• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1843-1850
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• 2014

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

• Journal of the Korean Society of Physical Medicine
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• v.12 no.4
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• pp.93-103
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• 2017

PURPOSE: This study aimed to determine the effect of active trunk training in the sitting position on balance and energy consumption in early stroke patients. METHODS: The subjects of this study were twenty four early stroke patients were recruited and randomly divided into two groups; experimental group (n=12) and control group (n=12). The subjects in the experimental group participated in active trunk training (20 min) and conventional physical therapy (20 min) five times a week for four weeks. The control group conducted general balance exercise (20 min) and conventional physical therapy (20 min) five times a week for four weeks. The outcomes were assessed using the Berg balance scale, Timed up and go test and energy cost and physiological cost index. RESULTS: Both groups showed a significant increase in their Berg balance scale and significant decrease in their Timed up and go test score, and energy cost and physiological cost index (p<.05). Compared to the control group, the experimental group experienced a greater increase in the Berg balance scale (p<.05) and a larger decrease in the timed up and go test score, and energy cost and physiological cost index (p<.05). CONCLUSION: We suggest that active trunk training in the sitting position may be effective in improving balance and energy consumption in early stroke patients. Thus, active trunk training is important for such patients. Further studies are needed to better understand the effectiveness of trunk training in early stroke patients.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1658-1662
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• 2010

This paper presents an advanced energy saving algorithm in building. It is important to aggregate a various demand side resource which is surely controllable at the peak power time to reduce the energy cost. Previous demand side algorithm appropriate for building is based on peak power. In this paper, we develop the new energy saving algorithm to reduce the quantity of power consumption. The simulation results show that the proposed tem is very effective.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.580-588
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• 2016

Energy efficiency is the main objective in the design of a wireless sensor network (WSN). In many applications, sensing data must be transmitted from sources to a sink in a timely manner. This paper describes an investigation of the trade-off between two objectives in WSN design: minimizing energy consumption and minimizing end-to-end delay. We first propose a new distributed clustering approach to determining the best clusterhead for each cluster by considering both energy consumption and end-to-end delay requirements. Next, we propose a new energy-cost function and a new end-to-end delay function for use in an inter-cluster routing algorithm. We present a multi-hop routing algorithm for use in disseminating sensing data from clusterheads to a sink at the minimum energy cost subject to an end-to-end delay constraint. The results of a simulation are consistent with our theoretical analysis results and show that our proposed performs much better than similar protocols in terms of energy consumption and end-to-end delay.

• Journal of Construction Engineering and Project Management
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• v.3 no.3
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• pp.17-22
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.294-299
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.928-933
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• 2009

The purpose of this study is to examine the energy consumption, carbon dioxide emission & energy cost of district heating using sewage source. The annual TOE of heat pump using sewage source save 37.1 percent than city gas boiler. And annual carbon dioxide emission of heat pump cut down 41.3 percent than city gas boiler. If it charges the rate schedule for district heating to apartment resident, collected amount are 3,127,170 thousand won. As energy cost of heat pump & circulation pump are 1,378,072 thousand won. the profits are 1,749,098 thousand won. As payback period is 8.97years, applicability is low level. However, it has advantages in energy consumption, carbon dioxide emission & energy cost. Therefore, it needs to proceed through government assistance.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.236-240
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• 2011

Currently one of the most importance issues in industrial sector is energy cost and energy efficiency. The manufacturing plants especially have made many efforts to reduce energy cost by implementing maintenances. But in many cases, they are not aware that how much energy could be saved more. If we know the best energy consumption, which signifies energy baseline, we can control the intensity of maintenances. One way to obtain the baseline is using proper statistics from a specific plant, a sector of industry. Energy bandwidth signifies the gap between actual Specific Energy Consumption(SEC) of a certain plant and minimum SEC of the best plant, and estimate energy saving potential(ESP) is a result of bandwidth analysis. We chose a model plant and implemented some maintenance for a year, and then we obtained ESP. Additionally we could determine the decreased amount of carbon emissions from the plant using Carbon Emissions Factor(CEF) by Intergovernmental Panel on Climate Change(IPCC).

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.32-41
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• 2024

Building energy management plays a crucial role in improving energy efficiency and optimizing energy usage. To achieve this, it is important to monitor and analyze energy-related data from buildings in real time using sensors to understand energy consumption patterns and establish optimal operational strategies. Because of the uncertainties in building energy-related data, there are challenges in analyzing these data and formulating operational strategies based on them. Artificial intelligence (AI) technology can help overcome these challenges. This paper investigates past and current research trends in AI technology and examines its future prospects for building energy management. By performing prediction and analysis based on energy consumption or supply data, the future energy demands of buildings can be forecasted and energy consumption can be optimized. Additionally, data related to the surrounding environment, occupancy, and other building energy-related factors can be collected and analyzed using sensors to establish operational strategies aimed at further reducing energy consumption and increasing efficiency. These technologies will contribute to cost savings and help minimize environmental impacts for building owners and operators, ultimately facilitating sustainable building operations.