This paper analyzes the energy consumption situation in Beijing,based on the comparison of common energy consumption prediction methods.Here we use multiple linear regression analysis,grey prediction,BP neural net-wor...This paper analyzes the energy consumption situation in Beijing,based on the comparison of common energy consumption prediction methods.Here we use multiple linear regression analysis,grey prediction,BP neural net-work prediction,grey BP neural network prediction combined method,LSTM long-term and short-term memory network model prediction method.Firstly,before constructing the model,the whole model is explained theoretically.The advantages and disadvantages of each model are analyzed before the modeling,and the corresponding advantages and disadvantages of these models are pointed out.Finally,these models are used to construct the Beijing energy forecasting model,and some years are selected as test samples to test the prediction accuracy.Finally,all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019,and the relevant energy-saving opinions were given.展开更多
In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also ...In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also named COGM（1,1）, based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation＇s form. Then, original parameters are re- stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Finally, a numerical case and comparison with other grey prediction models are made to testify the new model＇s effectiveness, and the computational results show that the COGM（1,1） model has a better property and achieves higher precision. The new model is used to forecast China energy con- sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump- tion and production in future are predicted to decline.展开更多
本文基于能源平衡表的内在结构逻辑,在Log Mean Divisia指数分解法的基础上,建立拓展的能源强度指数分解方法,将单位GDP能耗指标变化分解为五个因素的效应(结构效应、部门强度效应、加工转换效应、输配效应以及终端比重效应),并应用这...本文基于能源平衡表的内在结构逻辑,在Log Mean Divisia指数分解法的基础上,建立拓展的能源强度指数分解方法,将单位GDP能耗指标变化分解为五个因素的效应(结构效应、部门强度效应、加工转换效应、输配效应以及终端比重效应),并应用这个方法对我国1991-2010年间的单位GDP能耗指标的变化进行实证分解。实际结果表明该期间单位GDP能耗的变化主要来自于产业部门强度效应,其它四个因素效应的作用相对较小,但即因素效应的作用随着时间有不同的变化。展开更多
基金supported by Research on Construction of Green Building Material Information Management Platform(Grant No.2016024).
文摘This paper analyzes the energy consumption situation in Beijing,based on the comparison of common energy consumption prediction methods.Here we use multiple linear regression analysis,grey prediction,BP neural net-work prediction,grey BP neural network prediction combined method,LSTM long-term and short-term memory network model prediction method.Firstly,before constructing the model,the whole model is explained theoretically.The advantages and disadvantages of each model are analyzed before the modeling,and the corresponding advantages and disadvantages of these models are pointed out.Finally,these models are used to construct the Beijing energy forecasting model,and some years are selected as test samples to test the prediction accuracy.Finally,all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019,and the relevant energy-saving opinions were given.
基金supported by the National Natural Science Foundation of China(710710777130106071371098)
文摘In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also named COGM（1,1）, based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation＇s form. Then, original parameters are re- stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Finally, a numerical case and comparison with other grey prediction models are made to testify the new model＇s effectiveness, and the computational results show that the COGM（1,1） model has a better property and achieves higher precision. The new model is used to forecast China energy con- sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump- tion and production in future are predicted to decline.
文摘本文基于能源平衡表的内在结构逻辑,在Log Mean Divisia指数分解法的基础上,建立拓展的能源强度指数分解方法,将单位GDP能耗指标变化分解为五个因素的效应(结构效应、部门强度效应、加工转换效应、输配效应以及终端比重效应),并应用这个方法对我国1991-2010年间的单位GDP能耗指标的变化进行实证分解。实际结果表明该期间单位GDP能耗的变化主要来自于产业部门强度效应,其它四个因素效应的作用相对较小,但即因素效应的作用随着时间有不同的变化。
