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.

Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network

Kybernetes ◽  
2015 ◽  
Vol 44 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Zhou Cheng ◽  
Tao Juncheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Models ◽  
Combination Forecasting ◽  
Freight Volume ◽  
Convergence Performance ◽  
Combination Forecasting Model ◽  
Improved Pso

Purpose – To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. Design/methodology/approach – Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. Findings – Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. Originality/value – SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

scholarly journals Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhou Cheng ◽  
Chen XiYang
Keyword(s):  
Energy Consumption ◽  
Percentage Error ◽  
Forecasting Model ◽  
Absolute Percentage Error ◽  
Model Based ◽  
Combination Forecasting ◽  
Correlation Degree ◽  
Weight Method ◽  
Combination Forecasting Model ◽  
Error Weight

To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individual models (BP, GM (1, 1), triple exponential smoothing model, and polynomial trend extrapolation model) and combination forecasting models to predict China’s energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecasting model, it is more effective to evaluate the performance of forecasting model. Firstly, the forecasting model’s performances rank in line with ACD. Then ACD is firstly proposed to choose individual models for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based on ACD shows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China’s energy consumption will be up to 5.7988 billion tons of standard coal in 2018.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

The hybrid intelligent systems design using grey systems theory

2015 ◽  
Vol 5 (2) ◽  
pp. 194-205 ◽  
Author(s):  
Scarlat Emil ◽  
Virginia Mărăcine
Keyword(s):  
Systems Theory ◽  
Intelligent Systems ◽  
Explicit Knowledge ◽  
Intelligent System ◽  
Grey System Theory ◽  
Knowledge Flows ◽  
Content Type ◽  
Hybrid Intelligent Systems ◽  
Advantages And Disadvantages ◽  
Grey Systems Theory

Purpose – The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems. Design/methodology/approach – Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages. Findings – Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches. Originality/value – The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

Substation Bus Reactive Load Forecasting in Large Iron and Steel Enterprise

2012 ◽  
Vol 433-440 ◽  
pp. 6168-6174
Author(s):  
Li Mu ◽  
Jia Chuan Shi ◽  
Xian Quan Li
Keyword(s):  
Reactive Power ◽  
Load Forecasting ◽  
National Standard ◽  
Forecasting Model ◽  
Impact Loads ◽  
Optimization Strategy ◽  
Reactive Power Optimization ◽  
Iron And Steel ◽  
Combination Forecasting ◽  
Combination Forecasting Model

Impact loads in large iron and steel enterprise bring the power system reactive power impact, which makes the fluctuation of the system voltage, power factor and other parameters are out of the limitation of the national standard. Substation bus reactive load forecasting in large iron and steel enterprise can be introduced to determine reactive power optimization strategy and the switching of capacitors. In this paper, a combination forecasting model of quadratic self-adaptive exponential smoothing (QSES) model and converse exponential (CE) model has been proposed for substation bus reactive load forecasting. The numerical results in Jinan iron and steel Group show the application of this model is encouraging. Introduction

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