Prediction of Coal Demand for Long-Term Power System Planning Based on Hybrid SSA and LSSVM Algorithms

LS-SVM based substation circuit breakers maintenance scheduling optimization

<p><strong>Abstract</strong><strong>:</strong> With the rapid development of artificial intelligence, machine learning has become an high-efficient tool applied in the fields of GNSS data analysis and processing, such as troposphere, ionosphere or satellite clock modeling and prediction. In this paper, zenith troposphere delay (ZTD) prediction algorithms based on BP neural network (BPNN) and least squares support vector machine (LSSVM) are proposed in the time and space domain. The main trend terms in ZTD time series are deducted by polynomial fitting, and the remaining residuals are reconstructed and modeled by BPNN and LSSVM algorithm respectively. The test results show that the performance of LSSVM is better than that of BPNN in term of prediction stability and accuracy by using ZTD products of International GNSS Service (IGS) of 20 stations in time domain. In order to further improve LSSVM prediction accuracy, a new strategy of training samples selection based on correlation analysis is proposed. The results show that using the proposed strategy, about 80% to 90% of the 1-hour prediction deviation of LSSVM can reach millimeter level depending on the season, and the percentage of the prediction deviation value less than 5 mm is about 60% to 70%, which is 5% to 20% higher than that of the classical random selection in different month. The mean values of RMSE in all 20 stations using the new strategy are 1-3mm smaller than those of the classical one. Then different prediction span from 1 to 12 hours is conducted to show the performance of the proposed method. Finally, the ZTD predictions based on BPNN and LSSVM in space domain are also verified and compared using GNSS CORS network data of Hong Kong, China.</p><p><strong>Keywords</strong><strong>:</strong> ZTD, BP Neural Network, Support Vector Machine, Least Squares, GNSS</p><p><strong>Acknowledgments:</strong> This work was supported by Natural Science Foundation of China (41874032) and the National Key Research and Development Program (2016YFB0501701)</p><p> </p>

Discriminating the Genuineness of Chinese Medicines Using Least Squares Support Vector Machines

The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM); 3.8%, 2.8%, 4.5%(RF); and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery Brain-Computer Interface (MI-BCI) and effectiveness in practical applications.

With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborate analysis of the change rules of the output electrical parameters and the equivalent circuit internal parameters of photovoltaic array in different fault states, the input variables of the photovoltaic array fault diagnosis model are determined. Second, through the LSSVM algorithm in the Bayesian framework, the fault diagnosis model based on the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array is built, which can effectively detect the photovoltaic array faults of short circuit, open circuit, and abnormal aging. Then, the simulation model is built to verify the validity of the LSSVM algorithm in the Bayesian framework by comparing it with the model of LSSVM and the Support Vector Machine (SVM). Moreover, a 5 × 3 photovoltaic array and a reference photovoltaic string are established and experimentally tested to validate the performance of the proposed method.

In this study, the response signals of three kinds of dry alfalfa volatile odors were collected by an electronic nose (E-nose), and the collected data were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). A least squares support vector machine (LS-SVM) model was established to classify and evaluate the data. For the combined E-nose algorithm, the classification accuracies of the PCA-LS-SVM and LDA-LS-SVM models are 85% and 100%, respectively. LDA as the input model has better classification accuracy than the PCA-based model. The results show that the combination of the LDA and LS-SVM algorithms using an E-nose signal is effective in identifying different drying alfalfa. The performance of the LDA-based LS-SVM model is slightly higher than that of the PCA-based LS-SVM model. It can be concluded that the E-nose system combined with the LDA-based model has great potential to distinguish different dry alfalfa.

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

LS-SVM(least squares support vector machine) has been widely used in engineering practice. However, the solving of LS-SVM still remains difficult under the condition of large sample. Based on algorithm of combinatorial optimization, this paper put forward the combinatorial optimization least squares support vector machine algorithm. On several different data aggregation of dimensions, the numerical value experiment and comparison are carried out on traditional LS-SVM algorithm, COLS-SVM algorithm and its improvement algorithm. The numerical value test has shown that COLS-SVM algorithm and its improvement algorithm are effective and have certain advantages on time and regression accuracy, compared with traditional LS-SVM algorithm.

Optimization of circuit breakers (CBs) maintenance schedule based on RCM can enhance substation reliability and lower maintenance cost. In this paper, a RCM approach based on Least Squares Support Vector Machines (LS-SVM) is proposed. Historical operation data are utilized to build a defects tree. A bi-level optimization algorithm is used to choose LS-SVM parameters. The LS-SVM algorithm is used to predict the distribution of defects before and after scheme optimization using aggregated defect data, outage duration, maintenance operation defect detection rate, etc. After the defect loss is quantified based on an expert scoring method and the Gross Domestic Product to power consumption ratio, a cost effect measurement is used to determine the best scheme. The effect of the proposed approach is verified using a numerical simulation of an electric power corporation.

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

The change-point detection problem is an important consideration in many application areas. This article aims to address this issue in auto-correlated process, where traditional statistical methods are not applicable. Based on least squares-support vector machine (LS-SVM) pattern recogniser, this article develops an intelligent method for solving the problem of the change-point detection, and the proposed model is applied to detect the change-point of process mean-shift in auto-correlated time-series process. In this research, LS-SVM algorithm and moving window method are used to detect the location of the mean-shift signal. The LS-SVM pattern recogniser is designed and the performance of the recogniser is evaluated in terms of Accuracy Rate. Results of simulation experiment show that the proposed intelligent model is an effective method to detect the change-point in the mean of autoregressive moving average data series. Compared with the traditional statistical methods for the change-points detection, th...

The problem of construction of B-spline curves by a set of given points is an important issue in computer aided geometric design (CAGD). It is actually regression problem. The traditional way is least squares fitting of the data based on minimizing the empirical risk. Least squares support vector machines (LS-SVMs) are very effective methods for regression issue. How to use LS-SVMs to solve the problem of construction B-spline curve in reverse engineering is discussed in this paper. Whereas LS-SVMs are not suitable for the regression curves by B-spline form, a modified least squares support vector machines algorithm is proposed which operates on the principle of structure risk minimization instead of the empirical risk minimization; hence a better generalization ability is guaranteed. A new kernel function is used to make curves have the B-spline form. Our new method provides a new fitting way for CAGD. Through the examples, the robust is compared among different methods. Results demonstrate the validity of this new algorithm

Optimization of vacuum assisted heat reflux extraction process of radix isatidis using least squares-support vector machine algorithm

Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.