Markov chain modeling for very-short-term wind power forecasting

Carpinone, A.; Giorgio, Massimiliano; Langella, R.; Testa, A.

doi:10.1016/j.epsr.2014.12.025

Cited by 137 publications

(85 citation statements)

References 27 publications

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“…With the increasing number of wind farms and the installed capacity, the wind power volatility represents a huge challenge to economic security and increases the difficulty of the operation and management of power network dispatching companies once incorporated into the power grid. Accurate prediction of the wind power in advance can relieve the pressure of peaking power systems, which can effectively improve the ability to include wind power in the grid [4].…”

Section: Introductionmentioning

confidence: 99%

“…This relationship can be expressed in function form, such as Markov chains [4], regression analysis [9,10], exponential smoothing method [11], Kalman filtering method [12], ARMA model [13] and so on. Among them, the ARMA (p, q) model, used as the common statistical model, has high accuracy of analysis and prediction for stationary time series, however, due to the impact of the uncertain natural climate, wind energy has obvious trends, diversity and periodicity, which show non-stationary time series.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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Abstract:The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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“…This information can also be used to obtain a deterministic forecast of future water demand q for (t + k∆t) at a generic time t + k∆t in the following manner [28].…”

Section: Demand Forecasting Modelmentioning

confidence: 99%

A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Gagliardi

Alvisi

Kapelan

et al. 2017

Water

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This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods), were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

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“…Generally, the past literature has adopted traditional methods such as linear regression [17], expert systems [18], neural networks [19,20], and Markov prediction and factor analysis [21] for yield prediction. However, the biofuels system is a complex system, which is easily affected by various factors such as the economy, resources, and social issues.…”

Section: Problems With Predicting Productionmentioning

confidence: 99%

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

et al. 2015

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Biofuel production in China suffers from many uncertainties due to concerns about the government's support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel's characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey-Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey-Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.

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Markov chain modeling for very-short-term wind power forecasting

Cited by 137 publications

References 27 publications

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

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