Wind Speed Interval Research Articles

A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution

Improving the quality of Wind Speed Interval prediction is important to maximize the usage of integrated wind energy as well as to reduce the adverse effects of the uncertainties, introduced by the random fluctuations of wind, to the power systems. This paper utilizes independently recurrent neural network to propose two new interval prediction frameworks. This network possesses the ability to retain memory at different lengths, which is helpful in capturing temporal features, especially for multi-horizon forecasts where the local dynamics get quite involved. In the first approach, we integrated a quantile regression loss function into this network to generate the intervals. This framework however, require to train different regressors to generate the conditional quantiles. Removing this limitation, a new simple and intuitive approach, is proposed which estimates the prediction intervals using a Gaussian function centered on the prediction and estimated error by a point prediction model and an error prediction model respectively. In our computational experiments, which involve two different wind fields contributing to eight different cases, an improvement of 43% and 12%, in average coverage width criterion index, over traditional models and LSTM based model respectively is remarkable. Thus, the proposed framework is able to produce high quality PIs while simultaneously reducing the computational cost.

A modified sub-assembly approach for hurricane induced wind-surge-wave vulnerability assessment of low-rise wood buildings in coastal communities

The coastal low-rise buildings account for large amounts of property loss during hurricanes, which makes it essential to perform vulnerability assessments of buildings and communities to estimate the possible structural damage and economical loss. Fragility assessment and loss prediction are usually performed with the sub-assembly (SA) approach in the HAZUS-MH model. However, hydrodynamic forces caused by surge and waves are excluded, and damages from wind and flood are assumed independent of each other. In the present study, a modified sub-assembly (MSA) approach is proposed based on the SA approach for the hurricane-induced wind-surge-wave vulnerability assessments for buildings and communities considering the hurricane-induced wind-surge-wave loadings without the predefined assumption. The physics-based high-fidelity finite element models are established, and the structural performances are analyzed for three typical coastal residential wood buildings under individual wind or surge-wave loadings and combined wind-surge-wave loadings. Building type is found to be a key factor influencing the vulnerabilities of the buildings, and building elevation is an effective method to mitigate building damages from surge-wave loadings as well as hurricane-induced wind-surge-wave loadings. Profound influences of wind profile shifting under combined wind-surge-wave loading are observed on elevated buildings' vulnerabilities at some stillwater levels for different building types. The MSA approach outperforms the SA approach in the building vulnerability prediction at the hurricane-induced wind-surge-wave scenarios, especially for the elevated buildings. A smaller wind speed interval is recommended to help with the building vulnerability prediction accuracy of the MSA approach. The vulnerability assessment of a coastal community further validates the accuracy of the proposed MSA approach.

A wind speed interval forecasting system based on constrained lower upper bound estimation and parallel feature selection

Wind speed interval forecasting is being considered increasingly because it can provide more comprehensive information to address uncertainties in wind power generation. It helps ensure power quality, optimize power dispatching, and bring more economic and social benefits. Currently, the lower upper bound estimation (LUBE) approach is believed to provide outstanding performance in interval forecasting. However, considering the significant noise and irregular characteristics of wind speed, most existing LUBE models can either ineffectively learn the wind speed variation patterns behind the data or behave in a very unstable manner, leading to unsatisfactory results. Thus, this paper proposes a novel LUBE-based wind speed interval forecasting system based on an innovative parallel feature selection module for extracting the underlying vital historical variation patterns and a unique constrained LUBE training algorithm characterized by an amnesia operator to further guarantee the efficiency and stability of the LUBE training. The system effectiveness was demonstrated by performing experiments using two real datasets. The results show that the proposed system performs better than the naive, bootstrap, error analysis, and other LUBE models. It at least enhances the coverage width criterion by 1.8% and 6.8% for the two datasets, respectively.

Influence of Wind Speed on CO2 and CH4 Concentrations at a Rural Site.

Meteorological variables have a noticeable impact on pollutant concentrations. Among these variables, wind speed is typically measured, although research into how pollutants respond to it can be improved. This study considers nine years of hourly CO2 and CH4 measurements at a rural site, where wind speed values were calculated by the METEX model. Nine wind speed intervals are proposed where concentrations, distribution functions, and daily as well as annual cycles are calculated. Contrasts between local and transported concentrations are around 5 and 0.03 ppm for CO2 and CH4, respectively. Seven skewed distributions are applied, and five efficiency criteria are considered to test the goodness of fit, with the modified Nash–Sutcliffe efficiency proving to be the most sensitive statistic. The Gumbel distribution is seen to be the most suitable for CO2, whereas the Weibull distribution is chosen for CH4, with the exponential function being the worst. Finally, daily and annual cycles are analysed, where a gradual decrease in amplitude is observed, particularly for the daily cycle. Parametric and nonparametric procedures are used to fit both cycles. The latter gave the best fits, with the agreement being higher for the daily cycle, where evolution is smoother than for the annual cycle.

A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction

With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.

Short-Term Forecasting and Uncertainty Analysis of Wind Power

Abstract Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic

Mapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density functions are used to model wind speed for five wind parks in the Norwegian Arctic region. A comparison between wind speed data from numerical weather prediction models and measurements is made, and a probability analysis for the wind speed interval corresponding to the rated power, which is largely absent in the existing literature, is presented. The results of the present study suggest that no single probability function outperforms across all scenarios. However, some differences emerged from the models when applied to different wind parks. The Nakagami and Generalised extreme value distributions were chosen for the numerical weather predicted prediction and the observed wind speed modelling, respectively, due to their superiority and stability compared with other methods. This paper, therefore, provides a novel direction for understanding the numerical weather prediction wind model and shows that its speed statistical features are better captured than those of real wind.

Probability box theory-based uncertain power flow calculation for power system with wind power

Abstract The uncertainty of wind speed may lead to the deviation and change of wind power output, which influences the stability of wind farm. Therefore, in this paper, a probability box (p-box) based uncertain power flow model for wind power is proposed, which initially introduces p-box to power flow calculation. A probabilistic interval power flow model with both probability and interval is established. Firstly, the drift interval of wind speed is obtained and its p-box model is established by analyzing the distribution of wind speed. Secondly, the wind power output p-box is derived from the wind speed p-box based on the relationship between wind power output and wind speed, then the p-box of wind power output is discretized and introduced into the power flow equation to obtain the power flow p-box model. Finally, Newton–Raphson method is used to solve the power flow p-box model. Experiments on data collected from a wind farm (running standard IEEE30-bus test system) in Inner Mongolia demonstrate that our method is more effective and accurate than the traditional Monte Carlo simulation (MCS) and classical interval power flow (IPF) method.

Desain Turbin Angin Horisontal untuk Area Kecepatan Angin Rendah dengan Airfoil S826

This research aims to determine performance of turbine rotor performance with a single rotor blade model with a diameter of 0.6 m that has been developed by NORCOWE, while for turbine rotor blades used is the NREL S826 airfoil series. The wind turbines are operated at wind speed intervals of 1-5 m / s. This parameter will also present data in the form of the optimal point of wind turbine rotation and rotor rotation speed. The pitch angles used are 25 °, 30 °, and 35 °. The pitch angle that affects the value of the ideal rotational speed with the highest optimization for the horizontal airfoil turbine S826 is 30 ° with a wind speed of 5 m / s and a rotation of 570 RPM. This is because the greater the pitch angle of the installation, the easier it will be to experience speed trimming but is vulnerable to too large an angle of attack that causes a stall.

An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting

Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming.

A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting

Wind speed forecasting is gaining importance as the share of wind energy in electricity systems increases. Numerous forecasting approaches have been used to predict wind speeds. However, considering the differences in wind speed time-series, there is no universal approach that has proven to be accurate under all circumstances. In our study, a combined prediction system is proposed, which consists of four parts: optimal sub-model selection, point prediction based on a modified multi-objective optimization algorithm, interval forecasting based on distribution fitting, and forecasting system evaluation. The developed combined system integrates the merits of the sub-models and provides accurate point and interval forecasting performance. The experimental results reveal that the proposed combined forecasting system can provide effective wind speed point and interval forecasts. The absolute percentage error values of the proposed system for point forecasting are 2.9220%, 3.1696%, and 4.8358% at Site 1 and 2.2719%, 2.5882%, and 3.4799% at Site 2 for one-, two-, and three-step forecasts, respectively. Therefore, the proposed system is deemed more useful for the scheduling and management of electric power systems than other benchmark models.

A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting

Wind energy is a renewable energy source with great development potential. However, its inherent instability and randomness have brought great challenges to the maximum utilization of wind energy. Wind speed forecasting is one of the most effective ways to mitigate these challenges, which plays an important role in the operational management and decision-making of wind power system operators. In this study, a novel wind speed interval prediction model based on gated recurrent unit, Variational Mode Decomposition, and Particle Swarm Optimization was proposed. The original wind speed sequence was decomposed into several smoother sub-sequences through the Variational Mode Decomposition algorithm, and corresponding sub-models were established based on the gated recurrent unit. To better supervise the training process, artificial prediction intervals with adaptive adjustment strategies were devised. Moreover, the Particle Swarm Optimization algorithm was adopted to search for the optimal superposition weights of PIs to achieve the integral optimization of the model. The qualitative and quantitative performance of the proposed method has been fully tested and verified in a series of real cases.

A Novel Wind Speed Interval Prediction Based on Error Prediction Method

Wind speed interval prediction plays an important role in wind power generation. In this article, a new interval construction model based on error prediction is proposed. The variational mode decomposition is used to decompose the complex wind speed time series into simplified modes. Two types of GRU models are built for wind speed prediction and error prediction. Prediction error for each mode is given a weight and accumulated to obtain the width of the prediction interval. The particle swarm optimization algorithm is applied to search for the optimal weights of the prediction errors. Experiments considering eight cases from two wind fields are conducted by using methods of interval construction in the literature for comparison with the proposed model. The result shows that the proposed model can obtain prediction intervals with higher quality.

Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

AbstractAs a clean and renewable energy source, wind energy has achieved remarkable growth around the world. Wind power/speed interval prediction has become an indispensable area of focus regarding the efficient dispatch of wind energy. As an important interval prediction method, the traditional lower and upper bound estimation (LUBE) has been a prevalent approach and a fundamental branch of energy prediction. However, the traditional LUBE model suffers from a low training efficiency owing to a lack of the gradient descent (GD) training mechanism. In this study, an improved LUBE model was designed using a novel training scheme based on the GD method for better efficiency and greater prediction performance. Initially, the new objective functions, which are continuous and differential, meeting the requirements of the GD method, were designed to obtain the best prediction interval (PI) quality with a narrower PI width and greater coverage probability. Then, different loss function forms have been proposed and compared, with the new Huber loss function having been confirmed to be more effective than other traditional loss functions. Finally, the new LUBE model with an objective part and adapting to the GD training method was constructed. Both traditional and improved LUBE models with different loss functions were compared experimentally, and the results indicate that the improved LUBE model with a Huber loss function significantly reduces the training time and improves the quality of the PI.

Temporal convolutional networks interval prediction model for wind speed forecasting

Wind speed interval prediction is one of the most elusive and long-standing challenges in wind power production. As a data source with intermittent and fluctuant characteristics, wind speed time series require highly nonlinear temporal features for the prediction tasks. In this paper, a novel interval prediction model is proposed based on temporal convolutional networks to forecast wind speed. A temporal convolutional networks architecture layer, multiple fully connected layers using tanh activation function and an end-to-end sorting layer are respectively served as input, hidden and output layers of the temporal convolutional networks interval prediction model which can generate prediction intervals directly. Additionally, an adaptive interval construction optimization strategy is put forward to devise training labels for learning of model. Eight cases from two wind fields are implemented to test and verify the proposed method. Specially, experiments have been designed to compare the prediction accuracy and reliability between the proposed model and the most recent state-of-the-art models. The forecasting results suggest that the proposed model has a significant performance improvement on both prediction interval coverage probability and prediction interval width criteria and thus can be a practical tool for wind speed forecasting.

Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution

At present, environmental pollution, climate warming and other problems are becoming more and more serious. And wind energy is pollution-free and never be exhausted, so it can make a major contribution to the global energy transformation. However, its random fluctuations and uncertainties bring adverse effects to the power system and endanger the safety of the power grid. Therefore, this paper combines artificial intelligence methods with statistical knowledge, and proposes a new interval prediction model based on the Fast Correlation Based Filter (FCBF) algorithm, the optimized Radial Basis Function (RBF) model and Fourier distribution for wind speed. Firstly considering environmental factors, this paper studies multi-factor wind speed prediction and applies the FCBF algorithm to filter the factors that affect the wind change. After that, this paper applies the idea of the Extremal Optimization (EO) to improve the Particle Swarm Optimization (PSO) and constructs a new EPSO optimization model for optimizing the RBF model. Next, using the Fourier function to fit the error probability distribution, and the wind speed interval is estimated based on point prediction results. Finally, the actual data of Changma Wind Farm is used for experiments to verify the feasibility and effectiveness of the proposed model. And through experimental results and comparison, it can be concluded: (1) Using the FCBF algorithm to select input variables can reduce redundant variables and lay a good foundation for subsequent prediction; (2) Applying the constructed EPSO-RBF model to predict wind speed, and the maximum and average value of the prediction error are only 0.8430 m/s, 0.1749 m/s, which is significantly better than several other traditional neural network models; (3) Introducing the Fourier function into the wind speed interval prediction, even at the 80% confidence level, the average width of the interval prediction is less than 3 m/s, and the coverage rate is higher than 90%.

High-speed train overturning safety under varying wind speed conditions

Given that the operation speeds of high-speed trains are rising constantly and considering the lightweight body technology that is being applied widely, vehicle systems have become much more sensitive to external excitation. When a train passes through a wind zone railway, for example, it often encounters an abrupt wind speed change phenomenon caused by the surrounding wind being affected by the local terrain. Such a phenomenon makes it more difficult to ensure train safety and commuter comfort. However, few studies have focused on the effect of changing wind speeds on a train’s operational safety. In this study, we developed a crosswind model on the basis of measured wind speed curves and investigated the effects of a single wind speed change and continuous wind speed changes on train dynamics. In addition, we also considered the influence of the rate of wind speed changes, the duration of the maximum wind speed, and changes in wind speed with different amplitudes. By considering the most critical wind speed changing conditions and by setting the overturning coefficient as the safety limit, we obtained the critical overturning wind speed under changing wind speed conditions. As a consequence, we suggested the process for estimating a wind speed interval for safe train operation under abruptly changing wind speed conditions.

Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction

Wind speed interval prediction is gaining importance in optimal planning and operation of power systems. However, the unpredictable characteristics of wind energy makes quality forecasting an arduous task. In this paper, we propose a novel hybrid model for wind speed interval prediction using an autoencoder and a bidirectional long short term memory neural network. The autoencoder initially extracts important unseen features from the wind speed data. The artificially generated features are utilized as input to the bidirectional long short term memory neural network to generate the prediction intervals. We also demonstrate that for time series prediction tasks, feature extraction through autoencoder is more effective than making deep residual networks. In our experiments which involve eight cases distributed among two wind fields, the proposed method is able to generate narrow prediction intervals with high prediction interval coverage and achieve an improvement of 39% in coverage width criterion over the traditional models.

Short-term wind speed interval prediction by improved regularized extreme learning machine based on attribute reduction

Accurate wind speed forecasting is important for stable operation of the power system when large-scale wind power is connected to the grid. According to the randomness of wind speed caused by the interaction of weather attributes, this article presents a new wind speed interval prediction method by improved regularized extreme learning machine based on attribute reduction. First, the principal component analysis is used to extract the principal component score sequences of multi-dimensional meteorological attribute factors, and the principal component score sequences are weighted by the variance contribution rate. Then, the original wind speed series is processed by fuzzy information granulation to obtain three components, which represent the minimum value, maximum value, and variation trend of the wind speed interval. The weighted principal component score sequence and the wind speed fuzzy granulation component are used as the input model of the prediction model, and the gradient prediction is performed using the improved regularized extreme learning machine of the gravity search algorithm. Finally, the prediction effect of the proposed method is simulated and analyzed based on the measured data of the wind farm. The results show that the combination prediction method can effectively improve the operational efficiency and accuracy of wind speed prediction, and has strong generalization ability.