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Jianzhou Wang
Institute of Systems Engineering, Macau University of Science and Technology, Taipa Street, Macau

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Journal article
Published: 12 July 2021 in Resources Policy
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Effective crude oil and natural gas futures price forecasting is a crucial endeavor for financial energy markets and is also a challenging work due to the nonlinear and fluctuant characteristic of futures price time series. Most existing researches have failed at the consideration of both linear and nonlinear information, optimal sub-model selection, and interval forecasting. To bridge these gaps, a novel decomposition-selection-ensemble forecasting system is proposed to perform deterministic prediction and interval forecasting in this study, which is constituted by data decomposition method, optimal sub-model selection strategy, proposed multi-objective version of chaos game algorithm, and multiple forecasting models. The proposed forecasting system prominently prompted the forecasting accuracy and stability of energy futures price, and improved the applicability at dealing with different data characteristic. Empirical results based on energy futures price demonstrated that the point forecasting and interval forecasting results obtained from the proposed forecasting system are more reliable and stable relative to other comparative models; thus, it can provide useful references for national economic policies and operators in financial energy markets.

ACS Style

Ping Jiang; Zhenkun Liu; Jianzhou Wang; Lifang Zhang. Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm. Resources Policy 2021, 73, 102234 .

AMA Style

Ping Jiang, Zhenkun Liu, Jianzhou Wang, Lifang Zhang. Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm. Resources Policy. 2021; 73 ():102234.

Chicago/Turabian Style

Ping Jiang; Zhenkun Liu; Jianzhou Wang; Lifang Zhang. 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm." Resources Policy 73, no. : 102234.

Journal article
Published: 15 June 2021 in Sustainable Energy Technologies and Assessments
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Short-term wind speed prediction is an indispensable part of the operation and control of wind energy power generation systems. However, many prediction models proposed by researchers did not preprocess the original data or consider the limitations of a single prediction model, resulting in poor prediction accuracy. Based on the no-negative constraint theory, this study uses five neural networks with advanced optimization algorithms and data preprocessing to obtain high-precision prediction results. Four experiments were designed to test the effectiveness of the proposed model and four analysis strategies were used to discuss the experimental results. The empirical study used wind speed data from China. The results show that the MAPE and Std performance indicators in the multi-step prediction of the hybrid model are lower than in other benchmark models; the proposed model is far superior to comparable models in terms of accuracy and stability.

ACS Style

Linyue Zhang; Jianzhou Wang; Xinsong Niu. Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm. Sustainable Energy Technologies and Assessments 2021, 47, 101346 .

AMA Style

Linyue Zhang, Jianzhou Wang, Xinsong Niu. Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm. Sustainable Energy Technologies and Assessments. 2021; 47 ():101346.

Chicago/Turabian Style

Linyue Zhang; Jianzhou Wang; Xinsong Niu. 2021. "Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm." Sustainable Energy Technologies and Assessments 47, no. : 101346.

Research article
Published: 07 May 2021 in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
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With the steady integration of wind energy into electricity networks, precise wind speed forecasting is an essential element in the administration and management of power systems. However, wind energy forecasting research has focused increasingly on short-term forecasting, leaving aside the challenging horizons of medium- and long-term predictions. Therefore, this study proposes a wind speed forecasting methodology based on two types of ensembles, which addresses the nonlinearity and chaotic behavior of wind speed using decomposition-based models. With the results of the first ensemble of 90 ARMA-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) models, the second ensemble is established based on three types of neural networks and learning functions. Finally, we propose the application of variational mode decomposition (VMD) before or after the first ensemble. The experimental outcomes lead us to divide the prediction horizons into two broad groups, those where VMD inclusion did and did not improve the ensemble results. These horizons are classified as short-term (3, 4, and 5 steps) and mid- and long-term forecast horizons (6, 12, 24, and 48 steps), where the best performance arises with the VMD application after the first ensemble. The research contributes to the existing literature studying a wide variety of innovation distribution and optimization methods that can be implemented with GARCH-type models. Simultaneously, the VMD application is proposed in a novel way not seen in the literature by applying it to the predictions already made by other models, in this case, in ensembles of GARCH-type models.

ACS Style

Angel Colmenares; Jianzhou Wang. Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2021, 1 -18.

AMA Style

Angel Colmenares, Jianzhou Wang. Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2021; ():1-18.

Chicago/Turabian Style

Angel Colmenares; Jianzhou Wang. 2021. "Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects , no. : 1-18.

Journal article
Published: 24 April 2021 in Energy Conversion and Management
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Reliable and accurate wind speed forecasting is the basis for the effective development of wind energy. However, wind speed is intermittent, presents nonlinear patterns, and exhibits nonstationary behavior; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. Hence, in this study, two novel hybrid forecasting systems based on the structural characteristics of wind speed are proposed to capture the linear and nonlinear factors hidden in wind speed series. First, a decomposition algorithm is used to eliminate noise from raw data and reconstruct a more reliable wind speed time series. Then, a linear model, which employs the exponential smoothing model or autoregressive moving average model, captures the linear patterns hidden in the time series, and a nonlinear model, which applies the back propagation neural network optimized by the cuckoo search algorithm, extracts the nonlinear patterns hidden in the data. The experimental results using nine datasets show that the proposed model has better prediction accuracy than the comparison models and the root mean square error (RMSE), the mean absolute error (MAE) are respectively less than 0.2139 and 0.125, which provides a scientific basis for power grid dispatch and guarantees the stable operation of the wind power system.

ACS Style

Xiaojia Huang; Jianzhou Wang; Bingqing Huang. Two novel hybrid linear and nonlinear models for wind speed forecasting. Energy Conversion and Management 2021, 238, 114162 .

AMA Style

Xiaojia Huang, Jianzhou Wang, Bingqing Huang. Two novel hybrid linear and nonlinear models for wind speed forecasting. Energy Conversion and Management. 2021; 238 ():114162.

Chicago/Turabian Style

Xiaojia Huang; Jianzhou Wang; Bingqing Huang. 2021. "Two novel hybrid linear and nonlinear models for wind speed forecasting." Energy Conversion and Management 238, no. : 114162.

Journal article
Published: 01 April 2021 in Expert Systems with Applications
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Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids.

ACS Style

Zhenkun Liu; Ping Jiang; Jianzhou Wang; Lifang Zhang. Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Systems with Applications 2021, 177, 114974 .

AMA Style

Zhenkun Liu, Ping Jiang, Jianzhou Wang, Lifang Zhang. Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Systems with Applications. 2021; 177 ():114974.

Chicago/Turabian Style

Zhenkun Liu; Ping Jiang; Jianzhou Wang; Lifang Zhang. 2021. "Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm." Expert Systems with Applications 177, no. : 114974.

Journal article
Published: 26 November 2020 in Energies
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As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

ACS Style

Yu Jin; Honggang Guo; Jianzhou Wang; Aiyi Song. A Hybrid System Based on LSTM for Short-Term Power Load Forecasting. Energies 2020, 13, 6241 .

AMA Style

Yu Jin, Honggang Guo, Jianzhou Wang, Aiyi Song. A Hybrid System Based on LSTM for Short-Term Power Load Forecasting. Energies. 2020; 13 (23):6241.

Chicago/Turabian Style

Yu Jin; Honggang Guo; Jianzhou Wang; Aiyi Song. 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting." Energies 13, no. 23: 6241.

Journal article
Published: 26 September 2020 in Resources Policy
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Metal prices forecasting has practical implications for a variety of participants, including producers, consumers, governments and investors. However, metal prices forecasting is a complex and challenging issue widely discussed among researchers, due to the complexity and significant fluctuations observed in metal market. Most of present metal price forecasting models only emphasized deterministic prediction, usually ignored the uncertainty analysis and prediction, and eventually provided limited reference information. Under this background, in this research a novel hybrid system, consisting of distribution functions estimation, point and interval prediction, was proposed. More specifically, five distribution functions based on optimization algorithms were estimated to mine and analyze the traits of metal prices. With respect to the point prediction, an innovative hybrid forecasting models using variational mode decomposition and an optimized outlier-robust extreme learning machine by an optimization algorithm was established for metal prices prediction. Finally, based on the results of the distribution functions and point forecasting, interval forecasting was designed to provide predictive range, confidence level and the other uncertain information. Three metal prices series were taken as illustrated example to test the effective of the presented system and numerical results showed that the developed hybrid system can not only obtain higher prediction accuracy than that of the comparison models and also offer more valuable suggestions for enterprise administrators and investors in financial market.

ACS Style

Pei Du; Jianzhou Wang; Wendong Yang; Tong Niu. Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine. Resources Policy 2020, 69, 101881 .

AMA Style

Pei Du, Jianzhou Wang, Wendong Yang, Tong Niu. Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine. Resources Policy. 2020; 69 ():101881.

Chicago/Turabian Style

Pei Du; Jianzhou Wang; Wendong Yang; Tong Niu. 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine." Resources Policy 69, no. : 101881.

Journal article
Published: 13 August 2020 in IEEE Transactions on Industrial Informatics
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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.

ACS Style

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 Style

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 (99):1-1.

Chicago/Turabian Style

Tong 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.

Research article
Published: 06 August 2020 in Applied Soft Computing
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High levels of air pollution can severely affect the living environment and even endanger the human lives. To reduce air pollution concentrations, and warn the public regarding the occurrence of hazardous air pollutants, an accurate and reliable air pollutant forecasting model must be designed. However, previous studies had numerous deficiencies, such as ignoring the importance of predictive stability and poor initial parameters; these deficiencies significantly affected the air pollution prediction performance. Therefore, in this study a novel hybrid model is proposed to address these issues. Powerful data preprocessing techniques are applied to decompose the original time series into different modes from low frequency to high frequency, and a new multi-objective Harris hawks optimization algorithm is developed to tune the parameters of the extreme learning machine (ELM) model with high forecasting accuracy and stability for prediction air pollution. The optimized ELM model is then used to predict a time-series of air pollution. Finally, a scientific and robust evaluation system with several error criteria, benchmark models, and experiments conducted using twelve air pollutant concentration time series from three cities in China is designed to assess the presented hybrid forecasting model. The experimental results indicate that the proposed hybrid model can achieve a more stable and higher predictive performance than other models, and its superior prediction ability may aid in developing effective plans for mitigating air pollutant emissions and preventing the health issues caused by air pollution.

ACS Style

Pei Du; Jianzhou Wang; Yan Hao; Tong Niu; Wendong Yang. A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting. Applied Soft Computing 2020, 96, 106620 .

AMA Style

Pei Du, Jianzhou Wang, Yan Hao, Tong Niu, Wendong Yang. A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting. Applied Soft Computing. 2020; 96 ():106620.

Chicago/Turabian Style

Pei Du; Jianzhou Wang; Yan Hao; Tong Niu; Wendong Yang. 2020. "A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting." Applied Soft Computing 96, no. : 106620.

Journal article
Published: 27 June 2020 in Applied Soft Computing
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Jianzhou 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.

Journal article
Published: 21 April 2020 in Atmospheric Pollution Research
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The air quality index (AQI) can reflect the change of air quality in real time. It has linear characteristics, nonlinear and fuzzy features. However, a single model cannot fit the dynamic changes of AQI scientifically and reasonably. Therefore, this paper proposes a new dynamic ensemble forecasting system based on multi-objective intelligent optimization algorithm to forecast AQI, which has time-varying parameter weights and mainly contains three module: data preprocessing module, dynamic integration forecasting module and system evaluation module. In the data preprocessing module, the off-line frequency domain filtering approach is applied to identify and correct the outliers in the series. To better extract the series information and remove the random noise, the time series is decomposed into multi-level utilizing decomposition strategy and reconstructed. In the dynamic integration forecasting module, three hybrid models based on ARIMA, optimized extreme learning machine and fuzzy time series model, named as HCA, HCME and HCFL respectively, are used to forecast the reconstructed series and time varying parameters are employed to dynamically combine the forecasting results. In the system evaluation module, the accuracy of the system was tested by parameter test method and non-parametric test method respectively. The results demonstrate that the proposed dynamic integrated model is not only superior to other comparison models in forecasting accuracy, but also provides strong technical support for air quality forecasting and treatment.

ACS Style

Hongmin Li; Jianzhou Wang; Hufang Yang. A novel dynamic ensemble air quality index forecasting system. Atmospheric Pollution Research 2020, 11, 1258 -1270.

AMA Style

Hongmin Li, Jianzhou Wang, Hufang Yang. A novel dynamic ensemble air quality index forecasting system. Atmospheric Pollution Research. 2020; 11 (8):1258-1270.

Chicago/Turabian Style

Hongmin Li; Jianzhou Wang; Hufang Yang. 2020. "A novel dynamic ensemble air quality index forecasting system." Atmospheric Pollution Research 11, no. 8: 1258-1270.

Journal article
Published: 13 March 2020 in IEEE Access
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Wind speed forecasting takes a significant place in electric system owing to the fact that it has significant influence on operation efficiency and economic benefits. Aimming at improving forecast performance, a substantial number of wind speed prediction models have been proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this study, a novel forecasting system is proposed consisting of three modules: data preprocessing module, individual forecasting module and weight optimization module, which effectively achieve better forecasting ability. For data preprocessing and individual forecasting module, more regular sequences are obtained by decomposition technology, and association features are extracted by deep learning algorithm in prediction module. In the weight optimized module, the combination method base on the multi-objective optimization algorithm and nonnegative constraint theory are used to improve the prediction effectiveness. The combination model successfully exceeds the limits of individual predicton models and comparatively improves prediction accuracy. The effectiveness of the developed combination system is evaluated by 10-min wind speed in Penglai, China. The experiment results indicate that proposed forecasting system is better than other traditional forecasting models on three real wind speed datasets indeed.

ACS Style

Xiaohui He; Ying Nie; Hengliang Guo; Jianzhou Wang. Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting. IEEE Access 2020, 8, 51482 -51499.

AMA Style

Xiaohui He, Ying Nie, Hengliang Guo, Jianzhou Wang. Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting. IEEE Access. 2020; 8 (99):51482-51499.

Chicago/Turabian Style

Xiaohui He; Ying Nie; Hengliang Guo; Jianzhou Wang. 2020. "Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting." IEEE Access 8, no. 99: 51482-51499.

Journal article
Published: 11 March 2020 in Journal of Cleaner Production
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With the haze pollution occurred frequently in recent years, establishing an effective air quality early-warning system has become the top priority. Nowadays, researchers have provided numerous methods for air quality early-warning. However, the majority of studies ignores the significance of data preprocessing and air quality evaluation while designing an early-warning system, which leads to poor forecasting performance and insufficient information. A novel hybrid air quality early-warning system that consists of three modules: data preprocessing, forecasting and air quality evaluation module is designed in this paper. Aiming at extract chaotic characteristics of raw data, a new hybrid data preprocessing strategy is firstly developed to construct a more stable series of pollutant data for forecasting. Then a multi-objective grasshopper optimization algorithm is adopted in order to enhance the forecasting capability of accuracy and stability in the forecasting module. Moreover, a fuzzy evaluation module of air quality is proposed to provide comprehensive results for the system. Through the eighteen data sets’ experimental process, the results and discussions indicate that not only the forecasting method achieves higher accuracy and stronger stability than other comparison models, but also the evaluation module provides sufficient air quality information, which forms a scientific guidance to decision-makers against air pollution.

ACS Style

Ying Wang; Jianzhou Wang; Zhiwu Li. A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China. Journal of Cleaner Production 2020, 260, 121027 .

AMA Style

Ying Wang, Jianzhou Wang, Zhiwu Li. A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China. Journal of Cleaner Production. 2020; 260 ():121027.

Chicago/Turabian Style

Ying Wang; Jianzhou Wang; Zhiwu Li. 2020. "A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China." Journal of Cleaner Production 260, no. : 121027.

Journal article
Published: 13 February 2020 in IEEE Access
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Wind speed forecasting is an essential procedure in electric grid dispatching. Short-term wind speed forecasting can be a great challenge and an intractable issue in increasing wind energy output and guaranteeing power safety. The current wind speed forecasting models are based on a single model, which is generally an artificial intelligence model or a statistical forecasting method. However, these models cannot perform well in all cases. An effective combined model is proposed in this paper, and this model includes four parts: weight coefficient optimization calculation based on the nonpositive constraint combination theory, singular-spectrum analysis, combined forecasting and discussion of results. The developed model can decrease the negative influences of the component models and maximize the advantages of each component model. To evaluate the forecasting accuracy of our proposed model, ten minutes of wind speed data from Shandong Peninsula, China, were used as test cases. It is clearly demonstrated that the developed combined strategy outperforms the individual forecasting methods in terms of forecast performance and stability.

ACS Style

Tongji Guo; Lifang Zhang; Zhenkun Liu; Jianzhou Wang. A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation. IEEE Access 2020, 8, 33039 -33059.

AMA Style

Tongji Guo, Lifang Zhang, Zhenkun Liu, Jianzhou Wang. A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation. IEEE Access. 2020; 8 (99):33039-33059.

Chicago/Turabian Style

Tongji Guo; Lifang Zhang; Zhenkun Liu; Jianzhou Wang. 2020. "A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation." IEEE Access 8, no. 99: 33039-33059.

Journal article
Published: 24 January 2020 in Expert Systems with Applications
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Tong 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.

Journal article
Published: 14 January 2020 in IEEE Access
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ACS Style

He Bo; Ying Nie; Jianzhou Wang. Electric Load Forecasting Use a Novelty Hybrid Model on the Basic of Data Preprocessing Technique and Multi-Objective Optimization Algorithm. IEEE Access 2020, 8, 13858 -13874.

AMA Style

He Bo, Ying Nie, Jianzhou Wang. Electric Load Forecasting Use a Novelty Hybrid Model on the Basic of Data Preprocessing Technique and Multi-Objective Optimization Algorithm. IEEE Access. 2020; 8 ():13858-13874.

Chicago/Turabian Style

He Bo; Ying Nie; Jianzhou Wang. 2020. "Electric Load Forecasting Use a Novelty Hybrid Model on the Basic of Data Preprocessing Technique and Multi-Objective Optimization Algorithm." IEEE Access 8, no. : 13858-13874.

Journal article
Published: 30 December 2019 in Processes
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Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, which results in poor forecast. In this research, we developed a combined wind speed forecasting strategy that includes several components: data pretreatment, optimization, forecasting, and assessment. The developed system remedies some deficiencies in traditional single models and markedly enhances wind speed forecasting performance. To evaluate the performance of this combined strategy, 10-min wind speed sequences gathered from large wind farms in Shandong province in China were adopted as a case study. The simulation results show that the forecasting ability of our proposed combined strategy surpasses the other selected comparable models to some extent. Thus, the model can provide reliable support for wind power generation scheduling.

ACS Style

Yao Dong; Lifang Zhang; Zhenkun Liu; Jianzhou Wang. Integrated Forecasting Method for Wind Energy Management: A Case Study in China. Processes 2019, 8, 35 .

AMA Style

Yao Dong, Lifang Zhang, Zhenkun Liu, Jianzhou Wang. Integrated Forecasting Method for Wind Energy Management: A Case Study in China. Processes. 2019; 8 (1):35.

Chicago/Turabian Style

Yao Dong; Lifang Zhang; Zhenkun Liu; Jianzhou Wang. 2019. "Integrated Forecasting Method for Wind Energy Management: A Case Study in China." Processes 8, no. 1: 35.

Journal article
Published: 02 December 2019 in IEEE Access
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ACS Style

Lifang Zhang; Yao Dong; Jianzhou Wang. Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy. IEEE Access 2019, 7, 176000 -176023.

AMA Style

Lifang Zhang, Yao Dong, Jianzhou Wang. Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy. IEEE Access. 2019; 7 ():176000-176023.

Chicago/Turabian Style

Lifang Zhang; Yao Dong; Jianzhou Wang. 2019. "Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy." IEEE Access 7, no. : 176000-176023.

Journal article
Published: 02 December 2019 in IEEE Access
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In the development of the wind power industry, short-term wind speed forecasting is necessary, and many researchers have made substantial efforts to establish wind speed prediction models. However, realizing the accurate prediction of wind speeds remains a challenging task. The current prediction models do not consider the preprocessing of the data, and each model has various shortcomings. Considering the disadvantages of the available models, in this paper, an advanced combined forecasting system is applied that utilizes a data preprocessing strategy and parameter optimization strategy to obtain accurate prediction values. The proposed prediction system employs linear and nonlinear models that can take into account the characteristics of wind speed sequences, successfully combine the advantages of various single models, and yield accurate and stable prediction values. Finally, according to the experimental analysis and discussion, the proposed combined prediction system outperforms the compared models in prediction. In conclusion, the powerful combined prediction model provides a feasible scheme for wind power prediction.

ACS Style

He Bo; Xinsong Niu; Jianzhou Wang. Wind Speed Forecasting System Based on the Variational Mode Decomposition Strategy and Immune Selection Multi-Objective Dragonfly Optimization Algorithm. IEEE Access 2019, 7, 178063 -178081.

AMA Style

He Bo, Xinsong Niu, Jianzhou Wang. Wind Speed Forecasting System Based on the Variational Mode Decomposition Strategy and Immune Selection Multi-Objective Dragonfly Optimization Algorithm. IEEE Access. 2019; 7 (99):178063-178081.

Chicago/Turabian Style

He Bo; Xinsong Niu; Jianzhou Wang. 2019. "Wind Speed Forecasting System Based on the Variational Mode Decomposition Strategy and Immune Selection Multi-Objective Dragonfly Optimization Algorithm." IEEE Access 7, no. 99: 178063-178081.

Journal article
Published: 19 September 2019 in Energies
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In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error).

ACS Style

Danxiang Wei; Jianzhou Wang; Kailai Ni; Guangyu Tang; Wei; Wang; Ni; Tang. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting. Energies 2019, 12, 3588 .

AMA Style

Danxiang Wei, Jianzhou Wang, Kailai Ni, Guangyu Tang, Wei, Wang, Ni, Tang. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting. Energies. 2019; 12 (18):3588.

Chicago/Turabian Style

Danxiang Wei; Jianzhou Wang; Kailai Ni; Guangyu Tang; Wei; Wang; Ni; Tang. 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting." Energies 12, no. 18: 3588.