基于人工情感LSTM算法的智慧家庭能量管理

Smart home energy management based on artificial emotion LSTM algorithm

随着中国社会城镇化的进一步深入和城市的进一步发展,电力资源逐步出现紧缺情况。不少地区在用电高峰期间由于城市用电超负荷而不得不进行区域性的拉闸限电以缓解城市供电的压力。在这种情况下,用户自主性地管理和节约电能就显得尤为重要。为了使家庭用户可以自主管理电能的使用,实现电网与用户之间的双向互动,同时保证需求侧响应(DR)的执行,提出一种基于人工情感长短期记忆(AELSTM)网络算法的智慧家庭能量管理的控制方法。该方法由人工情感深度神经网络(AEDNN)和长短期记忆(LSTM)网络构成。结合这2部分可以做到人性化实时监测和管理家庭用电情况。

With the further deepening of China's social urbanization and the development of cities,there are shortages of social resources,especially power resources.Overload of electricity consumption forces power rationing in some districts to ease the pressure of municipal power supply.In this context,it is particularly important for users to manage and save energy autonomously.In order to enable users of household electricity to independently manage the usage of electric energy,realize two-way interaction between the power grid and user ends,and ensure the implementation of demand-side response,a smart home energy management method based on artificial emotion long short-term memory(AELSTM)network algorithm is proposed.This method is mainly composed of artificial emotional deep neural network(AEDNN)and long-short-term memory(LSTM)network.The combination of the two components allows humanized real-time monitoring and management of household electricity consumption.