基于深度学习算法的社区微网家庭用电模式建模与仿真

Modeling and Simulation of Household Electricity Consumption in Community Microgrid Based on Deep Learning Algorithm

摘要: 随着能源消费的升级以及控制系统的进步，微网系统逐渐步入了人们的视野，改变了传统的电力供应模式。本文从社区微网入手，首先结合小波分解和CNN-Attention-BiLSTM深度学习算法模型，对光伏发电功率进行了预测；接着使用了遗传算法的优化手段，对电器的启停时段进行了优化；最后提出了三种典型的家庭用电模式，并进行建模与仿真，为后续进行社区微网家庭的用电研究提供了理论基础。

Abstract: With the upgrading of energy consumption and the progress of control system, microgrid system has gradually stepped into people’s vision and changed the traditional power supply mode. In this paper, the power of photovoltaic power generation is predicted by combining wavelet decomposition and CNN-Attention-BiLSTM model. Then, genetic algorithm is used to optimize the start-stop time of the electric appliance. Finally, three typical household electricity consumption modes are proposed, and the modeling and simulation are carried out, which provides a theoretical basis for the subsequent research on community microgrid household electricity consumption.