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Records with Subject: Intelligent Systems
Showing records 1 to 25 of 89. [First] Page: 1 2 3 4 Last
Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems
Yujing Sun, Fei Wang, Bo Wang, Qifang Chen, N.A. Engerer, Zengqiang Mi
March 15, 2019 (v1)
Keywords: correlation-based feature selection (CFS), meteorological impact factor (MIF), mutual information (MI) theory, photovoltaic (PV) module temperature, quantitative influence analysis
The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs) and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS) and MI theory to explore the sp... [more]
Deep Neural Network Based Demand Side Short Term Load Forecasting
Seunghyoung Ryu, Jaekoo Noh, Hongseok Kim
March 15, 2019 (v1)
Keywords: deep learning, deep neural network, exponential smoothing, pre-training, rectified linear unit (ReLU), restricted Boltzmann machine (RBM), short-term load forecasting, smart grid
In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt⁻Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage e... [more]
A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China
Haixiang Zang, Miaomiao Wang, Jing Huang, Zhinong Wei, Guoqiang Sun
February 27, 2019 (v1)
Keywords: climatic zones, solar energy, solar radiation, typical meteorological year (TMY)
Since a representative dataset of the climatological features of a location is important for calculations relating to many fields, such as solar energy system, agriculture, meteorology and architecture, there is a need to investigate the methodology for generating a typical meteorological year (TMY). In this paper, a hybrid method with mixed treatment of selected results from the Danish method, the Festa-Ratto method, and the modified typical meteorological year method is proposed to determine typical meteorological years for 35 locations in six different climatic zones of China (Tropical Zone, Subtropical Zone, Warm Temperate Zone, Mid Temperate Zone, Cold Temperate Zone and Tibetan Plateau Zone). Measured weather data (air dry-bulb temperature, air relative humidity, wind speed, pressure, sunshine duration and global solar radiation), which cover the period of 1994⁻2015, are obtained and applied in the process of forming TMY. The TMY data and typical solar radiation data are investig... [more]
Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach
Josué M. Polanco-Martínez, Luis M. Abadie
February 27, 2019 (v1)
Keywords: futures oil markets, Maximal Overlap Discrete Wavelet Transform (MODWT), nonlinear causality test, oil spot prices, stochastic model, tight oil, time series analysis, wavelet correlation
The West Texas Intermediate (WTI) spot price shows high volatility and in 2014 and 2015 when quoted prices declined sharply, long-term prices in future markets were less volatile. These prices are different and diverge depending on how they process fundamental and transitory factors. US tight oil production has been a major innovation with significant macroeconomic effects. In this paper we use WTI spot prices and long-term futures prices, the latter calculated as the expected value with a stochastic model calibrated with the futures quotes of each sample day. These long-term prices are the long-term equilibrium value under risk neutral measurement. In order to analyze potential time-scale relationships between spots and future, we perform a wavelet cross-correlation analysis using a novel wavelet graphical tool recently proposed. To check the direction of the causality, we apply non-linear causality tests to raw data and log returns as well as to the wavelet transform of the spot and... [more]
Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application
Si Le Van, Bo Hyun Chon
February 27, 2019 (v1)
Keywords: artificial neural network, chemical flooding, enhanced oil recovery, net present value, Optimization
Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30%... [more]
Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming
Agustín A. Sánchez de la Nieta, Virginia González, Javier Contreras
February 27, 2019 (v1)
Keywords: ARIMA models, day-ahead electricity market price, forecasting portfolio, stochastic programming
Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP... [more]
Classification of Gene Expression Data Using Multiobjective Differential Evolution
Shijing Ma, Xiangtao Li, Yunhe Wang
February 27, 2019 (v1)
Keywords: binary differential evolution, binary optimization, differential evolution algorithm, multiobjective method
Gene expression data are usually redundant, and only a subset of them presents distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in bioinformatics. In this paper, a multiobjective binary differential evolution method (MOBDE) is proposed to select a small subset of informative genes relevant to the classification. In the proposed method, firstly, the Fisher-Markov selector is used to choose top features of gene expression data. Secondly, to make differential evolution suitable for the binary problem, a novel binary mutation method is proposed to balance the exploration and exploitation ability. Thirdly, the multiobjective binary differential evolution is proposed by integrating the summation of normalized objectives and diversity selection into the binary differential evolution algorithm. Finally, the MOBDE algorithm is used for feature selection, and support vector machine (SVM) is... [more]
Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms
Miloš Gligorić, Zoran Gligorić, Čedomir Beljić, Slavko Torbica, Svetlana Štrbac Savić, Jasmina Nedeljković Ostojić
February 27, 2019 (v1)
Keywords: adjusted Rand index, block model, coal deposit, entropy, Fukuyama-Sugeno validity functional, fuzzy C-mean clustering, fuzzy TOPSIS, technological model
The main aim of a coal deposit model is to provide an effective basis for mine production planning. The most applied approach is related to block modeling as a reasonable global representation of the coal deposit. By selection of adequate block size, deposits can be well represented. A block has a location in XYZ space and is characterized by adequate attributes obtained from drill holes data. From a technological point of view, i.e., a thermal power plant’s requirements, heating value, sulfur and ash content are the most important attributes of coal. Distribution of attributes’ values within a coal deposit can vary significantly over space and within each block as well. To decrease the uncertainty of attributes’ values within blocks the concept of fuzzy triangular numbers is applied. Production planning in such an environment is a very hard task, especially in the presence of requirements. Such requirements are considered as target values while the values of block attributes are the a... [more]
Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting
Yuqi Dong, Xuejiao Ma, Chenchen Ma, Jianzhou Wang
February 27, 2019 (v1)
Keywords: data decomposition, electrical load forecasting, generalized regression neural network, Genetic Algorithm
Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actu... [more]
Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques
Fakhri J. Hasanov, Lester C. Hunt, Ceyhun I. Mikayilov
February 27, 2019 (v1)
Keywords: Azerbaijan electricity demand, cointegration and error correction models, forecast scenarios, time series analysis
Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios—as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)—it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4½% per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources... [more]
Predictive Modeling of a Buoyancy-Operated Cooling Tower under Unsaturated Conditions: Adjoint Sensitivity Model and Optimal Best-Estimate Results with Reduced Predicted Uncertainties
Federico Di Rocco, Dan Gabriel Cacuci
February 27, 2019 (v1)
Keywords: adjoint cooling tower model solution verification, adjoint sensitivity analysis, best-estimate predictions, cooling tower, data assimilation, model calibration, reduced predicted uncertainties
Nuclear and other large-scale energy-producing plants must include systems that guarantee the safe discharge of residual heat from the industrial process into the atmosphere. This function is usually performed by one or several cooling towers. The amount of heat released by a cooling tower into the external environment can be quantified by using a numerical simulation model of the physical processes occurring in the respective tower, augmented by experimentally measured data that accounts for external conditions such as outlet air temperature, outlet water temperature, and outlet air relative humidity. The model’s responses of interest depend on many model parameters including correlations, boundary conditions, and material properties. Changes in these model parameters induce changes in the computed quantities of interest (called “model responses”), which are quantified by the sensitivities (i.e., functional derivatives) of the model responses with respect to the model parameters. Thes... [more]
Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation
Azhar Ahmed Mohammed, Zeyar Aung
February 27, 2019 (v1)
Keywords: ensemble models, Machine Learning, probabilistic forecasting, regression, solar power
Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the da... [more]
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Taiyong Li, Min Zhou, Chaoqi Guo, Min Luo, Jiang Wu, Fan Pan, Quanyi Tao, Ting He
February 27, 2019 (v1)
Keywords: crude oil price, energy forecasting, ensemble empirical mode decomposition (EEMD), kernel methods, particle swarm optimization (PSO), relevance vector machine (RVM)
Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kern... [more]
Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems
Suliang Ma, Mingxuan Chen, Jianwen Wu, Wenlei Huo, Lian Huang
February 27, 2019 (v1)
Keywords: artificial neural network (ANN), augmentation system, DC/DC converter, maximum power-point tracking (MPPT), non-linear controller, photovoltaic (PV) systems
Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate th... [more]
A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction
Guowei Cai, Wenjin Wang, Junhai Lu
February 27, 2019 (v1)
Keywords: artificial bee colony (ABC), seasonal autoregressive integrated moving average (SARIMA), short term load forecasting (STLF), support vector regression (SVR)
In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are c... [more]
Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network
Cheng-Ming Lee, Chia-Nan Ko
February 27, 2019 (v1)
Keywords: adaptive annealing learning algorithm, Particle Swarm Optimization, radial basis function neural network, short-term load forecasting, support vector regression
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various... [more]
Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine
Nantian Huang, Chong Yuan, Guowei Cai, Enkai Xing
February 27, 2019 (v1)
Keywords: partial autocorrelation function, variational mode decomposition, weighted regular extreme learning machine, wind speed forecasting
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.
Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices
Antonio Bello, Derek Bunn, Javier Reneses, Antonio Muñoz
February 5, 2019 (v1)
Keywords: densities, electricity, forecasting, fundamentals, hybrid, prices
This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tai... [more]
Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission
Yi Liang, Dongxiao Niu, Ye Cao, Wei-Chiang Hong
February 5, 2019 (v1)
Keywords: carbon emission, electricity demand forecasting, extreme learning machine (ELM), grey relation degree (GRD), induced ordered weighted harmonic averaging operator (IOWHA), multiple regression (MR)
The power industry is the main battlefield of CO₂ emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model base... [more]
Application of a Method for Intelligent Multi-Criteria Analysis of the Environmental Impact of Tailing Ponds in Northern Kosovo and Metohija
Gordana Milentijević, Blagoje Nedeljković, Milena Lekić, Zoran Nikić, Ivica Ristović, Jelena Djokić
February 5, 2019 (v1)
Keywords: ecology, environment, multi-criteria analysis, tailing pond, Trepča
The technological process of exploitation of mineral resources and processing of mined ores to cater to the market results, among other things, in a large amount of tailings deposed on tailing ponds. Because of the chemical composition of the material, the increasing amount of waste, and the mismanagement of recovery and reclamation of ponds, these ponds have become a significant element of negative impact on the surrounding ecosystem. Economics was behind the discharging of this material, resulting in tailing ponds created in inappropriate areas. There is an ongoing process of depositing tailings on old tailing ponds, although no special attention has been paid to the subsequent effect on the environment. Application of intelligent multi-criteria analysis AHP and PROMETHEE has been performed in this paper for the purpose of ranking the degree of negative impact on the environment of tailing ponds. Analysis is performed for five tailing ponds of MMCC (Mining Metallurgy Chemical Combine... [more]
Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution
Nabeel A. Khan, Faisal Baig, Syed Junaid Nawaz, Naveed Ur Rehman, Shree K. Sharma
February 5, 2019 (v1)
Keywords: distribution, power quality, power signals, smoothing, time-frequency
Spikes frequently occur in power quality (PQ) disturbance signals due to various causes such as switching of the inductive loads and the energization of the capacitor bank. Such signals are difficult to analyze using existing time-frequency (TF) methods as these signals have two orthogonal directions in a TF plane. To address this issue, this paper proposes an adaptive TF distribution (TFD) for the analysis of PQ signals. In the proposed adaptive method, the smoothing kernel’s direction is locally adapted based on the direction of energy in the joint TF domain, and hence an improved TF resolution can be obtained. Furthermore, the performance of the proposed adaptive technique in analyzing electrical PQ is thoroughly studied for both synthetic and real world electrical power signals with the help of extensive simulations. The simulation results (specially for empirical data) indicate that the adaptive TFD method achieves high energy concentration in the TF domain for signals composed of... [more]
Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology
Kaijian He, Hongqian Wang, Jiangze Du, Yingchao Zou
February 5, 2019 (v1)
Keywords: electricity market risk, Empirical Mode Decomposition (EMD), Exponential Weighted Moving Average (EWMA), Value at Risk (VaR)
The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement models based on the homogeneous and efficient market assumption no longer suffice, facing the increasing level of accuracy and reliability requirements. In this paper, we propose a new Empirical Mode Decomposition (EMD)-based Value at Risk (VaR) model to estimate the downside risk measure in the electricity market. The proposed model investigates and models the inherent multiscale market risk structure. The EMD model is introduced to decompose the electricity time series into several Intrinsic Mode Functions (IMF) with distinct multiscale characteristics. The Exponential Weighted Moving Average (EWMA) model is used to model the individual risk factors across different scales. Experimental results using different models in the Australian electric... [more]
Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm
Nantian Huang, Hua Peng, Guowei Cai, Jikai Chen
February 5, 2019 (v1)
Keywords: classification and regression tree algorithm, decision tree, feature selection, optimal multi-resolution fast S-transform, power quality disturbances
In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can... [more]
Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method
Luyu Ji, Junyong Wu, Yanzhen Zhou, Liangliang Hao
January 31, 2019 (v1)
Keywords: feature extraction and selection, support vector machines, trajectory clusters, transient stability prediction
To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter⁻wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologie... [more]
A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction
Yiqi Chu, Chengcai Li, Yefang Wang, Jing Li, Jian Li
January 31, 2019 (v1)
Keywords: ensemble forecasting, statistical correction, weather classification, wind forecasting, wind power
Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28⁻52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.
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