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[1]Li Y.F., Shi H.P., Liu H.*: A Hybrid Model for River Water Level Forecasting: Cases of XiangJiang River and YuanJiang River, China. Journal of Hydrology, 2020: 124934. (影响因子: 4.405, JCR-Q1, 中科院Top期刊)
[2]Chen C., Liu H.*: Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection. Energy Conversion and Management, 2020, 206: 112492. (影响因子: 7.181, JCR-Q1, 中科院Top期刊)
[3]Xu Y.N., Liu H.*: Spatial ensemble prediction of hourly PM2.5 concentrations around Beijing railway station in China. Air Quality, Atmosphere & Health, 2020. (影响因子: 2.297, , JCR-Q2)
[4]Liu H.*, Long Z.H., Duan Z., Shi H.P.: A new model using multiple feature clustering and neural networks for forecasting hourly PM2.5 concentrations and its applications in China. Engineering, 2020. (影响因子: 4.568, JCR-Q1, 中科院1区)
[5]Liu H.*, Yu C.M., Yu C.Q., Wu H.P., Chen C.: A Novel Axle Temperature Forecasting Method Based on Decomposition, Reinforcement Learning Optimization and Neural Network. Advanced Engineering Informatics, 2020, 44: 101089. (影响因子: 3.772, JCR-Q1, 中科院1区)
[6]Liu H.*, Duan Z., Chen C.: A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2.5 concentrations. Information Sciences, 2020, 516: 266-292. (影响因子: 5.524, JCR-Q1, 中科院1区)
[7]Liu H.*, Duan Z.: A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection. Applied Energy, 2020, 261: 114367. (影响因子: 8.426, JCR-Q1, 中科院Top期刊)
[8]Liu H.*, Chen C.: Prediction of outdoor PM2.5 concentrations based on a three-stage hybrid neural network model. Atmospheric Pollution Research, 2020, 11(3): 469-481. (影响因子: 2.918, JCR-Q2)
[9]Xu Y.N., Liu H.*, Long Z.H.: A distributed computing framework for wind speed big data forecasting on Apache Spark. Sustainable Energy Technologies and Assessments, 2020, 37: 100582. (影响因子: 3.456, JCR-Q2)
[10]Liu H.*, Duan Z.: Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation. Energy Conversion and Management, 2020, 203: 112265. (影响因子: 7.181, JCR-Q1, 中科院Top期刊)
[11]Liu H.*, Yu C.M., Wu H.P., Chen C., Wang Z.Q.: An improved non-intrusive load disaggregation algorithm and its application. Sustainable Cities and Society, 2020, 53: 101918. (影响因子: 4.624, JCR-Q1)
[12]Liu H.*, Long Z.: An Improved Deep Learning Model for Predicting Stock Market Price Time Series. Digital Signal Processing, 2020. (影响因子: 2.792, JCR-Q2)
[13]Xu Y.N., Liu H.*, Duan Z.: A novel hybrid model for multi-step daily AQI forecasting driven by air pollution big data. Air Quality, Atmosphere & Health, 2020, 13: 197-207. (影响因子: 2.297, JCR-Q2)
[14]Liu H.*, Duan Z., Wu H.P., Li Y.F., Dong S.Y.: Wind Speed Forecasting Models based on Data Decomposition, Feature Selection and Group Method of Data Handling Network. Measurement, 2019, 148: 106971. (影响因子: 2.791, JCR-Q2, IMEKO会刊)
[15]Liu H.*, Duan Z., Chen C., Wu H.P.: A Novel Two-stage Deep Learning Wind Speed Forecasting Method with Adaptive Multiple Error Corrections and Bivariate Dirichlet Process Mixture Model. Energy Conversion and Management, 2019, 199: 111975. (影响因子: 7.181, JCR-Q1, 中科院Top期刊)
[16]Duan Z., Liu H.*: An evolution-dependent multi-objective ensemble model of vanishing moment with adversarial auto-encoder for short-term wind speed forecasting in Xinjiang wind farm, China. Energy Conversion and Management, 2019, 198: 111914. (影响因子: 7.181, JCR-Q1, 中科院Top期刊)
[17]Liu H.*, Jin K.R., Duan Z.: Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China. Atmospheric Pollution Research, 2019, 10(5): 1588-1600. (影响因子: 2.918, JCR-Q2)
[18]Liu H.*, Chen C.: Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction. Applied Energy, 2019, 254: 113686. (影响因子: 8.426, JCR-Q1, 中科院Top期刊)
[19]Liu H.*, Chen C., Lv X.W., Wu X., Liu M.: Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 2019, 195: 328-345. (影响因子: 7.181, JCR-Q1, 中科院Top期刊)
[20]Liu H.*, Mi X.W., Li Y.F., Duan Z., Xu Y.N.: Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression. Renewable Energy, 2019, 143: 842-854. (影响因子: 5.439, JCR-Q1, 中科院Top期刊)
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