Optimal Design of Energy System Based on the Forecasting Data with Particle Swarm Optimization

Yamin Yan, Haoran Zhang, Jianqin Zheng and Yongtu Liang

A chapter in Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems from IntechOpen

Abstract: Renewable energy source has developed rapidly and attracted considerable attention. The integration of renewable energy into the energy supply chain requires precise forecast of the output of energy supply chain, thereby reducing energy resource waste and greenhouse gas emissions. In this study, a coupled model system is developed to forecast energy supply chain for the design optimization of distributed energy system, which can be divided into two parts. In the first part, long short-term memory (LSTM) and particle swarm optimization algorithm (PSO) contribute to energy supply chain forecast considering time series, and particle swarm optimization is used to optimize the parameters of the long short-term memory model to improve the forecast accuracy. Results show that the mean absolute error and root mean squared error are 8.7 and 16.3 for the PSO-LSTM model, respectively. In the second part, the forecast results are used as input of the distributed energy system to further optimize the design and operation schemes, so as to achieve the coupling optimization of forecast and design. Finally, a case study is carried out to verify the effectiveness of the proposed method.

Keywords: coupled model system; forecast; design optimization; renewable energy system (search for similar items in EconPapers)
JEL-codes: Q20 Q40 (search for similar items in EconPapers)
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:204674

DOI: 10.5772/intechopen.90007

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