This paper deals with short-term load forecasting for energy management systems in small and middle-sized buildings. Unlike existing studies that focus on the forecasting accuracy, this study examined some candidate load forecasting methods with regard to convenience and cost-efficiency. Three-year energy use patterns of office buildings were identified according to calendar data and weather data. Simple forecasting equations were derived based on regression analyses using linear, seasonal linear, and quadratic models. The quadratic model was found most appropriate for Korea’s climate with four distinct seasons. The forecasting equation derived from the energy consumption of 2017 was verified by comparing the energy consumption forecast obtained by applying the weather data of 2018 to the equation and the actual energy consumption of 2018. This study will be using our simple load forecasting system that does not need to install sensors in all the target buildings but only in some representative buildings of similar shapes and calculate energy consumption forecasts for each target building by using the least possible data.

Phase noise, a unique characteristic of electrical appliances, for non‐intrusive load monitoring (NILM) is proposed. NILM is a technique, which can distinguish the power consumption of an individual electrical appliance, without requiring the installation of sensors on the appliance, reducing the number of sensors required in an energy management system. It utilises the previously‐recorded power supply characteristics of individual appliances and measures the changes in the bus voltage or current, which provide power supply to several appliances, thereby distinguishing the operational appliances without direct measurement. Considering electrical appliances that can be commonly found in a laboratory, such as an electric drill, soldering iron, and light bulb, the authors found that, when each was turned on, the current at an offset of 5 Hz from the centre frequency of 60 Hz was 61.4, 18.8, and 6.2 mA for each appliance, respectively. As each appliance was turned on in the sequence, the bus current providing electrical energy to all three appliances was measured to be 60.4, 11.1, and 5.7 mA, respectively. Thus, it was experimentally confirmed that the current at frequencies near the centre frequency, i.e. the phase noise, can be used for NILM.

Lithium-ion batteries, known for their high efficiency and high energy output, have gained significant attention as energy storage devices. Monitoring the state of charge through battery management systems plays a crucial role in enhancing the safety and extending the lifespan of lithium-ion batteries. In this paper, we propose a state-of-charge estimation method to overcome the limitations of the traditional open-circuit voltage method and electrochemical impedance spectroscopy. We verified changes in the shape of the voltage relaxation curve based on battery impedance through simulations and analyzed the impact of individual impedance on the voltage relaxation curve using differential equations. Based on this relationship, we estimated the impedance from the battery’s voltage relaxation curve through curve fitting and subsequently estimated the state of charge using a pre-established lookup table. In addition, we introduced a partial curve-fitting method to reduce the estimation time compared to the existing open-circuit voltage method and confirmed the trade-off relationship between the estimation time and estimation error.

This paper discusses the economic operation strategy of the energy hub, which is being established in South Korea. The energy hub has five energy conversion devices: a turbo expander generator, a normal fuel cell, a fuel cell with a hydrogen outlet, a small-scale combined heat and power device, and a photovoltaic device. We are developing the most economically beneficial operation strategy for the operators who own the hub, without making any systematic improvements to the energy market. First, sixteen conversion efficiency matrices can be achieved by turning each device (except the PV) on or off. Next, even the same energy must be divided into different energy flows according to price. The energy flow is controlled to obtain the maximum profit, considering the internal load of the energy hub and the price fluctuations of the energy market. Using our operating strategy, the return on investment period is approximately 9.9 years, which is three years shorter than that without the operating strategy.

Multi-region integrated multi-energy system (MES) combines electricity, heat, and hydrogen networks to improve energy efficiency and reduce carbon emissions. Planning such interregional systems remains difficult because of nonlinear energy interactions, spatial–temporal coupling, and various regulatory constraints. This paper reviews optimization-based planning methods for multi-region MES and explains how recent studies manage system coordination, uncertainty, and trade-offs among energy carriers. The reviewed literature was selected from major databases—Scopus, Web of Science, and IEEE Xplore—to reflect the development of optimization approaches such as mixed-integer programming (MILP/MINLP), metaheuristic algorithms, model predictive control, and data-driven frameworks. The literature spans from 2009 to 2025, capturing both early foundational studies and the most recent advances in optimization-based MES planning. The analysis summarizes the main progress, technical limitations, and remaining challenges related to policy integration, investment interaction, and regional coordination. The paper concludes that future research should establish scalable optimization methods that link technical design, economic performance, and policy goals in integrated energy planning.