Linear and non-linear autoregressive models for short-term wind speed forecasting

Kumar, Santosh; Selvakumar, A. Immanuel; Lydia, M.

doi:10.1016/j.enconman.2016.01.007

Cited by 235 publications

(76 citation statements)

References 23 publications

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“…The optimal penalty factor and kernel function variance of SVR are determined using the cross-validation method. The characteristics of ARIMA are set according to References [5,12]. The experimental results of the long interval are shown in Figure 15 and Table 6.…”

Section: Comparison Experiments and Discussionmentioning

confidence: 99%

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Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

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Section: Comparison Experiments and Discussionmentioning

confidence: 99%

“…It helps to improve power quality and to maintain the reliability and stability of power grids. The short-term wind speed forecasting model can be divided into three types: statistical, physical, and intelligent models [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

et al. 2016

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“…The first one adopts stochastic processes, that are described through a stochastic differential equation or through a probability distribution [10], [11], [12], [13]. The second one studies the dataset of wind speeds through linear time series models [14], [15], [16]. The third technique is the spatially correlated technique and it consists in a comparison of data among sites [17].…”

Section: B Wind Speedsmentioning

confidence: 99%

Cash flow at risk of offshore wind plants

Vaienti

Ιωάννου

2017

2017 6th International Conference on Clean Electrical Power (ICCEP)

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Abstract-Offshore wind power plants might be seen as high risk investments. Their risk depends on technical and financial elements. When some corporations decide to invest in a plant, they decide to take all above-mentioned risks. The question "Given a specific investor, a specific plant, etc., how big are the investment risks?" has not a clear answer. In fact, the impact of the previous risk factors on cash flows is not completely quantified, mainly because all the risks are related, but the dependency structure is difficult to be modelled. Hence, it is important to have a measure of the impact of the risks into the cash flows. Due to the lack of knowledge in this quantification, we have decided to investigate it more in the detail. The paper aims to measure the variability of cash flows and how effective are the strategies for locking electricity prices, ship freight rates, or both in the reduction of this variability. We adopt the Monte Carlo approach for simulating all the possible cash flows and for measuring all the uncertainties. The output shows that seasonal and uncertain cash flows. The strategies, for reducing the probability of negative cash flows, work only with locked electricity prices.Keywords-cash flow at risk; offshore wind energy.

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“…The effect of the wind energy assessment directly depends on the accuracy of the wind speed forecasting. Many techniques have recently been proposed to forecast the wind speed, and the related techniques can usually be divided into the following three categories: short-term wind speed forecasting [8][9][10], medium-term wind speed forecasting [11] and long-term wind speed forecasting. One of the most popular skills used for wind speed forecasting is to construct a hybrid model based on several single forecasting approaches.…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms

Wang

2016

Sustainability

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Accurate quantification and characterization of a wind energy potential assessment and forecasting is significant to optimal wind farm design, evaluation and scheduling. However, wind energy potential assessment and forecasting remain difficult and challenging research topics at present. Traditional wind energy assessment and forecasting models usually ignore the problem of data pre-processing as well as parameter optimization, which leads to low accuracy. Therefore, this paper aims to assess the potential of wind energy and forecast the wind speed in four locations in China based on the data pre-processing technique and swarm intelligent optimization algorithms. In the assessment stage, the cuckoo search (CS) algorithm, ant colony (AC) algorithm, firefly algorithm (FA) and genetic algorithm (GA) are used to estimate the two unknown parameters in the Weibull distribution. Then, the wind energy potential assessment results obtained by three data-preprocessing approaches are compared to recognize the best data-preprocessing approach and process the original wind speed time series. While in the forecasting stage, by considering the pre-processed wind speed time series as the original data, the CS and AC optimization algorithms are adopted to optimize three neural networks, namely, the Elman neural network, back propagation neural network, and wavelet neural network. The comparison results demonstrate that the new proposed wind energy assessment and speed forecasting techniques produce promising assessments and predictions and perform better than the single assessment and forecasting components.

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Linear and non-linear autoregressive models for short-term wind speed forecasting

Cited by 235 publications

References 23 publications

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Cash flow at risk of offshore wind plants

Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms

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