Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil

Alencar, David Barbosa de; Affonso, Carolina M.; Oliveira, Roberto Célio Limão de; Filho, José Carlos Reston

doi:10.1109/access.2018.2872720

Cited by 52 publications

(18 citation statements)

References 20 publications

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“…A new SLA-Aware Risk Management Framework (SA-RMF) is proposed in cloud here for load balancing. The main contribution is: The SLOs' output on runtime is seized and predicted by adapting an efficient hybrid approach (Alencar et al 2018) and a dynamic threshold concept is proposed in this framework, and the service provider recommends steps necessary to actively reduce the SLA violations risks. Therefore, the remaining of the work is designed accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…The fuzzy inference system (FIS) is used in this method which explores inputs that are the expected trajectory of consumer behavior, danger mentality of the supplier as well as reliability of a service. Alencar et al (2018) proposed an S along with seasonal autoregressive integrated moving average (SARIMA) based hybrid approach to predict multi-step ahead wind speed with the help of descriptive values. There, the explanatory variables are first estimated and wind speed prediction achieved.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Gupta¹,

Bhadauria²,

Singh³

2020

J Ambient Intell Human Comput

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In cloud computing environment, load balancing is an important task. So many of the researchers had focused on load balanced scheduling technique. Those provides better load balancing in cloud but there are some issues like resource allocation and cost maintenance. One of the major issue in load balancing techniques is service level agreement (SLA) management because many of them are affected by this SLA-violation. Many researchers have proposed various risk based framework but few of them has guides the service provider to take steps for SLA violation abatement and they also need some improvements. To tackle this problem, a new SLA-aware risk management framework (SA-RMF) is proposed in this work for efficient load balancing in cloud. A new technique is presented here based on CPU parameter for generating efficient dynamic threshold. A better quality of service values prediction is achieved by using hybrid approach in SA-RMF. Through experiments, the appropriateness of proposed system is demonstrated and validated that it helps cloud providers to mitigate future violations of services and consequences.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Gupta¹,

Bhadauria²,

Singh³

2020

J Ambient Intell Human Comput

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“…In that paper, the ELM was optimized using two bioinspired algorithms: standard genetic algorithm and cuckoo search. Alencar et al [73] proposed a hybrid approach com-bining SARIMA and MLP for multi-step forecasting in two cities of Brazil, Petrolina and Macau. Ferreira, Santos, and Lucio [76] combined Holt-Winters model (HW) with MLP to forecast the mean hourly wind speed at two locations of Brazil: Camocim (coastal region) and Belo Jardim (complex topography).…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

et al. 2020

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Wind speed is one of the primary renewable sources for clean power. However, it is intermittent, presents nonlinear patterns, and has nonstationary behavior. Thus, the development of accurate approaches for its forecasting is a challenge in wind power generation engineering. Hybrid systems that combine linear statistical and Artificial Intelligence (AI) forecasters have been highlighted in the literature due to their accuracy. Those systems aim to overcome the limitations of the single linear and AI models. In the literature about wind speed, these hybrid systems combine linear and nonlinear forecasts using a simple sum. However, the most suitable function for combining linear and nonlinear forecasts is unknown and the linear relationship assumption can degenerate or underestimate the performance of the whole system. Thus, properly combining the forecasts of linear and nonlinear models is an open question and its determination is a challenge. This paper proposes a hybrid system for monthly wind speed forecasting that uses a nonlinear combination of the linear and nonlinear models. A data-driven intelligent model is used to search for the most suitable combination, aiming to maximize the performance of the system. An evaluation has been carried out using the monthly data from three wind speed stations in northeast Brazil, evaluated with two traditional metrics. The assessment is performed for two scenarios: with and without exogenous variables. The results show that the proposed hybrid system attains an accuracy superior to other hybrid systems and single linear and AI models.

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“…Hybrid models [27] are a combination of physical and statistical models or a combination of two models with different time horizons from the same type or different types. The forecast error increases as the time horizon of the forecast increases.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system ⎯namely, teaching-learningbased optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system. INDEX TERMS Generation gas emission, generation operating cost, optimal power flow, symbiotic organisms search algorithm, valve-point effects, wind speed forecast.

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Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil

Cited by 52 publications

References 20 publications

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

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