A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction
Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/301032 |
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| author | Xiaojun Guo Sifeng Liu Lifeng Wu Lingling Tang |
| author_facet | Xiaojun Guo Sifeng Liu Lifeng Wu Lingling Tang |
| author_sort | Xiaojun Guo |
| collection | DOAJ |
| description | Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k) model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. |
| format | Article |
| id | doaj-art-cb40ddf9ebd54973893c09dad252aeb4 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-cb40ddf9ebd54973893c09dad252aeb42025-02-03T06:13:54ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/301032301032A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption PredictionXiaojun Guo0Sifeng Liu1Lifeng Wu2Lingling Tang3College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USAEnergy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k) model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.http://dx.doi.org/10.1155/2014/301032 |
| spellingShingle | Xiaojun Guo Sifeng Liu Lifeng Wu Lingling Tang A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction The Scientific World Journal |
| title | A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction |
| title_full | A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction |
| title_fullStr | A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction |
| title_full_unstemmed | A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction |
| title_short | A Grey NGM(1,1,k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction |
| title_sort | grey ngm 1 1 k self memory coupling prediction model for energy consumption prediction |
| url | http://dx.doi.org/10.1155/2014/301032 |
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