The Experts below are selected from a list of 11865 Experts worldwide ranked by ideXlab platform

Vasilis Katos - One of the best experts on this subject based on the ideXlab platform.

  • An Ensemble Model for Short-Term Traffic Prediction in Smart City Transportation System
    2019 IEEE Global Communications Conference (GLOBECOM), 2019
    Co-Authors: Ge Zheng, Wei Koong Chai, Vasilis Katos
    Abstract:

    Smart city visions aim to offer citizens with intelligent services in various aspects of life. The services envisioned have been significantly enhanced with the proliferation of Internet-of-Things (IoT) technology offering real-time and ubiquitous monitoring capability. In this paper, we focus on the short-term traffic flow prediction problem based on real-world traffic data as one critical component of a smart city. In contrast to long-term traffic prediction, accurate prediction of short-term traffic flow facilitates timely traffic management and rapid response. We develop and study a novel Ensemble Model (EM) based on long short term memory (LSTM), deep autoencoder (DAE) and convolutional neural network (CNN) Models. Our approach takes into account both temporal and spatial characteristics of the traffic conditions. We evaluate our proposal against well-known existing prediction Models. We use two real traffic data (California and London roadways) with different characteristics to train and test the Models. Our results indicate that our proposed Ensemble Model achieves the most accurate predictions (approx. 97.50% and approx. % accuracy) and is robust against high variance traffic flow.

Jianzhou Wang - One of the best experts on this subject based on the ideXlab platform.

  • a novel decomposition Ensemble Model for forecasting short term load time series with multiple seasonal patterns
    Applied Soft Computing, 2018
    Co-Authors: Xiaobo Zhang, Jianzhou Wang
    Abstract:

    Abstract Effective and stable load forecasting is necessary and of great importance in ensuring a reliable supply of electricity and the security of the power system. However, due to such factors as cyclicity and seasonality, electric load series show complex nonlinearity characteristics. As a result, obtaining the desired forecasting accuracy becomes highly difficult and challenging. To address this problem, based on the “divide and conquer” idea, we developed a novel decomposition‐Ensemble Model for short‐term load forecasting (STLF) by integrating singular spectrum analysis (SSA), a support vector machine, the Autoregressive Integrated Moving Average Model and the cuckoo search algorithm. To effectively tackle nonlinearity characteristics and later improve forecasting performance, an SSA-based decomposition and reconstruction strategy was introduced into the proposed Model and performed based on the pre-analysis of hidden characteristics of the data. Specifically, the decomposed modes that reflect the inner data characteristics were analyzed and selected to establish specific individual predictors. Finally, the cuckoo search algorithm was employed to generate the Ensemble result. To verify the performance of the proposed Model, half-hourly load data from New South Wales and hourly load data from Singapore were used as illustrative cases. The experimental results demonstrate that the proposed decomposition-Ensemble Model can provide more accurate electric power forecasting compared with the eight Models discussed.

F.j. Von Zuben - One of the best experts on this subject based on the ideXlab platform.

  • IJCNN - A Hybrid Ensemble Model Applied to the Short-Term Load Forecasting Problem
    The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
    Co-Authors: R.m. Salgado, J.j.f. Pereira, T. Ohishi, R. Ballini, C.a.m. Lima, F.j. Von Zuben
    Abstract:

    In this paper we present a methodology based on a combination of many distinct predictors in an Ensemble, named hybrid Ensemble Model, to obtain a more accurate output using the results of single predictors. As basic components, we have used artificial neural networks and support vector machines Models. In order to evaluate the performance, the hybrid Model was required to predict a 24 h daily series energy consumption of a Brazilian electrical operation unit located in the northeast of Brazil. The proposed Ensemble Model has reached an error 25% smaller than that achieved by the best single predictor. The Model was initialized several times to confirm that Ensembles of predictors also tend to produce low variance profiles.

  • A Hybrid Ensemble Model Applied to the Short-Term Load Forecasting Problem
    The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
    Co-Authors: R.m. Salgado, J.j.f. Pereira, T. Ohishi, R. Ballini, C.a.m. Lima, F.j. Von Zuben
    Abstract:

    In this paper we present a methodology based on a combination of many distinct predictors in an Ensemble, named hybrid Ensemble Model, to obtain a more accurate output using the results of single predictors. As basic components, we have used artificial neural networks and support vector machines Models. In order to evaluate the performance, the hybrid Model was required to predict a 24 h daily series energy consumption of a Brazilian electrical operation unit located in the northeast of Brazil. The proposed Ensemble Model has reached an error 25% smaller than that achieved by the best single predictor. The Model was initialized several times to confirm that Ensembles of predictors also tend to produce low variance profiles.

Ge Zheng - One of the best experts on this subject based on the ideXlab platform.

  • An Ensemble Model for Short-Term Traffic Prediction in Smart City Transportation System
    2019 IEEE Global Communications Conference (GLOBECOM), 2019
    Co-Authors: Ge Zheng, Wei Koong Chai, Vasilis Katos
    Abstract:

    Smart city visions aim to offer citizens with intelligent services in various aspects of life. The services envisioned have been significantly enhanced with the proliferation of Internet-of-Things (IoT) technology offering real-time and ubiquitous monitoring capability. In this paper, we focus on the short-term traffic flow prediction problem based on real-world traffic data as one critical component of a smart city. In contrast to long-term traffic prediction, accurate prediction of short-term traffic flow facilitates timely traffic management and rapid response. We develop and study a novel Ensemble Model (EM) based on long short term memory (LSTM), deep autoencoder (DAE) and convolutional neural network (CNN) Models. Our approach takes into account both temporal and spatial characteristics of the traffic conditions. We evaluate our proposal against well-known existing prediction Models. We use two real traffic data (California and London roadways) with different characteristics to train and test the Models. Our results indicate that our proposed Ensemble Model achieves the most accurate predictions (approx. 97.50% and approx. % accuracy) and is robust against high variance traffic flow.

Xiaobo Zhang - One of the best experts on this subject based on the ideXlab platform.

  • a novel decomposition Ensemble Model for forecasting short term load time series with multiple seasonal patterns
    Applied Soft Computing, 2018
    Co-Authors: Xiaobo Zhang, Jianzhou Wang
    Abstract:

    Abstract Effective and stable load forecasting is necessary and of great importance in ensuring a reliable supply of electricity and the security of the power system. However, due to such factors as cyclicity and seasonality, electric load series show complex nonlinearity characteristics. As a result, obtaining the desired forecasting accuracy becomes highly difficult and challenging. To address this problem, based on the “divide and conquer” idea, we developed a novel decomposition‐Ensemble Model for short‐term load forecasting (STLF) by integrating singular spectrum analysis (SSA), a support vector machine, the Autoregressive Integrated Moving Average Model and the cuckoo search algorithm. To effectively tackle nonlinearity characteristics and later improve forecasting performance, an SSA-based decomposition and reconstruction strategy was introduced into the proposed Model and performed based on the pre-analysis of hidden characteristics of the data. Specifically, the decomposed modes that reflect the inner data characteristics were analyzed and selected to establish specific individual predictors. Finally, the cuckoo search algorithm was employed to generate the Ensemble result. To verify the performance of the proposed Model, half-hourly load data from New South Wales and hourly load data from Singapore were used as illustrative cases. The experimental results demonstrate that the proposed decomposition-Ensemble Model can provide more accurate electric power forecasting compared with the eight Models discussed.