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兰州大学 [9]
兰州理工大学 [5]
西安交通大学 [4]
中南大学 [4]
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山东大学 [3]
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期刊论文 [27]
会议论文 [10]
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Indian Ocean Basin Warming in 2020 Forced by Thermocline Anomalies of the 2019 Indian Ocean Dipole
期刊论文
JOURNAL OF CLIMATE, 2024, 卷号: 37, 期号: 1, 页码: 145-163
作者:
Wang, Jing
;
Zhang, Shouwen
;
Jiang, Hua
;
Yuan, Dongliang
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/04/07
Indian Ocean
Oceanic waves
ENSO
Seasonal forecasting
Data assimilation
Tropical variability
A novel two-stage seasonal grey model for residential electricity consumption forecasting
期刊论文
ENERGY, 2022, 卷号: 258, 页码: 18
作者:
Du, Pei
;
Guo, Ju'e
;
Sun, Shaolong
;
Wang, Shouyang
;
Wu, Jing
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2023/02/07
Electricity consumption forecasting
Grey model
Seasonal factor
Error correction strategy
Daily tourism demand forecasting: the impact of complex seasonal patterns and holiday effects
期刊论文
CURRENT ISSUES IN TOURISM, 2022, 页码: 20
作者:
Liu, Yunhao
;
Feng, Gengzhong
;
Chin, Kwai-Sang
;
Sun, Shaolong
;
Wang, Shouyang
收藏
  |  
浏览/下载:205/0
  |  
提交时间:2022/06/21
Tourism demand forecasting
daily tourism demand
holiday effects
seasonal patterns
FB Prophet
Differing roles of base and fast flow in ensemble seasonal streamflow forecasting: An experimental investigation
期刊论文
JOURNAL OF HYDROLOGY, 2020, 卷号: 591, 页码: 12
作者:
Huang, Zeqing
;
Zhao, Tongtiegang
;
Liu, Yang
;
Zhang, Yongyong
;
Jiang, Tao
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2021/03/15
Streamflow forecasting
Seasonal flow
Base flow
Fast flow
Forecast skill
Spatial characteristics of deep-developed boundary layers and numerical simulation applicability over arid and semi-arid regions in Northwest China
期刊论文
Atmosphere, 2019, 卷号: 10, 期号: 4
作者:
Ma, Minjin
;
Tan, Ziyuan
;
Ding, Fan
;
Chen, Yue
;
Yang, Yi
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2020/11/14
Arid regions
Boundary layer flow
Numerical models
Weather forecasting
Arid and semi-arid regions
Boundary layer heights
European centre for medium-range weather forecasts
NorthWest China
Parameterization schemes
Seasonal variation
Spatial characteristics
Weather research forecasts
Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks
期刊论文
Energy Conversion and Management, 2018, 卷号: 155, 页码: 188-200
作者:
Liu, Hui
;
Mi, Xiwei
;
Li, Yanfei*
收藏
  |  
浏览/下载:41/0
  |  
提交时间:2019/12/03
Wind speed forecasting
Wavelet packet decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Artificial Neural Network
ANN
Artificial Neural Network
AR
Auto Regressive
ARIMA
Auto Regressive Integrated Moving Average
BA
Bat Algorithm
BP
Back-propagation Neural Network
CEEMDAN
Complete Ensemble Empirical Mode Decomposition
CG
Conjugate Gradient
CLSFPA
Flower Pollination Algorithm with Chaotic Local Search
CNN
Convolutional Neural Network
CS
Compressive Sensing
CSA
Cuckoo Search Algorithm
EEMD
Ensemble Empirical Mode Decomposition
ESM
Exponential Smoothing Method
FA
Firefly Algorithm
FAC
First-order Adaptive Coefficient
GA
Genetic Algorithm
GRNN
General Regression Neural Network
HBSA
Hybrid Backtracking Search Algorithm
HWM
Holt-Winters Model
IMFs
Intrinsic Mode Functions
IS
Input parameter Selection
KF
Kalman filter
LSSVM
Least Square Support Vector Machine
MAE
Mean Absolute Error
MAPE
Mean Absolute Percentage Error
MLP
Multilayer Perceptron Neural Network
MOBA
Multi Objective Bat Algorithm
NNCT
No Negative Constraint Theory
NSGA
Non-dominated Sorting Genetic Algorithm
OVMD
Optimized Variational Mode Decomposition
PSO
Particle Swam Optimization
PSOSA
Particle Swarm Optimization based on Simulated Annealing
PSR
Phase Space Reconstruction
RBF
Radial Basis Function Neural Network
RMSE
Root Mean Square Error
SAC
Second-order Adaptive Coefficient
SAM
Seasonal Adjustment Method
SDA
Secondary Decomposition Algorithm
SEA
Seasonal Exponential Adjustment
SOM
Self-Organizing feature Maps
SSA
Singular Spectrum Analysis
SVR
Support Vector Regression
VMD
Variational Mode Decomposition
v-SVM
v-Support Vector Machine
WD
Wavelet Decomposition
WPD
Wavelet Packet Decomposition
An improved seasonal rolling grey forecasting model using a cycle truncation accumulated generating operation for traffic flow
期刊论文
Applied Mathematical Modelling, 2017, 卷号: 51, 页码: 386-404
作者:
Xiao, Xinping
;
Yang, Jinwei*
;
Mao, Shuhua
;
Wen, Jianghui
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2019/12/04
Seasonal rolling grey forecasting model(Rolling SGM(1,1))
Cycle truncation accumulated generating operation(CTAGO)
Limited data
New information priority
Traffic flow forecasting
Forecasting monthly energy production of small hydropower plants in ungauged basins using grey model and improved seasonal index
期刊论文
JOURNAL OF HYDROINFORMATICS, 2017, 卷号: 19, 页码: 993-1008
作者:
Cheng, Chun-Tian
;
Miao, Shu-Min
;
Luo, Bin
;
Sun, Yong-Jun
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/03
grey model
improved seasonal index
monthly energy production forecasting
small hydropower plant
ungauged basins
A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting
期刊论文
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2017, 卷号: 32, 期号: 5, 页码: 3447-3459
作者:
Ma, Tao
;
Wang, Fen
;
Wang, Jianzhou
;
Yao, Yukai
;
Chen, Xiaoyun
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2019/11/15
Support vector machines
seasonal autoregressive integrated moving average
combining optimization model
electric load forecasting
particle swarm optimization algorithm
Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting
期刊论文
APPLIED SOFT COMPUTING, 2016, 卷号: 49, 页码: 663-675
作者:
Yang, Yi
;
Chen, Yanhua
;
Wang, Yachen
;
Li, Caihong
;
Li, Lian
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2017/01/16
Forecasting
Backpropagation
Electric power utilization
Energy resources
Evolutionary algorithms
Fuzzy inference
Fuzzy neural networks
Optimization
Adaptive network based fuzzy inference system
ANFIS
Back propagation neural networks
Combined forecasting
diff-SARIMA
Electricity demand forecasting
Optimization algorithms
Seasonal autoregressive integrated moving averages
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