With the increasing application of small distributed renewable energy systems in household power supplies, when a large number of distributed renewable energy power generation systems are connected to the power grid, the time-varying output power of small solar energy, wind turbines, etc. Disaggregation and analysis of regional household electricity and renewable energy power supply systems connected to household electricity will help grid companies to conduct power dispatch management. This paper employed a two-way two-layer Long Short-term Memory deep learning network with improved input form to perform non-intrusive load disaggregation on household power with solar panels, which can monitor the load status of household electrical appliances and the output power of solar power generation system in real time. The power situation provides a decision basis for optimizing the response value of household energy demand and improving the demand of the power system from the response management level. The combined dataset from UK-DALE and kaggle’solar panel power generation data is adopted to train and test the proposed improved Long Short-term Memory network. The test results show that the proposed algorithm is applied to the household electric load disaggregation with solar panels, with high accuracy and reliability.

中文翻译：

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基于改进的长短期记忆网络的太阳能电池板家庭电力负荷分解

随着小型分布式可再生能源系统在家庭电源中的应用越来越多，当大量分布式可再生能源发电系统接入电网时，小型太阳能、风力发电机等的时变输出功率。分析区域家庭用电和与家庭用电连接的可再生能源供电系统，有助于电网公司进行电力调度管理。本文采用改进输入形式的双向两层长短期记忆深度学习网络对太阳能电池板的家用电源进行非侵入式负载分解，可以监测家用电器的负载状态和输出功率。太阳能发电系统的实时信息。用电情况从响应管理层面为优化家庭能源需求响应值、提高电力系统需求提供决策依据。采用来自 UK-DALE 和 kaggle 太阳能电池板发电数据的组合数据集来训练和测试所提出的改进的长短期记忆网络。测试结果表明，该算法应用于太阳能电池板家庭用电负荷分解，具有较高的准确性和可靠性。