Development of Wind Speed Forecasting Model Based on the Weibull Probability Distribution

Wang, Ruigang; Li, Wenyi; Bagen, B.

doi:10.1109/cdciem.2011.333

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…3) Maximum Likelihood Estimation (mle): This method [42] estimates the parameters of a probability distribution by maximising a likelihood function so that the observed data is most probable.…”

Section: B Agents' Local Res Probabilistic Forecasting Modelsmentioning

confidence: 99%

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Shamsi

Cuffe

2022

IEEE Trans. Sustain. Energy

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This paper demonstrates how a binary prediction market is capable of achieving a probabilistic renewable energy forecast. In prediction markets, participants trade shares associated with the outcome of unknown future events (here, the renewable production, as a random variable), and the instantaneous price of shares represents the probability of the outcome. The focus of this study is to exploit this informational value of the prediction market price in renewable energy forecasting. To this end, in this paper three different methods of renewable probabilistic forecasting have been considered as the trading agents in a binary prediction market, the aggregated probability of the renewable output is elicited from the equilibrium price in this market and finally, the full cumulative distribution function of possible renewable output is extracted through regression analysis. The proposed method is applied to the test cases of three onshore wind farms in Australia. The simulation results suggest that the performance of the proposed method is superior to the individual models and forecasting is improved in terms of reduction in the electricity market imbalance costs.

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Section: B Agents' Local Res Probabilistic Forecasting Modelsmentioning

confidence: 99%

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Shamsi

Cuffe

2022

IEEE Trans. Sustain. Energy

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“…The forecast errors of REG active power can be expressed with various models, such as Gaussian distribution [22], beta distribution [23] and Weibull distribution [24]. The performance of any model is characterized by the probability density function (PDF) of the associated REG forecast errors, and the proposed multi-state models method does not depend on any particular model of REG forecast errors.…”

Section: Continuous Probabilistic Distribution Model Of Reg Active Powermentioning

confidence: 99%

Day-Ahead Active Power Scheduling in Active Distribution Network Considering Renewable Energy Generation Forecast Errors

et al. 2017

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Abstract:With large-scale integration of distributed energy resources (DERs), distribution networks have turned into active distribution networks (ADNs). However, management risks and obstacles are caused by this in due to renewable energy generation (REG) forecasting errors. In this paper, a day-ahead active power scheduling method considering REG forecast errors is proposed to eliminate the risks, minimize the costs of distribution companies and achieve optimal power flow. A hierarchical coordination optimization model based on chance constrained programming is established to realize day-ahead optimal scheduling of active power in ADNs coordinated with network reconfiguration, achieving an optimal solution of network topologies and DER outputs. The hierarchical method includes three levels: the first level provides initial values, and multiple iterations between the second and third level are used to solve the multi-period mixed integer nonlinear optimization problem. The randomness due to REG forecast errors is tackled with chance constrained programming in the scheduling procedure. The hybrid particle swarm optimization algorithm is employed to solve the proposed model. Simulation results verify the validity of the proposed method with an improved 33 nodes distribution network.

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“…Wind 2023, 3 will represent the exact wind speed variations [17,18]. The work of [19] depended on the Weibull distribution to obtain wind characteristics in the Alaçatı region, Turkey, and found that its shape and scale parameters equal 2.05 and 9.16, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The two parameters of the Weibull distribution are well-known for being straightforward and fitting the actual wind speed readings perfectly. Hence, if these two parameters are accurately predicted, the Weibull-based forecasting model will represent the exact wind speed variations [17,18]. The work of [19] depended on the Weibull distribution to obtain wind characteristics in the Alaçatı region, Turkey, and found that its shape and scale parameters equal 2.05 and 9.16, respectively.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

Younis

Elshiekh

Osama

et al. 2023

Wind

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In this quick study, we estimated the Weibull distribution’s parameters using wind data collected between March 2017 and January 2018 using a twelve-meter mast meteorological station on the grounds of the National Energy Research Center in Khartoum. In order to quantify these descriptors, we relied on analytical and stochastic methods, subsequently enabling specialists from researchers, engineers, decision-makers, and policymakers to apprehend the wind characteristics in the vicinity. Hence, the computed scale and shape parameters were provided, in which the Firefly algorithm (FA) resulted in the most accuracy in terms of the coefficient of determination, which equaled 0.999, which we considered logical due to the observed nonlinearity in the wind speed numbers. On the contrary, the energy pattern factor method had the worst prediction capability depending on several goodness-of-fit metrics. This concise work is unique because it is the first to use data from Sudan to forecast local wind speeds using artificial intelligence algorithms, particularly the FA technique, which is widely used in solar photovoltaic modeling. Additionally, since classic estimating approaches act differently spatially, evaluating their efficacy becomes innovative, which was accomplished here. On a similar note, a weighted-average wind speed was found to equal 4.98 m/s and the FA average wind speed was 3.73 m/s, while the rose diagram indicated that most winds with potential energy equivalent to 3 m/s or more blow from the north.

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Development of Wind Speed Forecasting Model Based on the Weibull Probability Distribution

Cited by 13 publications

References 17 publications

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Day-Ahead Active Power Scheduling in Active Distribution Network Considering Renewable Energy Generation Forecast Errors

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

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