@ARTICLE{Han_Guozheng_Load_2023,
 author={Han, Guozheng and Tan, Shujuan and Zhang, Zihan},
 volume={vol. 72},
 number={No 2},
 journal={Archives of Electrical Engineering},
 pages={429-441},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences},
 abstract={For a solar photovoltaic power system on a university campus, the electricity generated by the system meets the campus load, and the extra electricity is delivered to the grid. Generally, the price of the photovoltaic system is cheaper than that of the utility power system. The full use of solar electricity can reduce the electricity cost of the school. The deep belief network is used to predict solar photovoltaic generation and electricity load, and the gap is found. According to the gap, the power loads on the campus are adjusted to improve the utilization rate of solar power generation. Through the practical application of Changqing Campus of Qilu University of Technology in China, it is found that the utilization rate of solar photovoltaic power generation effectively improved from 91.24% in 2017 to 98.16% in 2019, and the annual electricity is saved by 68 610 yuan (in 2019).},
 type={Article},
 title={Load regulation application of university campus based on solar power generation forecasting},
 URL={http://czasopisma.pan.pl/Content/127611/PDF-MASTER/art09_int.pdf},
 doi={10.24425/aee.2023.145418},
 keywords={load regulation, solar power generation forecast, photovoltaic generation},
}