The Experts below are selected from a list of 45435 Experts worldwide ranked by ideXlab platform

Abbas Khosravi - One of the best experts on this subject based on the ideXlab platform.

  • nn based Prediction Interval for nonlinear processes controller
    International Journal of Control Automation and Systems, 2021
    Co-Authors: Mohammad Anwar Hosen, Abbas Khosravi, Saeid Nahavandi, Douglas Creighton, H Dipu M Kabir, Michael Johnstone, Peng Shi
    Abstract:

    Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point Prediction models are commonly used for nonlinear process control. However, Prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, Prediction Interval (PI)-based forecast bears extra information such as the Prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

  • Prediction Interval with examples of similar pattern and Prediction strength
    2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017
    Co-Authors: H.m. Dipu Kabir, Mohammad Anwar Hosen, Saeid Nahavandi, Abbas Khosravi
    Abstract:

    In this paper, we formed Prediction Intervals using historical similarities, found through the direct correlation. At first, a string of 5 to 20 recent samples is correlated with a long training string of samples. Then, the highest normalized correlation values and corresponding indexes are picked. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude of recent string and by dividing by the amplitude of matched strings. These adjusted samples are actually the Prediction values. Each Prediction value is given a weight (relevance) based on the value of normalized correlation and a function of the ratio between amplitudes of strings. A bar chart is drawn using the weighted (relevance) distribution and less relevant regions are discarded from sides. A Prediction strength is calculated from relevances. Except for the calculation of relevance, everything is calculated without any assumption. The user can check similar occurrences and decide to search more when the Prediction strength is low.

  • a new fuzzy based combined Prediction Interval for wind power forecasting
    IEEE Transactions on Power Systems, 2016
    Co-Authors: Abdollah Kavousifard, Abbas Khosravi, Saeid Nahavandi
    Abstract:

    This paper makes use of the idea of Prediction Intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

  • Prediction Interval estimation for wind farm power generation forecasts using support vector machines
    International Joint Conference on Neural Network, 2015
    Co-Authors: Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi
    Abstract:

    Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction Intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.

  • Prediction Interval estimation of electricity prices using pso tuned support vector machines
    IEEE Transactions on Industrial Informatics, 2015
    Co-Authors: Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi
    Abstract:

    Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction Intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania–New Jersey–Maryland (PJM) interconnection day-ahead and real-time markets.

Bijaya Ketan Panigrahi - One of the best experts on this subject based on the ideXlab platform.

  • a multiobjective framework for wind speed Prediction Interval forecasts
    Renewable Energy, 2016
    Co-Authors: Nitin Anand Shrivastava, Kunal Lohia, Bijaya Ketan Panigrahi
    Abstract:

    Abstract Wind energy is rapidly emerging as a potential and viable replacement for fossil fuels owing to its clean way of power production. However, integration of this abundantly available renewable energy into the power system is constrained by its intermittent nature and unpredictability. Efforts to improve the Prediction accuracy of wind speed is therefore imperative for its successful integration into the grid. The uncertainty associated with the Prediction is also an important information needed by the system operators for reliable and economic operations. This paper presents the implementation of a multi-objective differential evolution (MODE) algorithm for generation of Prediction Intervals (PIs) for capturing the uncertainty related to forecasts. Support vector machine (SVM) is used as the machine learning technique and its parameters are tuned such that multiple contradictory objectives are satisfied to generate Pareto-optimal solutions. Several case studies are performed for data from wind farms located in the eastern region of United States. The obtained results prove the successful implementation of the methodology and generation of high quality PIs.

  • Prediction Interval estimation for wind farm power generation forecasts using support vector machines
    International Joint Conference on Neural Network, 2015
    Co-Authors: Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi
    Abstract:

    Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction Intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.

  • Prediction Interval estimation of electricity prices using pso tuned support vector machines
    IEEE Transactions on Industrial Informatics, 2015
    Co-Authors: Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi
    Abstract:

    Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction Intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania–New Jersey–Maryland (PJM) interconnection day-ahead and real-time markets.

Pradipta Kishore Dash - One of the best experts on this subject based on the ideXlab platform.

  • a multi objective wind speed and wind power Prediction Interval forecasting using variational modes decomposition based multi kernel robust ridge regression
    Renewable Energy, 2019
    Co-Authors: Jyotirmayee Naik, Pradipta Kishore Dash, Snehamoy Dhar
    Abstract:

    Abstract This paper presents a new hybrid multi-objective wind speed and wind power Prediction Interval forecasting (PIs) model which is the combination of variational mode decomposition (VMD), Multi-kernel robust ridge regression (MKRR) and a multi-objective Chaotic water cycle algorithm (MOCWCA). VMD is applied to decompose the main time series signals into appropriate number of modes that avoids the mutual effects present in between the modes. The VMD based MKRR method is applied to estimate the wind speed and wind power Prediction Intervals at a Prediction Interval nominal confidence levels (PINC) of 95%, 90%,85% and 80%, respectively. Further to improve the performance of the proposed Prediction model MOCWCA is introduced for the optimization of the Prediction models parameters in such a way that multiple objectives are satisfied to produce Pareto-optimal solutions. The wind speed and power data samples for Prediction Interval forecasting are collected at 30 min and 1 hour time Intervals from the Sotavento wind farm located in Spain.

  • Prediction Interval forecasting of wind speed and wind power using modes decomposition based low rank multi kernel ridge regression
    Renewable Energy, 2018
    Co-Authors: Jyotirmayee Naik, Ranjeeta Bisoi, Pradipta Kishore Dash
    Abstract:

    Abstract In this paper a new hybrid method combining variational mode decomposition (VMD) and low rank Multi-kernel ridge regression (MKRR) is presented for direct and effective construction of Prediction Intervals (PIs) for short-term forecasting of wind speed and wind power. The original time series signals are decomposed using VMD approach to prevent the mutual effects among the different modes. The proposed VMD-MKRR method is used to construct the PIs with different confidence levels of 95%, 90% and 85% for wind speed and wind power of two wind farms which are located in the state of Wyoming, USA for time Intervals of 10 min, 30 min and 1 h and in the state of California for time Interval of 1 h respectively. Comparison with empirical mode decomposition (EMD) based low rank kernel ridge regression is also presented in the paper to validate the superiority of the VMD based wind speed and wind power model. Further to enhance the proposed model performance their parameters are optimized using Mutated Firefly Algorithm with Global optima concept (MFAGO).

Jyotirmayee Naik - One of the best experts on this subject based on the ideXlab platform.

  • a multi objective wind speed and wind power Prediction Interval forecasting using variational modes decomposition based multi kernel robust ridge regression
    Renewable Energy, 2019
    Co-Authors: Jyotirmayee Naik, Pradipta Kishore Dash, Snehamoy Dhar
    Abstract:

    Abstract This paper presents a new hybrid multi-objective wind speed and wind power Prediction Interval forecasting (PIs) model which is the combination of variational mode decomposition (VMD), Multi-kernel robust ridge regression (MKRR) and a multi-objective Chaotic water cycle algorithm (MOCWCA). VMD is applied to decompose the main time series signals into appropriate number of modes that avoids the mutual effects present in between the modes. The VMD based MKRR method is applied to estimate the wind speed and wind power Prediction Intervals at a Prediction Interval nominal confidence levels (PINC) of 95%, 90%,85% and 80%, respectively. Further to improve the performance of the proposed Prediction model MOCWCA is introduced for the optimization of the Prediction models parameters in such a way that multiple objectives are satisfied to produce Pareto-optimal solutions. The wind speed and power data samples for Prediction Interval forecasting are collected at 30 min and 1 hour time Intervals from the Sotavento wind farm located in Spain.

  • Prediction Interval forecasting of wind speed and wind power using modes decomposition based low rank multi kernel ridge regression
    Renewable Energy, 2018
    Co-Authors: Jyotirmayee Naik, Ranjeeta Bisoi, Pradipta Kishore Dash
    Abstract:

    Abstract In this paper a new hybrid method combining variational mode decomposition (VMD) and low rank Multi-kernel ridge regression (MKRR) is presented for direct and effective construction of Prediction Intervals (PIs) for short-term forecasting of wind speed and wind power. The original time series signals are decomposed using VMD approach to prevent the mutual effects among the different modes. The proposed VMD-MKRR method is used to construct the PIs with different confidence levels of 95%, 90% and 85% for wind speed and wind power of two wind farms which are located in the state of Wyoming, USA for time Intervals of 10 min, 30 min and 1 h and in the state of California for time Interval of 1 h respectively. Comparison with empirical mode decomposition (EMD) based low rank kernel ridge regression is also presented in the paper to validate the superiority of the VMD based wind speed and wind power model. Further to enhance the proposed model performance their parameters are optimized using Mutated Firefly Algorithm with Global optima concept (MFAGO).

Saeid Nahavandi - One of the best experts on this subject based on the ideXlab platform.

  • nn based Prediction Interval for nonlinear processes controller
    International Journal of Control Automation and Systems, 2021
    Co-Authors: Mohammad Anwar Hosen, Abbas Khosravi, Saeid Nahavandi, Douglas Creighton, H Dipu M Kabir, Michael Johnstone, Peng Shi
    Abstract:

    Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point Prediction models are commonly used for nonlinear process control. However, Prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, Prediction Interval (PI)-based forecast bears extra information such as the Prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

  • Prediction Interval with examples of similar pattern and Prediction strength
    2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017
    Co-Authors: H.m. Dipu Kabir, Mohammad Anwar Hosen, Saeid Nahavandi, Abbas Khosravi
    Abstract:

    In this paper, we formed Prediction Intervals using historical similarities, found through the direct correlation. At first, a string of 5 to 20 recent samples is correlated with a long training string of samples. Then, the highest normalized correlation values and corresponding indexes are picked. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude of recent string and by dividing by the amplitude of matched strings. These adjusted samples are actually the Prediction values. Each Prediction value is given a weight (relevance) based on the value of normalized correlation and a function of the ratio between amplitudes of strings. A bar chart is drawn using the weighted (relevance) distribution and less relevant regions are discarded from sides. A Prediction strength is calculated from relevances. Except for the calculation of relevance, everything is calculated without any assumption. The user can check similar occurrences and decide to search more when the Prediction strength is low.

  • a new fuzzy based combined Prediction Interval for wind power forecasting
    IEEE Transactions on Power Systems, 2016
    Co-Authors: Abdollah Kavousifard, Abbas Khosravi, Saeid Nahavandi
    Abstract:

    This paper makes use of the idea of Prediction Intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

  • Prediction Interval based neural network modelling of polystyrene polymerization reactor a new perspective of data based modelling
    World Congress on Engineering, 2014
    Co-Authors: Mohammad Anwar Hosen, Abbas Khosravi, Saeid Nahavandi, Douglas Creighton
    Abstract:

    Abstract In this paper, Prediction Interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors.

  • Prediction Interval construction and optimization for adaptive neurofuzzy inference systems
    IEEE Transactions on Fuzzy Systems, 2011
    Co-Authors: Abbas Khosravi, Saeid Nahavandi, Douglas Creighton
    Abstract:

    The performance of an adaptive neurofuzzy inference system (ANFIS) significantly drops when uncertainty exists in the data or system operation. Prediction Intervals (PIs) can quantify the uncertainty associated with ANFIS point Predictions. This paper first presents a methodology to adapt the delta technique for the construction of PIs for outcomes of the ANFIS models. As the ANFIS models are linear in their consequent part, the ANFIS-based PIs are computationally less expensive than neural network (NN)-based PIs. Second, this paper proposes a method to optimize ANFIS-based PIs. A new PI-based cost function is developed for the training of the ANFIS models. A simulated annealing-based algorithm is applied to minimize the new nonlinear cost function and adjust the premise and consequent parameters of the ANFIS model. Using three real-world case studies, it is shown that ANFIS-based PIs are computationally less expensive than NN-based PIs. The application of the proposed optimization algorithm leads to better quality PIs than optimized NN-based PIs.