Building energy management system

• Main Author: Wee, Herman Ding Xian
• Other Authors: So Ping Lam
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/78310
• Institution: Nanyang Technological University

Demand response is proposed as a strategy to reduce energy consumptions especially with the integration of renewable energy sources. This paper demonstrates the implementation of several demand response strategies with advanced signal processing techniques, neural network and optimization algorithms to achieve substantial cost savings. Visualized with LabVIEW and powered by MATLAB, this project integrates green technology with a specially designed prototype armed with an advanced wireless Building Energy Management System (BEMS). The designed BEMS will perform modeling on a series of trained data for prediction and real-time forecast. The prediction horizon will supplement several demand response strategies. Phase-balancing is employed to improve power efficiency. Lagrange multipliers with second-order cost function is introduced for optimal load distribution during load shedding and load shifting. In addition, Lagrange multipliers will be used to optimize the integration of clean technology to maximize energy transfer and increase revenue from the sale of excess battery supply at high prices. To increase performance of data modeling and prediction, neural network is extensively used to train the data.