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PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.
Wendong Yang; Guolin Tang; Yan Hao; Jianzhou Wang. A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health. Atmosphere 2021, 12, 1020 .
AMA StyleWendong Yang, Guolin Tang, Yan Hao, Jianzhou Wang. A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health. Atmosphere. 2021; 12 (8):1020.
Chicago/Turabian StyleWendong Yang; Guolin Tang; Yan Hao; Jianzhou Wang. 2021. "A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health." Atmosphere 12, no. 8: 1020.
Accurately crude oil price prediction remains challenging so far. Despite the abundant research achievements of crude oil price prediction, most of them emphasize the linear and deterministic modeling, which cannot adequately capture the complex nonlinear characteristics and uncertainties involved, thus impeding further developments in the field. In this study, a novel learning system with the aim of obtaining deterministic and probabilistic predictions is presented to model the nonlinear dynamics in crude oil price, composed by the modules of recurrence analysis, outlier detection, data preprocessing, feature selection, predictive modeling based on deep learning, and system evaluation. In particular, the temporal convolution is developed to perform the feature selection, thus improving the generalization of the system. Additionally, the extensions, including the predictive performance test evaluation, convergence investigation, and sensitivity analysis, are carried out. The experimental simulations show that the proposed system can yield the deterministic and probabilistic predictions with higher accuracy and feasibility compared with the benchmarks considered, further indicating its effectiveness.
Tong Niu; Jianzhou Wang; Hai Yan Lu; Wendong Yang; Pei Du. A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price. IEEE Transactions on Industrial Informatics 2020, PP, 1 -1.
AMA StyleTong Niu, Jianzhou Wang, Hai Yan Lu, Wendong Yang, Pei Du. A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price. IEEE Transactions on Industrial Informatics. 2020; PP (99):1-1.
Chicago/Turabian StyleTong Niu; Jianzhou Wang; Hai Yan Lu; Wendong Yang; Pei Du. 2020. "A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.
The quantification of the uncertainty in crude oil price is of significance to improve the related financial decision-making. However, studies in this field have remained limited because the nonlinearity inherent in the crude oil price makes it challenging to model its uncertainty. In this paper, a novel learning system of ensemble probabilistic prediction combining five popular machine learning methods and an improved optimizer is presented to effectively model the uncertainty in crude oil price and establish the corresponding prediction interval with satisfactory reliability and resolution. An improved grey wolf optimizer based on the adaptive Cuckoo search algorithm (AGWOCS) is proposed in the learning system to integrate the prediction intervals produced by the above machine learning methods. In addition, the superiority of the proposed AGWOCS is validated based on an algorithm test, compared to three benchmark optimizers. To validate the effectiveness of the proposed learning system, the uncertainties in daily and weekly Europe Brent spot prices are modeled as a case study. The evaluation results based on the reliability, resolution, and sharpness demonstrate that the proposed learning system can yield the prediction interval with a higher quality, which has distinct advantages over eight benchmarks as a whole. The convergence and scalability of the learning system are also investigated, which reveals its feasibility.
Jianzhou Wang; Tong Niu; Pei Du; Wendong Yang. Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price. Applied Soft Computing 2020, 95, 106509 .
AMA StyleJianzhou Wang, Tong Niu, Pei Du, Wendong Yang. Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price. Applied Soft Computing. 2020; 95 ():106509.
Chicago/Turabian StyleJianzhou Wang; Tong Niu; Pei Du; Wendong Yang. 2020. "Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price." Applied Soft Computing 95, no. : 106509.
Intelligent financial forecasting modeling plays an important role in facilitating investment-related decision-making activities in financial markets. However, accurate multivariate financial time series forecasting remains a challenge due to its complex nonlinear pattern. Aiming to fill the gap in the field, a novel forecasting framework, based on a two-stage feature selection model, deep learning model, and error correction model, is presented in this study, aiming at effectively capturing the nonlinearity inherent in multivariate financial time series. Concretely, the proposed two-stage feature selection model is utilized to determine the optimal feature set to further improve the generalization of the proposed deep learning model based on three deep learning units. Meanwhile, the error correction model is used to correct the forecasts and improve the accuracy further. To validate the performance of the forecasting framework, the case studies and the corresponding sensitivity analysis are carried out, consequently demonstrating its superiority, as compared to 16 benchmarks considered.
Tong Niu; Jianzhou Wang; Haiyan Lu; Wendong Yang; Pei Du. Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Systems with Applications 2020, 148, 113237 .
AMA StyleTong Niu, Jianzhou Wang, Haiyan Lu, Wendong Yang, Pei Du. Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Systems with Applications. 2020; 148 ():113237.
Chicago/Turabian StyleTong Niu; Jianzhou Wang; Haiyan Lu; Wendong Yang; Pei Du. 2020. "Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting." Expert Systems with Applications 148, no. : 113237.
Electricity price forecasting is an important and challenging issue for all participants in the power market because of the wide application of electricity in our society and its inherent features. In this context, some current forecasting systems use data preprocessing and optimization for theoretical and practical achievements. However, some limitations to these systems exist which need to be urgently solved. First, future information is overdrawn in the data preprocessing stage of these forecasting systems, which is actually unknown in practical applications. The crucial question, therefore, is how to develop a forecasting system without using any future information. Second, the complex features of original nonlinear and nonstationary electricity price have a negative influence on the generalization ability of these previously developed models. To decrease the negative effects on management, a method to develop a forecasting system to improve the model’s generalization ability is required. Therefore, in this study, we developed an adaptive deterministic and probabilistic interval forecasting system for multi-step electricity price forecasting, which can present more valuable information to power market decision makers. Two cases and one comparative study are provided and analyzed to validate the performance of the developed system in multi-step electricity price forecasting. Furthermore, further discussions are presented to illustrate the significance of this study, thus proving that the results of the present study fill the present knowledge gap and provide some new future directions for related studies.
Wendong Yang; Jianzhou Wang; Tong Niu; Pei Du. A novel system for multi-step electricity price forecasting for electricity market management. Applied Soft Computing 2019, 88, 106029 .
AMA StyleWendong Yang, Jianzhou Wang, Tong Niu, Pei Du. A novel system for multi-step electricity price forecasting for electricity market management. Applied Soft Computing. 2019; 88 ():106029.
Chicago/Turabian StyleWendong Yang; Jianzhou Wang; Tong Niu; Pei Du. 2019. "A novel system for multi-step electricity price forecasting for electricity market management." Applied Soft Computing 88, no. : 106029.
Jianzhou Wang; Tong Niu; Haiyan Lu; Wendong Yang; Pei Du. A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting. IEEE Transactions on Sustainable Energy 2019, 11, 337 -349.
AMA StyleJianzhou Wang, Tong Niu, Haiyan Lu, Wendong Yang, Pei Du. A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting. IEEE Transactions on Sustainable Energy. 2019; 11 (1):337-349.
Chicago/Turabian StyleJianzhou Wang; Tong Niu; Haiyan Lu; Wendong Yang; Pei Du. 2019. "A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting." IEEE Transactions on Sustainable Energy 11, no. 1: 337-349.
Electricity price forecasting plays a crucial role in balancing electricity generation and consumption, which is of great political and economic significance for all of society but is still a challenging task. However, in previous studies, most researchers have focused on improving either forecasting accuracy or stability while ignoring the significance of performing these tasks simultaneously. More importantly, few researchers have deeply studied the data preprocessing strategy, only focusing on the application of individual decomposition approaches. Therefore, a novel hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization is developed for electricity price forecasting that includes four modules: a data preprocessing module, optimization module, forecasting module and evaluation module. In this system, an effective multi-objective optimization algorithm is employed to guarantee simultaneous improvements in accuracy and stability. In addition, an improved data preprocessing approach named the dual decomposition strategy is developed, which successfully overcomes the potential drawback of the individual decomposition approach and further improves the effectiveness of the developed forecasting system. Moreover, the evaluation module is incorporated to verify the superiority of the developed forecasting system. Case studies utilizing half-hourly electricity price data collected from New South Wales, Australia are employed as examples. The results prove the superiority of the multi-objective optimization algorithm and the developed dual decomposition strategy and reveal that the developed forecasting system outperforms all of the considered comparison models, which shows its better ability to forecast future electricity prices with better accuracy and stability.
Wendong Yang; Jianzhou Wang; Tong Niu; Pei Du. A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Applied Energy 2018, 235, 1205 -1225.
AMA StyleWendong Yang, Jianzhou Wang, Tong Niu, Pei Du. A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Applied Energy. 2018; 235 ():1205-1225.
Chicago/Turabian StyleWendong Yang; Jianzhou Wang; Tong Niu; Pei Du. 2018. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting." Applied Energy 235, no. : 1205-1225.
Managers and researchers have put more emphasis on electrical power system forecasting to obtain effective management in electrical power system. However, enhancing prediction accuracy is not only a highly challenging task, but also a concerned problem in electrical power system. Traditional single algorithms usually ignore the significance of parameter optimization and data preprocessing, which always leads to poor results. Thus, in this paper a novel hybrid forecasting system was successfully developed, including four modules: data preprocessing module, optimization module, forecasting module and evaluation module. In this system, a signal processing approach is employed to decompose, reconstruct, identify and mine the primary characteristics of electrical power system time series in data preprocessing module. Moreover, to achieve high accuracy and overcome the drawbacks of single models, optimization algorithms are also employed to optimize the parameters of these individual models in the optimization and forecasting modules. Finally, evaluation module including hypothesis testing, evaluation criteria and case studies is introduced to make a comprehensive evaluation for this system. Experimental results showed that the hybrid system not only can be able to satisfactorily approximate the actual value, but also be regarded as an effective and simple tool adopted in smart grids.
Pei Du; Jianzhou Wang; Wendong Yang; Tong Niu. Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renewable Energy 2018, 122, 533 -550.
AMA StylePei Du, Jianzhou Wang, Wendong Yang, Tong Niu. Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renewable Energy. 2018; 122 ():533-550.
Chicago/Turabian StylePei Du; Jianzhou Wang; Wendong Yang; Tong Niu. 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system." Renewable Energy 122, no. : 533-550.
Jianzhou Wang; Wendong Yang; Pei Du; Tong Niu. A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Conversion and Management 2018, 163, 134 -150.
AMA StyleJianzhou Wang, Wendong Yang, Pei Du, Tong Niu. A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Conversion and Management. 2018; 163 ():134-150.
Chicago/Turabian StyleJianzhou Wang; Wendong Yang; Pei Du; Tong Niu. 2018. "A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm." Energy Conversion and Management 163, no. : 134-150.
Jianzhou Wang; Wendong Yang; Pei Du; Yifan Li. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system. Energy 2018, 148, 59 -78.
AMA StyleJianzhou Wang, Wendong Yang, Pei Du, Yifan Li. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system. Energy. 2018; 148 ():59-78.
Chicago/Turabian StyleJianzhou Wang; Wendong Yang; Pei Du; Yifan Li. 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system." Energy 148, no. : 59-78.
Jianzhou Wang; Tong Niu; Haiyan Lu; Zhenhai Guo; Wendong Yang; Pei Du. An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms. Applied Energy 2018, 211, 492 -512.
AMA StyleJianzhou Wang, Tong Niu, Haiyan Lu, Zhenhai Guo, Wendong Yang, Pei Du. An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms. Applied Energy. 2018; 211 ():492-512.
Chicago/Turabian StyleJianzhou Wang; Tong Niu; Haiyan Lu; Zhenhai Guo; Wendong Yang; Pei Du. 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms." Applied Energy 211, no. : 492-512.
Jianzhou Wang; Pei Du; Tong Niu; Wendong Yang. A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting. Applied Energy 2017, 208, 344 -360.
AMA StyleJianzhou Wang, Pei Du, Tong Niu, Wendong Yang. A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting. Applied Energy. 2017; 208 ():344-360.
Chicago/Turabian StyleJianzhou Wang; Pei Du; Tong Niu; Wendong Yang. 2017. "A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting." Applied Energy 208, no. : 344-360.
Pei Du; Jianzhou Wang; Zhenhai Guo; Wendong Yang. Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Conversion and Management 2017, 150, 90 -107.
AMA StylePei Du, Jianzhou Wang, Zhenhai Guo, Wendong Yang. Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Conversion and Management. 2017; 150 ():90-107.
Chicago/Turabian StylePei Du; Jianzhou Wang; Zhenhai Guo; Wendong Yang. 2017. "Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting." Energy Conversion and Management 150, no. : 90-107.
Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA) and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics.
Ping Jiang; Zeng Wang; KeQuan Zhang; Wendong Yang. An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting. Energies 2017, 10, 954 .
AMA StylePing Jiang, Zeng Wang, KeQuan Zhang, Wendong Yang. An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting. Energies. 2017; 10 (7):954.
Chicago/Turabian StylePing Jiang; Zeng Wang; KeQuan Zhang; Wendong Yang. 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting." Energies 10, no. 7: 954.
Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series’ forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR) model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.
Wendong Yang; Jianzhou Wang; Rui Wang. Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting. Entropy 2017, 19, 52 .
AMA StyleWendong Yang, Jianzhou Wang, Rui Wang. Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting. Entropy. 2017; 19 (2):52.
Chicago/Turabian StyleWendong Yang; Jianzhou Wang; Rui Wang. 2017. "Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting." Entropy 19, no. 2: 52.
Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of “decomposition and ensemble” and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.
Yunzhen Xu; Wendong Yang; Jianzhou Wang. Air quality early-warning system for cities in China. Atmospheric Environment 2016, 148, 239 -257.
AMA StyleYunzhen Xu, Wendong Yang, Jianzhou Wang. Air quality early-warning system for cities in China. Atmospheric Environment. 2016; 148 ():239-257.
Chicago/Turabian StyleYunzhen Xu; Wendong Yang; Jianzhou Wang. 2016. "Air quality early-warning system for cities in China." Atmospheric Environment 148, no. : 239-257.