A framework for building energy management system with residence mounted photovoltaic

作     者：Chellaswamy C Ganesh Babu R Vanathi A Chellaswamy C;Ganesh Babu R;Vanathi A

作者机构：Department of Electronics and Communication EngineeringKings Engineering CollegeChennaiIndia Department of Electronics and Communication EngineeringSRM TRP Engineering CollegeTiruchirappalliIndia Department of Electronics and Communication EngineeringRajalakshmi Institute of TechnologyChennaiIndia 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2021年第14卷第4期

页      面：1031-1046页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 081304[工学-建筑技术科学] 0813[工学-建筑学] 0814[工学-土木工程] 

主　　题：building energy management convolution neural network photovoltaic coordinated scheduling 

摘      要：Efficient utilization of a residential photovoltaic (PV) array with grid connection is difficult due to power fluctuation and geographical dispersion. Reliable energy management and control system are required for overcoming these obstacles. This study provides a new residential energy management system (REMS) based on the convolution neural network (CNN) including PV array environment. The CNN is used in the estimation of the nonlinear relationship between the residence PV array power and meteorological datasets. REMS has three main stages for the energy management such as forecasting, scheduling, and real functioning. A short term forecasting strategy has been performed in the forecasting stage based on the PV power and the residential load. A coordinated scheduling has been utilized for minimizing the functioning cost. A real-time predictive strategy has been used in the actual functioning stage to minimize the difference between the actual and scheduled power consumption of the building. The proposed approach has been evaluated based on real-time power and meteorological data sets.