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Novel analysis-forecast system based on multi-objective optimization for air quality index

JOURNAL OF CLEANER PRODUCTION 2019年 208卷 1365-1383页

作者： Li, Hongmin Wang, Jianzhou Li, Ranran Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sch Software Fac Engn & Informat Technol Sydney NSW Australia

The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Air quality index forecasting Analysis-forecast system Feature selection multi-objective optimization algorithm Forecasting accuracy and stability

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A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting

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APPLIED ENERGY 2019年 235卷 1205-1225页

作者： Yang, Wendong Wang, Jianzhou Niu, Tong Du, Pei Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China

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.

关键词： Electricity price forecasting system Dual decomposition strategy multi-objective optimization algorithm Forecasting accuracy and stability

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Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network

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ENVIRONMENTAL POLLUTION 2021年 274卷 116429-116429页

作者： Wang, Jianzhou Li, Hongmin Yang, Hufang Wang, Ying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Owing to the high nonlinearity and noise in the air quality index (AQI), tackling the uncertainties and fuzziness in the forecasting process is still a prevalent problem. Therefore, this study developed an intelligent hybrid air-quality forecasting system based on feature selection and a modified evolving interval type-2 quantum fuzzy neural network (eIT2QFNN), which provides accurate air-quality forecasting information by considering climate influencing factors. The main contributions of this study are as follows. The optimal input structure of the model is determined by the proposed second-stage feature-selection model, which can better extract the influencing variables and remove redundant information. Moreover, a novel multi-objective chaotic Bonobo optimizer algorithm is proposed to improve the eIT2QFNN. The modified eIT2QFNN implements AQI prediction by considering the importance of influencing variables that can cope with the uncertainties and fuzziness in the forecasting process. Finally, the Diebold-Mariano and modified Diebold-Mariano tests are employed to evaluate the performance of the proposed system. The experimental results demonstrate that our proposed system significantly improves the modeling performance in terms of high accuracy and compact structure, and can thus serve as an effective tool for air-quality management. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Air quality forecasting Feature selection Interval type-2 fuzzy sets Fuzzy neural network multi-objective optimization algorithm

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Axial force identification of space grid structural members using particle swarm optimization method

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JOURNAL OF BUILDING ENGINEERING 2020年 32卷

作者： Ding, Beidou Liu, Jinling Huang, Zhenhua Li, Xian Wu, Xiaosuo Cai, Liping China Univ Min & Technol Jiangsu Key Lab Environm Impact & Struct Safety E Xuzhou 221116 Jiangsu Peoples R China Univ North Texas Dept Mech Engn Denton TX 76207 USA

Space grid structures are popularly used in large-span civil structures. Identification of the axial internal forces and boundary conditions of the space grid structural members is very important. However, in the engineering practice, the boundary conditions of such members are not ideally rigid or pinned. They are sometimes semi-rigid conditions. Besides, direct measurements of the axial forces and the boundary rigidities of in-situ space grid structural members are difficult to make. Therefore, in this study, a Particle Swarm optimization (PSO) algorithm-based axial force and boundary rigidity identification method is proposed for space grid structural members using multi-order natural frequencies, which can be easily obtained from in-situ tests. The theoretical background of the proposed method is discussed in detail, including the coupling relationship between the axial force and natural frequency and the PSO algorithm. The applicability and accuracy of the proposed method are validated through experimental tests and comparative analyses.

关键词： Particle swarm optimization (PSO) Natural vibration frequency multi-objective optimization algorithm Axial force identification Grid structure

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A new combined model based on multi-objective salp swarm optimization for wind speed forecasting

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APPLIED SOFT COMPUTING 2020年 92卷 106294-106294页

作者： Cheng, Zishu Wang, Jiyang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

Wind energy as the representative renewable energy sources attracted the global attention and wind power plays a significant role in power system. Thus, wind speed forecasting is highly critical in wind power grid management. The short-term wind speed prediction can effectively support power grid-management to reduce wind curtailments. In the past, lots of researches had often considered how to enhance the accuracy or stability in short wind speed forecasting. Nevertheless, just focus on one criterion is the inability to build an effective predictive system. In this paper, a novel combined forecasting system was proposed and effectively applied to address the issue of wind speed prediction while obtaining high precision and strong stability simultaneously at the same time. Four ANNs (artificial neural networks) were combined by the optimal weighting coefficients determined by MSSO (multi-objective salp swarm optimizer) in this system and data decomposition and denoising are included in the data preprocessing stage. The multi-objective optimization algorithm overcomes the weakness of the single-objective optimization algorithm that can only achieve one criterion. It can simultaneously optimize accuracy and stability. The 10-minute wind speed data of three data sets of Penglai, China were selected for multi-step forecasting to evaluate the effectiveness of the proposed combined model. And experimental results show that the proposed model not only achieves excellent precision and stability but also outperforms other proposed combined models. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Combined model Wind speed forecasting multi-objective optimization algorithm Data preprocessing technique

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Point and interval prediction for non-ferrous metals based on a hybrid prediction framework

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RESOURCES POLICY 2021年 73卷 1页

作者： Wang, Jianzhou Niu, Xinsong Zhang, Linyue Lv, Mengzheng Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

As a bulk product with huge international circulation, non-ferrous metals have frequent and severe price fluctuations, which have attracted great attention from academia and industry. However, the non-ferrous metal price series has strong volatility and nonlinear characteristics, which makes the realization of high-precision forecasts still a difficult and challenging problem. In this paper, a hybrid point prediction system is constructed to achieve high precision point prediction results. Moreover, uncertain forecasts contain more information and can provide market participants with more detailed guidance, but uncertainty forecasting is often ignored in practice. Based on the high precision point prediction system, the uncertainty prediction framework is proposed in this paper. Different distribution functions were used to analyze the distribution characteristics of the data, and the uncertainty prediction at different levels was successfully realized according to point prediction results. To verify prediction performance of the proposed prediction framework, multiple contrast experiments have been carried out using the London Metal Exchange daily future prices of Zinc, Copper and Lead. The empirical results show that the developed prediction framework has better predictive power for non-ferrous metals price prediction.

关键词： Non-ferrous metals Interval prediction Artificial intelligence multi-objective optimization algorithm Distribution function

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Novel NSGA-II optimized LSTM and GRU models for short-term forecasting of residential heating load

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Energy and Buildings 2025年 344卷

作者： Priyam Deka Moisés Cordeiro-Costas Raquel Pérez-Orozco Michał Chabiński Andrzej Szlęk Department of Thermal Technology Silesian University of Technology Konarskiego 22 44-100 Gliwice Poland CINTECX Universidade de Vigo Rúa Maxwell s/n 36310 Vigo Spain

Heating load forecasting in the building sector is crucial for energy conservation, management, and regularization. This article presents two deep learning models based on long short-term memory (LSTM) and gated recurrent network (GRU) for 1- hour ahead heating load forecasting of a residential building. The hyperparameters (number of hidden layers, number of nodes in each hidden layer, learning rate, batch size and window size) of the models were optimized by employing non-dominated sorting genetic algorithm- II (NSGA-II). This work aims to compare the hyperparameter optimization of the two models by NSGA-II and compare their forecasting performances. The performances of the models were evaluated and compared using root mean square error (RMSE), coefficient of variation of root mean square error (CV(RMSE)), normalized mean bias error (nMBE) and mean absolute percentage error (MAPE). The models were tested on two different datasets including outdoor and indoor temperatures as the only environmental input features. Despite using limited environmental input features in the data, both models obtained high accuracies of forecasting. The NSGA-II- GRU model was 40.55% faster than the NSGA-II- LSTM model during training, indicating more efficient usage of the available hyperparameter resources. Also, the former outperformed the later in terms of MAPE by 8.86 % on test data 1 and 22.58 % on test data 2, respectively.

关键词： Adam Adaptive moment estimation ACO Ant colony optimization BA Batch size BBO Biogeography based optimization CNN Convolutional Neural network CV(RMSE) Coefficient of variation of root mean square error DL Deep learning DRNN Deep recurrent neural network DT Decision tree ELM Extreme learning machine GA Genetic algorithm GRU Gated recurrent unit HL Heating load kNN k- nearest neighbor LSTM Long short- term memory LR Learning rate MAPE Mean absolute percentage error MBE Mean bias error MLP multi-layer perceptron MOOA multi-objective optimization algorithm MSE Mean squared error NARX-GA Nonlinear autoregressive external genetic algorithm nMBE Normalized mean bias error NoHL Number of hidden layers NoN Number of nodes NSGA-II Non-dominated genetic algorithm- II IT Indoor temperature OT Outdoor temperature PBIL Population- based incremental learning PSO Particle swarm optimization ReLU Rectified linear unit RF Random forest RMSE Root mean squared error R 2 Coefficient of determination SAE Stack autoencoder SOOA Single-objective optimization algorithm SVR Support vector regression WS Window size LSTM GRU NSGA-II Deep learning Heating load forecasting Hyperparameter optimization

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A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting

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APPLIED ENERGY 2019年 238卷 368-383页

作者： Hao, Yan Tian, Chengshi Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China

With the fast growth of wind power penetration into the electric grid, wind power forecasting plays an increasingly significant role in the secure and economic operation of power systems. Although there have been numerous studies concerning wind power forecasting, most of them have failed to make the best of the information implied in the error value, focused only on simple error correction, adopted a simple ensemble method to aggregate the predictions of each component, and considered improving only forecasting accuracy. Recognizing these issues, a novel two-stage forecasting model based on the error factor, a nonlinear ensemble method and the multi-objective grey wolf optimizer algorithm is proposed for wind power forecasting. More specially, in stage I, the extreme learning machine optimized by the multi-objective grey wolf optimizer is used to forecast the components decomposed by variational mode decomposition, and an error prediction model based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to predict forecast errors;also, a novel nonlinear ensemble method based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to integrate all the components and forecast error values in stage II. Three real-world wind power datasets collected from Canada and Spain are introduced to demonstrate the forecasting performance of the developed model. The forecasting results reveal that the proposed model is superior to all the other considered models in terms of both accuracy and stability and thus can be a useful tool for wind power forecasting.

关键词： Wind power forecasting Two-stage forecasting model multi-objective optimization algorithm Extreme learning machine

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A novel hybrid model for short-term wind power forecasting

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APPLIED SOFT COMPUTING 2019年 80卷 93-106页

作者： Du, Pei Wang, Jianzhou Yang, Wendong Niu, Tong Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hybrid forecasting model Wavelet neural network multi-objective optimization algorithm Wind power forecasting

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Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

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IEEE ACCESS 2019年 7卷 176000-176023页

作者： Zhang, Lifang Dong, Yao Wang, Jianzhou Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Appl Stat Res Ctr Nanchang 330013 Jiangxi Peoples R China

Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting.

关键词： Wind speed forecasting multi-objective optimization algorithm ensemble strategy forecasting accuracy

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