ARIMA vs. Neural networks for wind speed forecasting

Palomares-Salas, José Carlos; Rosa, Juan José González de la; Ramiro, J. G.; Melgar, J.; Aguera, A.; Moreno, Antonio

doi:10.1109/cimsa.2009.5069932

Cited by 57 publications

(21 citation statements)

References 4 publications

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“…The information related to renewable energy generation consists of both linear as well as non-linear data. The ARIMA model which can handle both linear and non-linear data, was used for stationary time series by Box [13], for enterovirus time series analysis by Shih [14], and wind speed forecasting [15]. Thus, the ARIMA model will also be used in this work.…”

Section: Related Workmentioning

confidence: 99%

Model Predictive Optimization for distribution management in smart grids

Chao

Hsieh

Yang

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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The traditional centralized power system is gradually being replaced by smart grids. However, an important design issue is how to perform accurate demand-response such that the power distribution management is effective. This includes two sub-problems, namely the accurate prediction of future electricity demand-response situations and the optimization of power distribution. In this work, we propose a novel Model Predictive Optimization (MPO) method for the advanced distribution management system in smart grids. Future electricity situations (surplus/deficit) are predicted using a customized Autoregressive Integrated Moving Average (ARIMA) model. Pairing between buyers and sellers of electricity are performed based on not only the current situation, but also considering future situations. As a result, trading pairs with overall near-optimal cost are found through concurrent and multiple instances of Particle Swarm Optimization (PSO), along with conflict resolution. Experimental results on 30 micro-grids show the error rate of the ARIMA prediction model to be less than 10%. The proposed MPO method saves totally 19.38% overall trading cost, if predictions are made for 4 future time slots.

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Section: Related Workmentioning

confidence: 99%

Model Predictive Optimization for distribution management in smart grids

Chao

Hsieh

Yang

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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“…Motivated by [6] and [11], for wind speed forecasting a ARIMA model was chosen. The parameters were selected, using the Akaike information criterion (AIC) [11] to evaluate the quality of different models, trained with a dataset of 5000 samples of 30 min mean values from the Azores Island Graciosa, provided by [14].…”

Section: A Wind Forecastmentioning

confidence: 99%

“…3. As reference a ARIMA(2, 0, 0) model, as proposed by [6], as well as a naïve forecast withŵ k+j|k = w k were chosen. With mean PRMSE 24 = 0.035 pu, (standard deviation: 0.040 pu), the mean PRMSE 24 of the ARIMA(χ d , 0, χ q ) model is about 13 % smaller and the standard deviation about 22 % less than with the naïve method.…”

Section: A Wind Forecastmentioning

confidence: 99%

Scenario-based model predictive operation control of islanded microgrids

Hans

Sopasakis

Bemporad

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We propose a model predictive control (MPC) approach for the operation of islanded microgrids that takes into account the stochasticity of wind and load forecasts. In comparison to worst case approaches, the probability distribution of the prediction is used to optimize the operation of the microgrid, leading to less conservative solutions. Suitable models for time series forecast are derived and employed to create scenarios. These scenarios and the system measurements are used as inputs for a stochastic MPC, wherein a mixedinteger problem is solved to derive the optimal controls. In the provided case study, the stochastic MPC yields an increase of wind power generation and decrease of conventional generation.

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“…However, it becomes a major issue for large penetration levels as larger quantities of reserves can be difficult to maintain. As a result, forecasting of wind speeds and associated power outputs has received a lot of attention by researchers [1][2][3][4][5][6][7][8][9].This covers both short term (up to 3 hours) and long term forecasting. Statistical methods are often used in short term forecasting [5,7] while numerical weather prediction methods [8,9] are more effective for long term forecasting.…”

Section: Introductionmentioning

confidence: 99%

Comparison of individual and combined forecasting of wind power output of two wind farms in Western Australia

Banerjee

Jayaweera

Islam

2012

2012 IEEE Power Electronics and Machines in Wind Applications

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Large penetration of wind farms in the electricity grid is expected to be an unknown challenge in the near future. The biggest factor is the intermittency of wind turbine output and the difficulty in forecasting it accurately. A number of studies on wind forecasting have shown that the error of statistical methods increases with forecast horizon. However, combined forecasting of geographically dispersed wind farm output, sees a reduction in error. This paper extends this further by analysis of the confidence interval and uncertainty of forecasts when combined forecasting is used. It is observed that both the forecast error as well as uncertainty is reduced when combined forecasting is used for two wind farms in Western Australia. For a short term forecasting horizon of 6 hours, maximum forecasting error is reduced by 6% while uncertainty is reduced by up to 20%. This is expected to improve if more wind farms are considered.Index Terms-combined wind forecasting, geographical dispersion, Wind power forecasting.

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ARIMA vs. Neural networks for wind speed forecasting

Cited by 57 publications

References 4 publications

Model Predictive Optimization for distribution management in smart grids

Model Predictive Optimization for distribution management in smart grids

Scenario-based model predictive operation control of islanded microgrids

Comparison of individual and combined forecasting of wind power output of two wind farms in Western Australia

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