基于能源互联网的办公建筑源荷一体式监控系统研究

Study on Office Building Source-Load Integrated Monitoring System Based on Internet Energy

研究一种针对办公建筑的源荷一体化监控系统,以机器自学习法对用户用电需求进行日前预测及超短期预测。基于太阳辐照度、温度、风速等实时监测数据对可再生能源发电量进行预测。根据以上预测对建筑内可再生能源、蓄能技术与常规能源开展优化调控。针对热泵供冷供热及太阳能光伏发电建筑,提出了一种优化控制策略,在保证建筑内用户舒适度的前提下有效减少用户用电负荷及可再生能源发电对电网的冲击,同时提高能源网内能源的高效利用性和可持续性。

The article studies source-load integrated monitoring system for office building based on machine learning method to give user electric power consumption day-ahead forecast and ultra-short-term forecast. According to online monitoring data, such as solar irradiance, temperature, air speed to carry out prediction on renewable energy power generation quantity. Implementing optimization and control on renewable energy, storage technology and conventional energy inside building based on above prediction. Focused on heat pump cooling and heating with solar photovoltaic power generation building, the author puts forward optimized control strategy to improve effective utilization and sustainability on energy network and guarantee user comfortability inside building to reduce user electric power consumption load and renewable power generation impact on power grid.