Coverage Width-based Criterion Research Articles

Intra-hour photovoltaic power point-interval prediction using a dual-view deep neural network

Photovoltaic (PV) power is intermittent, fluctuating, and uncertain due to meteorological factors, which makes it challenging to integrate PV power into electrical grids. Overcoming this problem requires an accurate and reliable PV power prediction model; thus, a dual-view deep neural network (Dv-DNN) is proposed. The model fuses a gate recurrent unit (GRU) neural network and a temporal convolutional network (TCN) in parallel, enabling end-to-end dual-view deep prediction for PV power. Using data from solar systems in the Yulara region of Australia, three sets of experiments—single-step, multi-step, and interval prediction—are conducted to explore the performance of two recurrent and convolutional deep neural networks in PV power prediction. For point prediction, the experimental results show that the Dv-DNN has better forecasting performance than other models as the prediction horizon increases. Regarding interval prediction, at confidence levels of 97.5%, 95%, 90%, and 85%, the method based on kernel density estimation (KDE) is superior to the method based on maximum likelihood estimation (MLE) according to the coverage width-based criterion (CWC).

Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit

Landslides, prevalent in mountainous areas, are typically triggered by tectonic movements, climatic changes, and human activities. They pose catastrophic risks, especially when occurring near settlements and infrastructure. Therefore, detecting, monitoring, and predicting landslide deformations is essential for geo-risk mitigation. The mainstream of the previous studies have often focused on deterministic models for immediate landslide prediction. However, in most of them, the aspect of prediction uncertainties are not sufficiently addressed. This paper introduces an innovative probabilistic prediction method using a Variational Autoencoder (VAE) combined with Gated Recurrent Unit (GRU) to forecast landslide deformations from a generative standpoint. Our approach consists of two main elements: firstly, training the VAE-GRU model to maximize the variational lower bound on the likelihood of historical precipitation data; secondly, using the learned approximated posterior distribution to predict imminent deformations from a generative angle. To assess the prediction quality, we use four widely-used metrics: Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Average Width (PINAW), Coverage Width-Based Criterion (CWC), and Prediction Interval Normalized Root Mean Square Width (PINRW). The results demonstrate that our proposed VAE-GRU framework surpasses traditional state-of-the-art (SOTA) probabilistic deformation prediction algorithms in terms of accuracy and reliability.

A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

Wind power prediction holds significant importance for the stable operation of power systems and the enhancement of energy utilization efficiency. Interval prediction, capable of providing more uncertain information, has garnered widespread attention. In this study we have designed a hybrid prediction framework for both deterministic and interval prediction. Initially, the original wind power data undergo cleaning using the quartile method and the fuzzy C-means (FCM) clustering algorithm. This approach not only eliminates scattered outliers but also identifies concentrated outliers more effectively. Subsequently, five distinct single prediction models were employed for point predictions. The critic weight method was applied to yield point forecasting results with higher prediction accuracy. Nonparametric kernel density estimation (KDE) and normal distribution (ND) were incorporated to calculate the prediction interval (PI) under varying confidence levels. Finally, an improved northern goshawk optimization (INGO) algorithm was proposed, integrating three enhancement methods: levy flight, sinusoidal mapping, and a reverse learning strategy. This optimization aims to fine-tune the weight coefficients of the aforementioned two interval prediction methods. The resulting PI overcomes the limitations of a single-interval prediction model. To effectively demonstrate the forecasting performance of the proposed prediction framework, two datasets from a wind turbine in northwest China were selected for simulation verification. In deterministic prediction, the designed hybrid forecast model demonstrates a reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by 8.5%, 16.98%, and 11.49%, respectively, compared to other single prediction models. In interval prediction, the prediction interval coverage probability (PICP) is enhanced, while the prediction interval normalized average width (PINAW) is effectively reduced. The designed interval prediction model can reduce the comprehensive evaluation index coverage width-based criterion (CWC) by 10.51%, 9.62%, and 12.95% on average at 95%, 90%, and 80% confidence levels, respectively. Simulation results verified the validity and feasibility of the proposed prediction framework.

Point and interval prediction for significant wave height based on LSTM-GRU and KDE

Accurate wave height prediction is significant for wave power generation, maritime traffic, and disaster prevention and reduction. This paper proposes a novel point and interval prediction framework based on deep learning methods for wave height prediction. First, multidimensional and multisource datasets are preprocessed. Subsequently, a feature selection process is executed to remove superfluous features. Third, a hybrid model that combines long short-term memory (LSTM) and a gated recurrent unit (GRU) is used to predict wave height. This model uses multivariate features that are positively correlated with wave height as input data. Finally, kernel density estimation (KDE) is leveraged to estimate the probability density distribution of the prediction errors generated the wave height prediction interval. The proposed LSTM-GRU-KDE forecasting framework is compared with benchmark models to verify its practicability. The LSTM-GRU model with multiple feature inputs has lower root mean square error and mean absolute error values and a higher coefficient of determination in predicting hourly wave height for all buoy stations compared to those of LSTM with multiple feature inputs and LSTM-GRU with a single feature input. Moreover, by the coverage width-based criterion, KDE (box) stands out as a superior choice among interval forecasting methods, offering a higher degree of reliability when constructing prediction intervals.

Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series

Many time series forecasting applications use ranges rather than point forecasts. Producing forecasts in the form of Prediction Intervals (PIs) is natural, since intervals are an important component of many mathematical models. The LUBE (Lower Upper Bound Estimation) method is aimed at finding ranges based on solving optimization problems taking into account interval width and coverage. Using the Particle Swarm Training of simple neural network, we look for a solution to the optimization problem of the Coverage Width-Based Criterion (CWC), which is the exponential convolution of conflicting criteria PICP (Prediction Interval Coverage Probability) and PINRW (Prediction Interval Normalized Root-mean-square Width). Based on the concept of the Pareto compromise, it is introduced as a Pareto front in the space of specified criteria. The Pareto compromise is constructed as a relationship between conflicting criteria based on the found solution to the optimization problem. The data under consideration are the financial time series of the MOEX closing prices. Our findings reveal that a relatively simple neural network, comprising eight neurons and their corresponding 26 parameters (weights of neuron connections and neuron signal biases), is sufficient to yield reliable PIs for the investigated financial time series. The novelty of our approach lies in the use of a simple network structure (containing fewer than 100 parameters) to construct PIs for a financial time series. Additionally, we offer an experimental construction of the Pareto frontier, formed by the PICP and PINRW criteria.

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.

Short-Term Probabilistic Forecasting Method for Wind Speed Combining Long Short-Term Memory and Gaussian Mixture Model

Wind speed forecasting is advantageous in reducing wind-induced accidents or disasters and increasing the capture of wind power. Accordingly, this forecasting process has been a focus of research in the field of engineering. However, because wind speed is chaotic and random in nature, its forecasting inevitably includes errors. Consequently, specifying the appropriate method to obtain accurate forecasting results is difficult. The probabilistic forecasting method has considerable relevance to short-term wind speed forecasting because it provides both the predicted value and the error distribution. This study proposes a probabilistic forecasting method for short-term wind speeds based on the Gaussian mixture model and long short-term memory. The precision of the proposed method is evaluated by prediction intervals (i.e., prediction interval coverage probability, prediction interval normalized average width, and coverage width-based criterion) using 29 monitored wind speed datasets. The effects of wind speed characteristics on the forecasting precision of the proposed method were further studied. Results show that the proposed method is effective in obtaining the probability distribution of predicted wind speeds, and the forecast results are highly accurate. The forecasting precision of the proposed method is mainly influenced by the wind speed difference and standard deviation.

A Predictive Maintenance Strategy Using Deep Learning Quantile Regression and Kernel Density Estimation for Failure Prediction

Failure prediction and maintenance decision-making are two core activities in a prognostics and health management (PHM) system. However, they are often studied independently and hierarchically. The main goal of this article is to combine system failure prediction with maintenance decision-making to develop a predictive maintenance strategy. System failure prediction is achieved by constructing an ensemble model of deep autoencoder (DAE), long short-term memory (LSTM), quantile regression (QR), and kernel density estimation (KDE), namely DAE-LSTMQR-KDE. Then, based on the probability density of system failure time obtained from the ensemble model, a replacement cost function (RCF) and an ordering cost function (OCF) are proposed to support maintenance and inventory decisions. Finally, optimal decisions are determined by minimizing the two cost functions. A score equal to 246.59 and a coverage width-based criterion (CWC) index equal to 0.35 were obtained when the DAE-LSTMQR-KDE ensemble model was applied to the C-MAPSS dataset, while the average maintenance cost rate (MCR) of the proposed maintenance strategy was 0.74. The results demonstrated that the proposed prediction and maintenance method outperforms several state-of-the-art methods. In addition, different cost structure scenarios are also investigated to illustrate the flexibility of maintenance decisions based on failure prediction information.

Ensemble Interval Prediction for Solar Photovoltaic Power Generation

In recent years, solar photovoltaic power generation has emerged as an essential means of energy supply. The prediction of its active power is not only conducive to cost saving but can also promote the development of solar power generation industry. However, it is challenging to obtain an accurate and high-quality interval prediction of active power. Based on the data set of desert knowledge in the Australia solar center in Australia, firstly, we have compared twelve interval prediction methods based on machine learning. Secondly, six ensemble methods, namely Ensemble-Mean, Ensemble-Median (Ensemble-Med), Ensemble-Envelop (Ensemble-En), Ensemble-Probability averaging of endpoints and simple averaging of midpoints (Ensemble-PM), Ensemble-Exterior trimming (Ensemble-TE), and Ensemble-Interior trimming (Ensemble-TI) are used to combine forecast intervals. The result indicates that Ensemble-TE is the best method. Additionally, compared to other methods, Ensemble-TE ensures the prediction interval coverage probability for confidence levels of 95%, 90%, 85%, and 80% as 0.960, 0.920, 0.873, and 0.824, respectively, using 15-min level data. Meanwhile, the narrower prediction interval normalized averaged width is calculated for the same confidence levels as 0.066, 0.045, 0.035, and 0.028, respectively. In addition, higher Winkler score and smaller coverage width-based criterion are obtained, representing high-quality intervals. We have calculated smaller mean prediction interval center deviation, which is approximately 0.044. Thus, the above demonstrates that this study obtains the prediction interval with better performance compared to other existing methods.

Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model

Accurate and reliable wind power forecasting (WPF) is significant for ensuring power systems’ economic operation and safe dispatching and for reducing the technical and economic risks faced by power market participants. Based on data-driven and deep-learning methods, we propose a hybrid ultra-short-term WPF framework that can achieve accurate point and interval WPF. First, the multi-sourced and multi-dimensional data sets of wind power plant are preprocessed. Second, feature selection (FS) is conducted to eliminate redundant features. Third, the wind power sequence is decomposed through the variational modal decomposition improved by grey wolf optimization (GWO-VMD). Then, the BiLSTM-Attention model is established to predict each subsequence of wind power. Finally, the prediction intervals of wind power under different confidence levels are estimated by kernel density estimation with the Gaussian kernel function (KDE-Gaussian). The proposed FS-GWO-VMD-BiLSTM-Attention forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the BPNN, the mean absolute error, mean absolute percentage error, and mean square error of the FS-GWO-VMD-BiLSTM-Attention model are reduced by 94.03%, 85.82%, and 99.51%, respectively. Furthermore, according to the coverage width-based criterion, KDE-Gaussian is superior to other interval forecasting methods, which can achieve more reliable forecasting of prediction interval.

A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms

The accurate wind speed prediction is of significant importance to decrease the adverse impact of wind randomness on the power systems and help wind power grid-tied. However, the previous studies mainly focus on the point forecast, and the uncertainties of the prediction accuracy have been ignored. Moreover, utilizing WRF numerous variables to construct wind speed prediction interval (PI) based on multivariate line regression theory has rarely been studied. Therefore, a novel wind speed PI method is proposed, including three modules: data collection, decomposition, and interval prediction. Specifically, various variables, such as wind speed, wind direction, temperature, pressure, and relative humidity, are first obtained from weather research and forecasting (WRF) simulation. Secondly, variational mode decomposition (VMD) is applied to decompose these variables and actual wind speed into respective intrinsic mode functions (IMFs). Lastly, generative adversarial network (GAN) captures temporal and spatial correlation features and realizes wind speed PI based on multivariate line regression theory. Four datasets are selected to verify the performance of the proposed model. The experimental results indicate that this novel model achieves better PI accuracy compared to several baseline methods. In the case of wind speed PI of dataset1, the PI normalized average width (PINAW) values improve by 49.53%, 15.21% over VMD-back propagation (BP), VMD-long short-term memory (LSTM) in the seventh-step prediction. In summary, the proposed model outperforms other comparative models due to the lower PINAW and coverage width-based criterion (CWC) with similar PI coverage probability (PICP).

A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting

The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10 min are used to train the models and forecast GHI at varying time horizons, ranging from 10 min to 4 h. Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination.

Flow interval prediction based on deep residual network and lower and upper boundary estimation method

Interval prediction is an efficient approach for quantifying the uncertainties of future flow. In this paper, a novel method, based on the combination of a deep residual neural network (ResNet) and lower and upper bound estimation (LUBE), is proposed to forecast future flow and construct prediction intervals. LUBE is proposed by optimizing a LUBE-based objective function and adjusting the type and quantity of residual blocks to design a combined residual network. The final proposed interval prediction model is stResNet-LUBEproposed. The performance of the stResNet-LUBEproposed model is verified using the spatiotemporal dataset of Tunxi, which is a small and medium watershed. The performance of proposed model is mainly evaluated by the root mean square error (RMSE), coefficient of determination (R2), and coverage width-based criterion (CWC). The experimental results show that the average values of RMSE, R2, and CWC of the proposed model are better than those of the spatiotemporal deep learning model stCNN-LUBEproposed, the deep learning model LSTM-LUBEproposed, and the machine learning model MLP-LUBEproposed by 1.881%, 10.574%, and 14.113%; 2.198%, 15.754%, and 18.546%; and 10.572%, 35.907%, and 46.819%, respectively (predict 6-step).

Short-term Electricity Price Probabilistic Forecasting Based on Support Vector Quantile Regression Optimized by Simulated Annealing Algorithm

Background: Electricity price forecasting is still a challenging issue as it plays an essential role in balancing electricity generation and consumption. Probabilistic electricity price forecasting not only provides deterministic price forecasts but also effectively quantifies the uncertainty of electricity price. Methods: This paper introduces a new short-term electricity price forecasting approach called SASVQR, which is based on support vector quantile regression (SVQR) optimized by simulated annealing algorithm. In this study, SVQR is employed to obtain the conditional quantiles of the electricity under different quantile points, while the simulated annealing algorithm is applied to optimize each SVR model. Then the kernel density estimation takes these conditional quantiles as inputs and generates the probability density functions for future electricity prices. Result: The proposed algorithm is assessed in three datasets: the GEFCom 2014, two real electricity price datasets from the PJM market and the Singapore market. Three popular probabilistic forecasting criteria, namely prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width-based criterion (CWC), are utilized to evaluate the numerical experiment results..It shows the promising forecasting performance, robustness, and effectiveness of SASVQR on different datasets. Conclusion: The SASVQR method can effectively forecast the short-term electricity price compared with other methods.

Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling

This paper presents the construction of prediction intervals (PIs) associated with the Emotional Artificial Neural Network (EANN) via the Lower Upper Bound Estimation method (LUBE) and the classic Bootstrap method for the Suspended Sediment Load (SSL) modeling. As point prediction conveys no information about modeling reliability, PIs were applied. The constructed PIs via the EANN were also compared to those of the classic feed-forward Neural Network (FFNN) model. It was attempted to construct the PIs of the SSL modeling in both daily and monthly scales for two watersheds, Upper Rio Grande River, in the United States and the Lighvanchai River in Iran. The PIs were quantified via coverage and width criteria. In the LUBE method, Genetic Algorithm (GA) constructed the PIs by minimizing the cost function of Combinational coverage Width-based Criterion (CWC). Comparison of the results indicated that the criterion of the CWC for PIs of EANN was up to 72% and 78% lower than that to the PIs of the FFNN, respectively, in the LUBE and Bootstrap methods, which showed the reliability of the EANN. In addition, obtained results via the LUBE and the Bootstrap techniques denoted the lower level of uncertainty of the LUBE method. Comparing the CWC criterion of both methods indicated that for the LUBE method, CWC was up to 39% lower than that for the Bootstrap method. Also, the PIs of the FFNN for the Lighvanchai river modeling showed reliable results with CWC of 56% lower than Upper Rio Grande River, but PIs of the EANN led to lower level of uncertainty in Upper Rio Grande River modeling.

Occupancy data at different spatial resolutions: Building energy performance and model calibration

Occupancy is a significant area of interest within the field of building performance simulation. Through Bayesian calibration, the present study investigates the impact of the availability of different spatial resolution of occupancy data on the gap between predicted and measured energy use in buildings. The study also examines the effect of occupancy data on the quality of the constructed prediction intervals (PIs) using the Coverage Width-based Criterion (CWC) metric. CWC evaluates the PIs based on both their coverage (correctness) and width (informativeness). This investigation takes the form of an actual building case study, with nine months of hourly measured building electricity use, WiFi connection counts as a proxy for occupancy, and actual weather data. In general, the building energy model’s accuracy improves with the occupancy and plug-loads schedule derived from WiFi data. Specifically, the Coefficient of Variation Root Mean Square Error (CV[RMSE]) reduced from 37% to 24% with an exponential improvement in the PIs quality compared to the results obtained with ASHRAE 90.1 reference schedules. However, the increase in prediction accuracy shrank to 5% CV(RMSE) and a comparable CWC upon calibrating the base loads of the reference schedules. Increasing the spatial resolution from building aggregated to floor aggregated occupancy data worsened the CV(RMSE) and CWC, suggesting trade-offs between parameter uncertainty and model bias/inadequacy. These results contribute to our understanding of the interactions between model complexity, simulation objectives, and data informativeness, facilitating future discussions on the right level of abstraction when modeling occupancy.

Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines

Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future landslide displacements. In this paper, under the framework of the original lower upper bound estimation method, a direct interval prediction approach is proposed for landslide displacements based on the least squares support vector machine (LSSVM) and differential search algorithms. Two LSSVM models are directly implemented to generate the interval of future displacements, and the optimal model parameters are derived by the differential search algorithm. The Baishuihe landslide and the Tanjiahe landslide located on the shoreline of the Three Gorges Reservoir, China, are used to test the proposed approach. Compared with other models, the proposed method performed best and presented the smallest coverage width-based criterion values of 0.8927 and 1.0562 at monitoring stations XD01 and ZG118 for the Baishuihe landslide, respectively, and 0.1316 and 0.1191 at monitoring stations ZG289 and ZG287 for the Tanjiahe landslide, respectively. The results indicate that the proposed approach can provide high-quality prediction intervals for landslide displacements in the Three Gorges Reservoir area.

Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation

The power integration is a challenge for the power system because of the fluctuation of the wind power. Wind power forecasting can estimate the future fluctuation of the wind power, and enhance the safety of the power integration. In this study, a corrected multi-resolution forecasting model is proposed to improve current wind power forecasting performance. The proposed model contains three stages, including multi-resolution ensemble, adaptive multiple error corrections and uncertainty estimation. Four real-time wind power data sets are applied to verify the effectiveness of the proposed model. The results are shown as follows: (a) the proposed model is effective for wind power forecasting, the 1-step index of agreement and coverage width-based criterion with 99% confidence level of the proposed model on the dataset #1 are 0.9432 and 0.6951 respectively; (b) the proposed model outperforms the previous models. Through techno-economic analysis, it can be concluded that the proposed model has the potential to be applied to improve the power integration performance.

A new hybrid framework for probabilistic wind speed prediction using deep feature selection and multi-error modification

Wind speed prediction is critical for the wind power exploitation and management due to their strong positive correlation. However, since natural wind owns the high uncertainty and nonstationarity, reliable wind speed prediction is generally difficult to be realized. The objective of this study is to develop a new method for accurately explaining these characteristics and then providing reliable prediction. To this end, a novel probabilistic forecasting framework using enhanced variational mode decomposition, deep feature selection and multi-error modification is proposed. Concretely, the raw data is preprocessed firstly by the enhanced variational mode decomposition where its decomposition level number can be adaptively optimized by the combination of Hilbert transform and empirical model decomposition. Then, an innovative feature selection method which is hybrid of Kullback-Leibler divergence, Gram-Schmidt orthogonal and sample entropy is developed to conduct deep feature identification. Finally, after the deterministic prediction is performed by least square support vector machine, a post-processing multi-error modification is generated to implement the probabilistic prediction. In this method, four probabilistic models including kernel density estimation, univariate conditional kernel density estimation, generalized autoregressive conditional heteroscedasticity and their hybrid model are employed to capture different properties embedded in the error component. Four case studies based on the measured data are carried out. Systematic assessment results show the proposed method has well-pleasing forecasting capability and may be more suitable for the data with higher nonstationarity and non-Gaussianity. For example, the coverage width-based criterion of the proposed method in terms of data collection 4 is 0.261, while those from data collections 1, 2 and 3 are 0.343, 0.295 and 0.282, respectively.

An Inter Type-2 FCR Algorithm Based T–S Fuzzy Model for Short-Term Wind Power Interval Prediction

Due to the intermittent and random nature of wind energy, wind power interval prediction (WPIP) is important to weaken the uncertainty and support the planning and scheduling of the power system. To improve the quality of WPIP, a novel fuzzy interval prediction model (FIPM) based on the lower upper bound estimation method is proposed in this paper. In the frame of the FIPM, a novel inter type-2 (IT-2) fuzzy model is designed to construct the lower and upper bounds of the prediction interval (PI), in which an IT-2 fuzzy c-regression algorithm is used to partition the data space and identify the fuzzy model. The gravitational search algorithm is employed to optimize the FIPM by minimizing coverage width-based criterion to reach a tradeoff between the interval width and coverage probability. In order to verify the effectiveness of the proposed method, existing interval prediction approaches are adopted in comparative experiments with 17 datasets extracted from five wind fields. The experimental results show that the proposed IT-2 FIPM achieves significant performance with huge promotion in the quality of the PI compared to the traditional forecasting models.