Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation
暂无分享,去创建一个
Xianchun Tan | Zhen Liu | Jinchai Lin | Kaiwei Zhu | Jenny Lieu | Zhen Liu | Kaiwei Zhu | Xianchun Tan | J. Lieu | Jinchai Lin
[1] Paola Zuccolotto,et al. Pricing strategies for Italian red wine , 2011 .
[2] Randall Spalding-fecher,et al. Electricity supply and demand scenarios for the Southern African power pool , 2017 .
[3] Christian A. Gueymard,et al. Minimum redundancy – Maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series , 2017 .
[4] C. Kang,et al. Input-output table of electricity demand and its application , 2010 .
[5] Yongxiu He,et al. Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin , 2017 .
[6] Usman Zia,et al. The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning , 2015 .
[7] Nicholas F. Marshall,et al. Extracting geography from trade data , 2016, 1607.05235.
[8] Hyojoo Son,et al. Short-term forecasting of electricity demand for the residential sector using weather and social variables , 2017 .
[9] H. Zha,et al. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..
[10] Shian-Chang Huang,et al. Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting , 2009, Expert Syst. Appl..
[11] Xiao Zhong,et al. Forecasting daily stock market return using dimensionality reduction , 2017, Expert Syst. Appl..
[12] Hai-ying He,et al. Electricity demand price elasticity in China based on computable general equilibrium model analysis , 2011 .
[13] Zhengyan Shao,et al. On electricity consumption and economic growth in China , 2017 .
[14] 张振跃,et al. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment , 2004 .
[15] W. Nuttall,et al. A dynamic simulation of low-carbon policy influences on endogenous electricity demand in an isolated island system , 2017 .
[16] Shaolong Sun,et al. A new dynamic integrated approach for wind speed forecasting , 2017 .
[17] A. Torres,et al. A multi-agent system providing demand response services from residential consumers , 2015 .
[18] Luiz Fernando Loureiro Legey,et al. Electricity consumption forecasting in Brazil: A spatial econometrics approach , 2017 .
[19] Bin Chen,et al. Can equalization of public services narrow the regional disparities in China? A spatial econometrics approach , 2017 .
[20] Qian Ma,et al. Factors influencing CO2 emissions in China's power industry: Co-integration analysis , 2013 .
[21] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[22] Humberto Verdejo,et al. Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile , 2017 .
[23] Julián Pérez-García,et al. Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain , 2016 .
[24] V. Bianco,et al. Electricity consumption forecasting in Italy using linear regression models , 2009 .
[25] Suhonoa. Long-term electricity demand forecasting of Sumatera system based on electricity consumption intensity and Indonesia population projection 2010-2035 , 2015 .
[26] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[27] Rosalie L. Tung. Opportunities and Challenges Ahead of China's “New Normal” , 2016 .
[28] Jiang Lin,et al. Economic rebalancing and electricity demand in China , 2016 .
[29] Eva González Romera,et al. Forecasting of the electric energy demand trend and monthly fluctuation with neural networks , 2007, Comput. Ind. Eng..
[30] Radu Zmeureanu,et al. Forecasting electric demand of supply fan using data mining techniques , 2016 .
[31] José Francisco Moreira Pessanha,et al. ScienceDirect Information Technology and Quantitative Management ( ITQM 2015 ) Forecasting long-term electricity demand in the residential sector , 2015 .
[32] Lei Zhu,et al. Emission path planning based on dynamic abatement cost curve , 2016, Eur. J. Oper. Res..
[33] Diana Domanska,et al. Handling high-dimensional data in air pollution forecasting tasks , 2016, Ecol. Informatics.
[34] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[35] Georgios Sermpinis,et al. Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds , 2017, Eur. J. Oper. Res..
[36] V. Bianco,et al. Linear Regression Models to Forecast Electricity Consumption in Italy , 2013 .
[37] Chi-Keung Woo,et al. How much have electricity shortages hampered China's GDP growth? , 2013 .
[38] Carlos Maté,et al. Electric power demand forecasting using interval time series: A comparison between VAR and iMLP , 2010 .
[39] Eva Gonzalez-Romera,et al. Monthly electric demand forecasting with neural filters , 2013 .
[40] B. Zhang,et al. Energy implications of China's regional development: New insights from multi-regional input-output analysis , 2017 .
[41] Lu Zongxiang,et al. Multiple Data Source Dimensionality Reduction Pretreatment Used in Ultra-Short Term Wind Resource Forecast , 2015 .
[42] Dongxiao Niu,et al. Incorporating the influence of China's industrial capacity elimination policies in electricity demand forecasting , 2017 .
[43] Zhen Liu,et al. Multi-agent based experimental analysis on bidding mechanism in electricity auction markets , 2012 .
[44] Maria Grazia De Giorgi,et al. Photovoltaic forecast based on hybrid PCA-LSSVM using dimensionality reducted data , 2016, Neurocomputing.
[45] Tanveer Ahmad,et al. Utility companies strategy for short-term energy demand forecasting using machine learning based models , 2018 .