Prediction for Batteries in the Data Centers

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