Abstract
In response to the long-term instability of energy supply and demand and the challenges posed by climate change, there has been a recognition of the importance of utilizing renewable energy as a sustainable energy source to reduce primary energy consumption. Consequently, the building sector, a significant contributor to energy consumption, is currently exploring various energy-saving technologies that incorporate renewable energy. As the application of renewable energy sources becomes more widespread, there is a growing need for a Building Energy Management System (BEMS) capable of optimizing production and consumption through the efficient integration of operation and control. This paper presents the development and demonstration of a BEMS designed for the integrated operation and control of electrical energy and thermal energy in buildings. In addition, the developed BEMS was verified through empirical operation in two buildings to reduce the building’s average power usage, peak power usage, and energy purchase costs.
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References
Hyang-in J et al (2013) Application status and improvement direction of renewable energy management system installed in building. J Korean Inst Archit Eng 29(2):227–234
Martirano L et al (2018) Aggregation of users in a residential/commercial building managed by a building energy management system (BEMS). IEEE Trans Ind Appl 55(1):26–34
Stropnik R et al (2019) Improved thermal energy storage for nearly zero energy buildings with PCM integration. Sol Energy 190(15):420–426
Boodi A et al (2018) Intelligent systems for building energy and occupant comfort optimization: a state of the art review and recommendations. Energies 11(10):2604
Kim D et al (2023) Short-term load forecasting for commercial building using convolutional neural network (CNN) and long short-term memory (LSTM) network with similar day selection model. J Electr Eng Technol 18:4001–4009
Jang M et al (2022) Analysis of residential consumers’ attitudes toward electricity tariff and preferences for time-of-use tariff in Korea. IEEE Access 10:26965–26973
Brockwell PJ, Davis RA (1996) Introduction to time series and forecasting. Springer, Cham
Charytoniuk W et al (1998) Nonparametric regression based short-term load forecasting. IEEE Trans Power Syst 13(3):725–730
Lamedica R et al (1996) A neural network based technique for short-term forecasting of anomalous load periods. IEEE Trans Power Syst 11(4):1749–1756
Moon H-J (2013) Recent research trends on building energy management system (BEMS). Korean Inst Facil Eng 42(9):54–63
Barchi G et al (2019) Predictive energy control strategy for peak switch and shifting using BESS and PV generation applied to the retail sector. Electronics 8(5):526
Kim T et al (2022) Short-term residential load forecasting using 2-step SARIMAX. J Electr Eng Technol 17:751–759
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Jeong, S., Wi, YM. Research on Development and Implementation of Integrated Energy Management System for Buildings. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01870-3
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DOI: https://doi.org/10.1007/s42835-024-01870-3