Abstract
With the development of the times, modern people’s life is more and more inseparable from electric energy, which leads to the rapid growth of the pace of power marketization, rapid development at the same time, users’ demand for related service quality is also increasing. Because of all kinds of reasons, the development direction of distribution enterprises is moving towards convenience, safety, flexibility and stability. In the daily management of the building, to ensure the uninterrupted power supply is an important factor affecting the service quality of the building. Whenever there is a power outage, it will cause unpleasant impact and experience to users and tourists, bring fright to employees, and cause unnecessary trouble to daily management. Therefore, this paper introduces the demand side response, which is based on an important interactive mode for the active distribution of power grid. According to the implementation of a series of incentive mechanisms, guide users to actively cooperate with the operation of the distribution network management. In recent years, according to the corresponding measures for energy consumption, adjusting the energy consumption mode can prevent the random fluctuation caused by intermittent renewable energy, distribute the demand side resources on demand, control the distributed power supply more effectively and operate the energy storage equipment more flexibly. According to the analysis and summary of the demand response of the system, this paper discusses the examples of short-term load forecasting and demand side response, which shows that the experiment of load forecasting model in this paper has high accuracy.
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Fang, X., Wang, Y., Xia, L., Yang, X., Lai, Y. (2021). Short Term Load Forecasting Model of Building Power System with Demand Side Response Based on Big Data of Electrical Power. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_55
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DOI: https://doi.org/10.1007/978-3-030-62743-0_55
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