Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods
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Qifa Xu | Yaoyao He | Q. Xu | Yaoyao He | Yaya Zheng | Yaya Zheng | Qifa Xu
[1] Xiping Wang,et al. A Hybrid Neural Network and ARIMA Model for Energy Consumption Forcasting , 2012, J. Comput..
[2] Yanpeng Cai,et al. An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan , 2017 .
[3] Omid Nematollahi,et al. Experimental investigation of energy consumption and environmental impact of adaptive defrost in domestic refrigerators , 2016 .
[4] Andreas Svensson,et al. Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes , 2018 .
[5] Derek W. Bunn,et al. Modeling the UK electricity price distributions using quantile regression , 2016 .
[6] R. Koenker,et al. Robust Tests for Heteroscedasticity Based on Regression Quantiles , 1982 .
[7] Xiantao Liu,et al. Prediction of long-term gas load based on particle swarm optimization and gray neural network model , 2017 .
[8] Haiyan Li,et al. Probability density forecasting of wind power using quantile regression neural network and kernel density estimation , 2018 .
[9] Harun Kemal Ozturk,et al. Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach , 2004 .
[10] H. Ozturk,et al. Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey , 2008 .
[11] Joakim Widén,et al. Review on probabilistic forecasting of photovoltaic power production and electricity consumption , 2018 .
[12] B. Moreno,et al. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors , 2016 .
[13] James W. Taylor. A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns , 2000 .
[14] Eduardo Gomes Salgado,et al. Forecasting number of ISO 14001 certifications in the Americas using ARIMA models , 2017 .
[15] Shanlin Yang,et al. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function , 2016 .
[16] Parag Sen,et al. Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization , 2016 .
[17] Zheng-Xin Wang,et al. Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models , 2017 .
[18] T. Yee. Quantile regression via vector generalized additive models , 2004, Statistics in medicine.
[19] Z. Yumurtacı,et al. Electric Energy Demand of Turkey for the Year 2050 , 2004 .
[20] Alex J. Cannon. Quantile regression neural networks: Implementation in R and application to precipitation downscaling , 2011, Comput. Geosci..
[21] Serhat Kucukali,et al. Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach , 2010 .
[22] R. Koenker,et al. Regression Quantiles , 2007 .
[23] Mohan Liu,et al. Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China , 2016 .
[24] G. Aydin. Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections , 2014 .
[25] A. Hussain,et al. Forecasting electricity consumption in Pakistan: the way forward , 2016 .
[26] Fernando Luiz Cyrino Oliveira,et al. Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods , 2018 .
[27] Tao Lu,et al. Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach , 2015 .
[28] S. Fan,et al. Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.
[29] Abbas Khosravi,et al. Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .
[30] Abbas Rohani,et al. Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models , 2018 .
[31] Carlo Renno,et al. ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building , 2016 .
[32] Chuanglin Fang,et al. Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces , 2014 .
[33] Tian Xia,et al. Estimation of demand response to energy price signals in energy consumption behaviour in Beijing, China , 2014 .
[34] Jolanta Szoplik,et al. Forecasting of natural gas consumption with artificial neural networks , 2015 .
[35] Jinxing Che,et al. An incremental electric load forecasting model based on support vector regression , 2016 .
[36] Tze Ling Ng,et al. Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities , 2017 .
[37] Shuo Wang,et al. Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory , 2017 .
[38] Derek W. Bunn,et al. Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models , 2016 .
[39] M. Hadi Amini,et al. A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon , 2017 .
[40] Saeed-Reza Sabbagh-Yazdi,et al. Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data , 2017 .
[41] Chun-Ping Chang,et al. The impact of energy consumption on economic growth: Evidence from linear and nonlinear models in Taiwan , 2007 .
[42] Guohua Cao,et al. Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting , 2016 .
[43] Yu Bengong. A Power Load Probability Density Forecasting Method Based on RBF Neural Network Quantile Regression , 2013 .
[44] Mahendran Shitan,et al. Forecasting with univariate time series models : a case of export demand for Peninsular Malaysia’s moulding and chipboard. , 2010 .
[45] Jeyraj Selvaraj,et al. Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming , 2017 .
[46] Amir H. Gandomi,et al. Building energy consumption forecast using multi-objective genetic programming , 2018 .
[47] M. Saberi,et al. Improved Estimation of Electricity Demand Function by Integration of Fuzzy System and Data Mining Approach , 2006, 2006 IEEE International Conference on Industrial Technology.
[48] Coşkun Hamzaçebi,et al. Forecasting the annual electricity consumption of Turkey using an optimized grey model , 2014 .
[49] A. Bennouna,et al. Energy needs for Morocco 2030, as obtained from GDP-energy and GDP-energy intensity correlations , 2016 .
[50] M. A. Rafe Biswas,et al. Regression analysis for prediction of residential energy consumption , 2015 .
[51] Nadia S. Ouedraogo. Africa energy future: Alternative scenarios and their implications for sustainable development strategies , 2017 .
[52] Feng Liu,et al. A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting , 2017 .
[53] Yi Zeng,et al. Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network , 2017 .
[54] T J Cole,et al. Smoothing reference centile curves: the LMS method and penalized likelihood. , 1992, Statistics in medicine.