Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China

Energy ◽  
2021 ◽  
pp. 123024
Author(s):  
Meng Wang ◽  
Wei Wang ◽  
Lifeng Wu
Keyword(s):  
Energy Consumption ◽  
Forecasting Model ◽  
Multivariate Forecasting

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

Grey Forecasting Model for CO2 Emissions

2012 ◽  
Vol 518-523 ◽  
pp. 1664-1668 ◽  
Author(s):  
Guo Lin Bao ◽  
Hong Qi Hui
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Energy Consumption ◽  
Greenhouse Gases ◽  
Carbon Intensity ◽  
Forecasting Model ◽  
Chinese Government ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Average Residual

CO2 is the most frequently implicated in global warming among the various greenhouse gases associated with climate change. Chinese government has been taking serious measures to control energy consumption to reduce CO2 emissions. This study applies the grey forecasting model to estimate future CO2 emissions and carbon intensity in Shijiazhuang from 2010 until 2020. Forecasts of CO2 emissions in this study show that the average residual error of the GM(1, 1) is below 1.5%. The average increasing rate of CO2 emissions will be about 6.71%; and the carbon intensity will be 2.10 tons/104GDP until year 2020. If the GDP of Shijiazhuang city can be quadruple, the carbon intensity will be half to the 2005 levels until 2020. The findings of this study provide a valuable reference with which the Shijiazhuang government can formulate measures to reduce CO2 emissions by curbing the unnecessary the consumption of energy.

scholarly journals Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1331 ◽  
Author(s):  
Peng Jiang ◽  
Jun Dong ◽  
Hui Huang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Power Consumption ◽  
Extreme Learning Machine ◽  
Activation Function ◽  
Forecasting Model ◽  
Fossil Energy ◽  
Mode Decomposition ◽  
Bacterial Foraging Algorithm ◽  
Learning Machine

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

A Novel Time Series Forecasting Approach Considering Data Characteristics

2014 ◽  
Vol 5 (3) ◽  
pp. 46-53
Author(s):  
Ling Tang ◽  
Shuai Wang ◽  
Lean Yu
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Total Energy ◽  
Time Series Data ◽  
Prediction Performance ◽  
Time Series Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Total Energy Consumption ◽  
Forecasting Models

A novel time series forecasting approach with consideration of inner knowledge hidden in data, in terms of data characteristics, is proposed. In the proposed methodology, the main data characteristics hidden in the observed time series data are first explored; and according to the data characteristics, suitable forecasting models are formulated to improve prediction performance. For illustration, the proposed methodology is used to predict Chinese total social consumption and total energy consumption. The empirical results show the forecasting model considering data characteristics outperforms other popular forecasting models ignoring data characteristics, which further implies that data characteristics exploration is an important and necessary step in forecasting and the proposed methodology can be used as a promising approach for time series forecasting.

Automated Feature Engineering Method in the Problem of Forecasting Energy Consumption

2021 ◽  
Vol 27 (10) ◽  
pp. 550-560
Author(s):  
K. V. Danilov ◽  
◽  
S. V. Maltseva ◽  
◽  
Keyword(s):  
Energy Consumption ◽  
Electricity Consumption ◽  
Engineering Method ◽  
Model Construction ◽  
Computational Experiments ◽  
Forecasting Model ◽  
Feature Engineering ◽  
Russian Regions

The automated feature engineering method in the problem of forecasting energy consumption is considered. The algorithm of the method and the scheme of the forecasting model construction are stated. The proposed approach was tested on data about electricity consumption in Russian regions. The results of the computational experiments carried out using the described method demonstrate an increase in the efficiency of the developed forecasting model and improvement of accuracy.

The analysis of the energy consumption of Chinese manufacturing based on the combination forecasting model

2015 ◽  
Vol 5 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Tianxiang Yao ◽  
Wenrong Cheng
Keyword(s):  
Energy Consumption ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Single Model ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Combination Forecasting ◽  
Combination Forecasting Model

Purpose – The purpose of this paper is to find a method that has high precision to forecast the energy consumption of China’s manufacturing industry. The authors hope the predicted data can provide references to the formulation of government’s energy strategy and the sustained growth of economy in China. Design/methodology/approach – First, the authors respectively make use of regression prediction model and grey system theory GM(1,1) model to construct single model based the data of 2001-2010, analyze the advantages and disadvantages of single prediction models. The authors use the data of 2011 and 2012 to test the model. Second, the authors propose combination forecasting model of manufacturing’s energy consumption in China by using standard variance to allocate the weight. Finally, this model is applied to forecast China’s manufacturing energy consumption during 2013-2016. Findings – The result shows that the combination model is a better one with higher accuracy; the authors can take the model as an effective tool to predict manufacturing’s energy consumption in China. And the energy consumption of China’s manufacturing industry continued to show a steady incremental trend. Originality/value – This method takes full advantages of the effective information reflected by the single model and improves the prediction accuracy.

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