Records with Subject: Intelligent Systems
Showing records 1 to 25 of 96. [First] Page: 1 2 3 4 Last
Determination of KOSGEB Support Models for Small- and Medium-Scale Enterprises by Means of Data Envelopment Analysis and Multi-Criteria Decision Making Methods
Ali Sevinç, Tamer Eren
July 11, 2019 (v1)
Keywords: AHP, data envelopment analysis, KOSGEB, productivity, SME, TOPSIS
Small- and Medium-Scale Enterprises (SMEs) act as catalysts in the general economy with regard to their added value. Support programs have been designed by the government through the Small and Medium Enterprises Development and Support Administration KOSGEB) and other institutions in order to further the general economic contributions of such enterprises. However, there is no method for using support models according to a productivity and effectiveness principle. This causes serious wastes of both resources and time. In this study, the problem of applying support models to improve the most critical problems of SMEs was discussed. As a place of application, 82 firms registered to the Konya Chamber of Industry were selected for the automotive supplier industry. Firstly, a productivity evaluation of companies was performed by a data envelopment analysis (DEA). Firms were grouped into A, B1, B2, C1, and C2 according to their activity scores. Using an Analytical Hierarchy Process (AHP), the... [more]
A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy
Yuxing Li, Xiao Chen, Jing Yu
May 16, 2019 (v1)
Keywords: complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), energy difference (ED), energy entropy (EE), hybrid energy feature extraction, ship-radiated noise (S-RN)
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first deco... [more]
Application of Data Mining in an Intelligent Early Warning System for Rock Bursts
Xuejun Zhu, Xiaona Jin, Dongdong Jia, Naiwei Sun, Pu Wang
May 16, 2019 (v1)
Keywords: clustering analysis, data mining, data warehouse, intelligent early warning, rock burst
In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real... [more]
Ultrasonic-Assisted Extraction and Swarm Intelligence for Calculating Optimum Values of Obtaining Boric Acid from Tincal Mineral
Bahdisen Gezer, Utku Kose
April 15, 2019 (v1)
Keywords: Artificial Intelligence, boric acid, central composite design, Optimization, swarm intelligence, tincal, ultrasound assisted extraction
The objective of this study is to focus on boric acid extraction from the mineral tincal, in order to determine the optimum conditions thanks to the ultrasonic-assisted extraction (UAE) technique (with the response surface methodology (RSM) for the first time), and artificial intelligence based swarm intelligence. Characterization of the tincal were done by using thermo-gravimetric assay (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. In detail, a central composite design (CCD) was used for determining the effects of different solvent/solid ratios, pH, extraction time, and extraction temperature on the yield, which was determined by the conductometric method. The optimum values regarding the best extraction process was calculated by using five different swarm intelligence techniques: Particle swarm optimization (PSO), cuckoo search (CS), genetic algorithms (GA), Differential evolution (DE), and the vortex optimization algorithm (VOA). In... [more]
FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time
Aydın Mühürcü
April 9, 2019 (v1)
Keywords: ABC, buck converter, control, FFANN, Modelling, Optimization, settling time
In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order phys... [more]
Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox
Wei Teng, Xiaolong Zhang, Yibing Liu, Andrew Kusiak, Zhiyong Ma
March 26, 2019 (v1)
Keywords: bearing in gearbox, prognostic, remaining useful life (RUL), wind turbine
Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN) is used to train data-driven models and to predict short-term tendencies of feature series. By combining the predicted and training features, a polynomial curve reflecting the long-term degradation process of bearings is fitted. Through solving the intersection between the fitted curve and the pre-defined threshold, the RUL can be deduced. The presented approach is validated by an operating wind turbine with a faulty bearing in the gearbox.
Development of a Numerical Weather Analysis Tool for Assessing the Precooling Potential at Any Location
Dimitris Lazos, Merlinde Kay, Alistair Sproul
March 26, 2019 (v1)
Keywords: climate effects, Energy Efficiency, precooling, weather analysis
Precooling a building overnight during the summer is a low cost practice that may provide significant help in decreasing energy demand and shaving peak loads in buildings. The effectiveness of precooling depends on the weather patterns at the location, however research in this field is predominantly focused in the building thermal response alone. This paper proposes an analytical tool for assessing the precooling potential through simulations from real data in a numerical weather prediction platform. Three dimensionless ratios are developed based on the meteorological analysis and the concept of degree hours that provide an understanding of the precooling potential, utilization and theoretical value. Simulations were carried out for five sites within the Sydney (Australia) metro area and it was found that they have different responses to precooling, depending on their proximity to the ocean, vegetation coverage, and urban density. These effects cannot be detected when typical meteorolo... [more]
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]
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