Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series
Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation sys...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/174203 |
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| author | Liu Hai Song Yong Du Qingfu |
| author_facet | Liu Hai Song Yong Du Qingfu |
| author_sort | Liu Hai |
| collection | DOAJ |
| description | Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction. |
| format | Article |
| id | doaj-art-03672e656ef44d058edd347e8c9f28f3 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-03672e656ef44d058edd347e8c9f28f32025-02-03T01:07:18ZengWileyJournal of Control Science and Engineering1687-52491687-52572015-01-01201510.1155/2015/174203174203Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time SeriesLiu Hai0Song Yong1Du Qingfu2School of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, ChinaTheoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction.http://dx.doi.org/10.1155/2015/174203 |
| spellingShingle | Liu Hai Song Yong Du Qingfu Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series Journal of Control Science and Engineering |
| title | Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series |
| title_full | Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series |
| title_fullStr | Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series |
| title_full_unstemmed | Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series |
| title_short | Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series |
| title_sort | power forecasting of combined heating and cooling systems based on chaotic time series |
| url | http://dx.doi.org/10.1155/2015/174203 |
| work_keys_str_mv | AT liuhai powerforecastingofcombinedheatingandcoolingsystemsbasedonchaotictimeseries AT songyong powerforecastingofcombinedheatingandcoolingsystemsbasedonchaotictimeseries AT duqingfu powerforecastingofcombinedheatingandcoolingsystemsbasedonchaotictimeseries |