Building Energy Management for Demand Response Using Kernel Lifelong Learning

• Main Authors: Sunyong Kim, Rami Mowakeaa, Seung-Jun Kim, Hyuk Lim
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Smart grid; demand response; building energy management; multi-task learning; lifelong learning; kernel-based learning
• Online Access: https://ieeexplore.ieee.org/document/9079854/

Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy management is a critical component for the DR portfolio. In this study, DR control policies for lighting and air-conditioner systems for the individual spaces in buildings are proposed. The policies are designed to achieve the energy reduction amount specified in the DR request while minimizing the user discomfort. A significant challenge is to cope with the uncertainty of various environmental factors such as the solar illuminance and ambient temperature, as well as the psycho-economic factors such as the energy usage preferences of the occupants. We employ a data-driven machine learning approach to tackle this challenge. Our novel idea is to take advantage of the structural similarity of the control policies across the spaces in a lifelong multi-task learning framework. To accommodate significant nonlinearity in efficient policies, a kernel-based learning approach is pursued. The dual decomposition method is employed to relax the constraint coupled across the spaces, which allows solving the overall learning problem via a series of unconstrained subproblems. The efficacy of the proposed method is verified by numerical experiments based on semi-real data sets.