Multiple power-based building energy management system for efficient management of building energy
- Authors
- Yoon, Seok-Ho; Kim, Seung-Yeon; Park, Geon-Hee; Kim, Yi-Kang; Cho, Choong-Ho; Park, Byung-Hun
- Issue Date
- 10월-2018
- Publisher
- ELSEVIER
- Keywords
- Building energy management system; Multiple power; Renewable energy; Energy saving system; Energy forecasting; Adaptive energy consumption prediction
- Citation
- SUSTAINABLE CITIES AND SOCIETY, v.42, pp.462 - 470
- Indexed
- SCIE
SCOPUS
- Journal Title
- SUSTAINABLE CITIES AND SOCIETY
- Volume
- 42
- Start Page
- 462
- End Page
- 470
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/73014
- DOI
- 10.1016/j.scs.2018.08.008
- ISSN
- 2210-6707
- Abstract
- As the significance of increased power consumption in modern society is emphasized, the need for technology to enhance energy efficiency is also increasing. The power consumption of buildings comprises a large proportion of the total energy consumption and systematic methods are needed in order to manage it effectively. This paper proposes a multiple power-based building energy management system (MPBEMS) for the efficient management of building energy. MPBEMS means a system that integrates and manages multiple power produced in one or more ways. The analysis of the big-data based power usage measured in different types of buildings utilizes the power distribution method between multiplepower sources. The paper also suggests and verifies an energy prediction model for efficient building energy management, called the Adaptive Energy Consumption Prediction (AECP) algorithm. This paper proposes a building energy management method using an energy prediction model and analyzes its efficiency by applying it to actual buildings. The result of the efficiency analysis using the proposed system shows an annual electricity rate reduction efficiency of 5%.
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Collections - College of Science and Technology > Department of Computer Convergence Software > 1. Journal Articles
- Graduate School > Department of Computer and Information Science > 1. Journal Articles
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