| Project/Area Number |
15560734
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Energy engineering
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
YOKOYAMA Ryohei Osaka Prefecture University, Graduate School of Engineering, Associate Professor, 工学研究科, 助教授 (70158385)
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| Co-Investigator(Kenkyū-buntansha) |
GAMOU Satoshi Osaka Prefecture University, Graduate School of Engineering, Research Associate, 工学研究科, 助手 (30254428)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2005: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2004: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 2003: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Keywords | Energy Management / Distributed Energy System / Prediction / Operation / Control / Optimization / Simulation |
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| Research Abstract |
In recent years, distributed energy systems have been installed increasingly for the purpose of reducing energy consumption and environmental effect. In general, seasonal and hourly variations in energy demands are larger in the commercial sector than in the industrial one. Therefore, operational management is a very important subject to promote energy saving positively in the commercial sector.
The objectives of this research are to develop integrated methods for the prediction of energy demands, the optimal operational planning of energy systems, and the optimal control of energy systems as an optimal operational management system to promote energy saving positively, and to apply them to an existing energy system. The following are the main results obtained by the research :
1.Prediction methods based on the time series analysis and neural network have been developed to predict energy demands. They have been revised so that they are suitable for operational planning by minimizing not o
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nly the errors between predicted and measured values but also the variations in predicted values. In addition, their accuracy has been improved by combining them with the prediction of some factors, such as air temperature, which affect energy demands.
2.An optimal operational planning method has been developed by using a nonlinear programming method which enables one to derive a global quasi-optimal solution, named "Modal Trimming Method." It has been combined hierarchically with an optimal control method based on the model predictive control. These methods enable one to rationally determine the set values of controlled variables and the optimal values of manipulating variables.
3.A general-purpose system for dynamic simulation of energy systems has been developed. An existing heat supply system for air conditioning has been studied as an example, and its dynamic simulation model has been created. The dynamic simulation has been conducted under certain conditions by the aforementioned hierarchical combination of the optimal operational planning and control, and simulation results have been validated. Less
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