• Advances in Energy Research
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• v.5 no.1
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• pp.57-64
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• 2017

Electricity is basic need for country development. But at the present time proper planning and policy is require at high pace for power generation network extension due to the increasing population growth rate. Present study aimed to analyze the present and future demand for electricity at household level in Province of Balochistan of Pakistan via simulation modeling. Data of year 2004-2005 was used as baseline data for electricity consumption to predict future demand of electricity at both rural and urban domestic level up to subsequent 30 years, with help of LEAP software. Basically three scenarios were created to run software. One scenario was Business-As-Usual and other two were green scenarios i.e., solar and wind energy scenarios. Results predicted that by using alternative energy sources, demand for electricity will be fulfill and will also reduce burden on non-renewable energy sources due to the greater potential for solar and wind energy present in Balochistan.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.13-22
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• 2018

At the time when zero energy building is expected to be generalized, this study examines whether the investment in the passive element or the active element is more effective in terms of energy in the construction process of zero energy building. In other words, the effect of energy demand by passive element and the change of energy generation by active element are examined in terms of the same investment cost. The purpose of this study is to examine the change of energy demand by passive element and the change of energy generation by active element in zero energy building and to make reasonable investment decision by comparing energy with cost aspect. For this purpose, we selected the buildings to be subjected to energy simulation and derive the required energy amount and energy generation amount by using meteorological data of four regions in Korea. The change of energy demand and energy generation according to the change of application condition was derived. In order to compare and analyze the changes in energy demand and generation at the same cost standard through price survey and quotation of window and photovoltaic power generation equipment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1566-1574
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• 2010

This paper presents an in-depth review for current status for demand-side management (DSM) investment of energy supplier and an useful prospect on the introduction of Energy Efficiency Resource Standards (EERS). According to the Article 9 of Rational Energy Utilization Act, Energy suppliers-Korea Electric Power Corporation (KEPCO), Korea Gas Corporation (KOGAS) and Korea District Heat Corporation (KDHC) prescribed by Presidential Decree-must establish and implement annual demand-side management investment plan to improve energy efficiency in production, transformation, transportation, storage and usage of corresponding energy and to reduce demand and green house gas emissions. In this paper, we examine the DSM programs of energy suppliers and the results of DSM investment in 2009, then we propose a reasonable solution for the development of DSM investment. Furthermore, in order to compare our situation, the case studies were conducted on EERS issues in England, Italy, France and U.S, such as establishing the energy saving target, selecting the target energy supplier, and penalty and incentive mechanisms. Throughout the case studies, this paper suggests the directions to the DSM investment planning of energy suppliers and the major issues to prepare EERS in Korea.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.991-994
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• 2016

This paper presents a load management method based on OpenADR of smart grid. Previous demand side algorithm is restricted on reducing peak power. But, in this paper we suggest a method of performing the energy-saving control according to the power price utilizing building automatic control system installed on the customer side in the case of hourly differential pricing signal is transmitted to the open automated demand response system. And, we showed the integrated demand management software for 3 buildings.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Environmental Engineering Research
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• v.16 no.1
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• pp.19-27
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• 2011

This paper introduces an approach to identify the total energy consumption with subsequent $CO_2$ emissions, for both industrial and non-industrial sectors. Statistical data for 2005 were compiled in a national account system to construct an energy input-output table for investigating the influence between energy demand and supply activities. The methodological approach was applied to South Korea. Twelve types of energy and fifteen industrial and non-industrial sectors are formed as the compartments of the input-output table. The results provided quantitative details of the energy consumption and identified the significant contributions from each sector. An impact analysis on the $CO_2$ emissions for the demand side was also conducted for comparison with the supply side.

• Steel and Composite Structures
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• v.6 no.6
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• pp.479-491
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• 2006

Structures in seismic regions are designed to dissipate seismic energy input through inelastic deformations. Structural or component failure occurs when the hysteretic energy demand for a structure or component subject to an earthquake ground motion (EQGM) exceeds its hysteretic energy dissipation capacity. This paper presents a study on identifying the hysteretic energy demand and distribution throughout the height of regular steel moment resisting frames (SMRFs) subject to severe EQGMs. For this purpose, non-linear dynamic time history (NDTH) analyses were carried out on regular low-, medium-, and high-rise steel SMRFs. An ensemble of ninety EQGMs recorded on different soil types was used in the study. The results show that the hysteretic energy demand decreases from the bottom stories to the upper stories and for high-rise structures, most of the hysteretic energy is dissipated by the bottom stories. The decrease is quite significant, especially, for medium- and high-rise structures.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.292-294
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• 1998

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.187-201
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• 2008

Earthquake induced hysteretic energy demand for a structure can be used as a limiting value of a certain performance level in seismic design of structures. In cases where it is larger than the hysteretic energy dissipation capacity of the structure, failure will occur. To be able to select the limiting value of hysteretic energy for a particular earthquake hazard level, it is required to define the variation of hysteretic energy in terms of probabilistic terms. This study focuses on the probabilistic evaluation of earthquake induced worst failure probability and approximate confidence intervals for inelastic single-degree-of-freedom (SDOF) systems with a typical steel moment connection based on hysteretic energy. For this purpose, hysteretic energy demand is predicted for a set of SDOF systems subject to an ensemble of moderate and severe EQGMs, while the hysteretic energy dissipation capacity is evaluated through the previously published cyclic test data on full-scale steel beam-to-column connections. The failure probability corresponding to the worst possible case is determined based on the hysteretic energy demand and dissipation capacity. The results show that as the capacity to demand ratio increases, the failure probability decreases dramatically. If this ratio is too small, then the failure is inevitable.