• KIEAE Journal
• /
• v.12 no.2
• /
• pp.77-82
• /
• 2012

Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.

• International Journal of Railway
• /
• v.6 no.2
• /
• pp.33-44
• /
• 2013

In recent years, energy consumption in the transportation sector by expanding motorization continues to increase in almost every country in the world. Moreover, the growth rate of the transportation energy consumption is significantly higher than those of the civilian and industrial sectors. Therefore, every country strives to reduce its dependence on private transport, which is the main contributor to the transportation energy consumption. In many countries, concepts such as Transit Oriented Development (TOD) or New Urbanism, which controls road traffic by increasing the proportion of the public transportation significantly, have been implemented to encourage a modal shift to public transport. However, the level of change required for eliminating environmental problems is a challenging task. Minimizing transportation energy consumption by controlling the increase of the traffic demand and maintaining the level of urban mobility simultaneously is a pressing dilemma for each city. Grasping the impact of the diversity of the urban transport and infrastructure is very important to improve transportation energy efficiency. However, the potential for reducing urban transportation energy consumption has often been ineffectively demonstrated by the diversity of cities. Therefore, the accuracy of evaluating the current efficiency rate of the urban energy consumption is necessary. Nevertheless, quantitative analyses related to the efficiency of transportation energy consumption are scarce, and the research on the current condition of consumption efficiency based on international quantitative analysis is almost nonexistent. On the basis of this background problem definitions, this research first built a database of the transportation energy consumption of private modes in 119 cities, with an attempt to reflect individual travel behaviors calculated by Person Trip data. Subsequently, Data Envelopment Analysis (DEA) was used as an assessment method to evaluate the efficiency of transportation energy consumption by considering the diversity of the urban traffic features in the world cities. Finally, we clarified the current condition of consumption efficiency by attempting to propose a target values for improving transportation energy consumption.

• Journal of the Korean Solar Energy Society
• /
• v.34 no.6
• /
• pp.93-102
• /
• 2014

The aim of this study is to analyze the influence of various factors on energy consumption of apartment buildings. Energy consumption data of the Green Together, integrated building energy management system maintained by the government were used, and end-use and primary energy consumption data of 2012 were analyzed for 181 apartment complexes completed between 2004 and 2011 in Seoul. Energy consumption by use, source and heating type were analyzed. Then, energy consumption trends were analyzed and suggested according to energy efficiency ratings, number of households, areas for exclusive use, number of floors, core types, building types, orientations and completion years.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.15 no.4
• /
• pp.287-293
• /
• 2003

This study examined the temperature-dependent regression model of energy consumption based on various measuring period. The methodology employed was to construct temperature-dependent linear regression model of daily energy consumption from one day to three months data-sets and to compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. Heating energy consumption from a building in Daejon was examined experimentally. From the results, predicted value based on one day experimental data can have error over 100%. But predicted value based on one week experimental data showed error over 30%. And predicted value based on over three months experimental data provides accurate prediction within 6% but it will be required very expensive.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.16 no.10
• /
• pp.914-922
• /
• 2004

In this study, measurement and analysis of energy consumption of a research building have been conducted. The energy audit procedure includes monitoring of electricity and LNG consumption over a period of three yews from 2000 to 2002. Data acquisition system for collecting energy consumption data of HVAC equipment such as chillers, fan filter units, AHUs, cooling towers, boilers, pumps, fan coil units, air compressors and etc. has been installed in a building located in Seoul. Data collected at an interval of 1 minute are analyzed for studying the energy consumption pattern of a research building. Percentage of energy consumption of all HVAC equipment is $51.0\%$ in 2000, $55.4\%$ in 2001, and $62.3\%$ in 2002, respectively. Electricity consumption of chillers accounts for $17.6\%$ of the total energy consumption, which is the largest. Annual energy consumption-rate per unit area is $840.5Mcal/m^2{\cdot}y$ in 2000, $1,064.8Mcal/m^2{\cdot}y$ in 2001, and $1,393.0Mcal/m^2{\cdot}y$ year 2002, respectively.

• KIEAE Journal
• /
• v.11 no.4
• /
• pp.71-78
• /
• 2011

The goal of this study is to analyze the impact of load factors on building energy consumption by using EnergyPlus program. We selected a campus building and monitored energy consumption from January 2009 to November 2010. First, we simulated energy consumption basically with weather data, building heat gain and EHP performance data. And then we simulated energy consumption with three additional parameter(infiltration, OA control and schedule). Simulation results are verified by MBE and Cv(RMSE) proposed by M&V guideline 3.0. Simulated total energy consumption was 104.3% of measurements, 4.33% of MBE, and 13.62% of Cv(RMSE). Results show infiltration and schedule were revealed as the most dominant factor of heating energy consumption and of cooling energy consumption, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.6
• /
• pp.1133-1151
• /
• 2010

Energy balanced consumption routing is based on assumption that the nodes consume energy both in transmitting and receiving. Lopsided energy consumption is an intrinsic problem in low-cost sensor networks characterized by multihop routing and in many traffic overhead pattern networks, and this irregular energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed low-cost sensor networks. We formulate the energy consumption balancing problem as an optimal balancing data transmitting problem by combining the ideas of corona cluster based network division and optimized transmitting state routing strategy together with data transmission. We propose a localized cluster based routing scheme that guarantees balanced energy consumption among clusters within each corona. We develop a new energy cluster based routing protocol called "OECR". We design an offline centralized algorithm with time complexity O (log n) (n is the number of clusters) to solve the transmitting data distribution problem aimed at energy balancing consumption among nodes in different cluster. An approach for computing the optimal number of clusters to maximize the network lifetime is also presented. Based on the mathematical model, an optimized energy cluster routing (OECR) is designed and the solution for extending OEDR to low-cost sensor networks is also presented. Simulation results demonstrate that the proposed routing scheme significantly outperforms conventional energy routing schemes in terms of network lifetime.

• Journal of Energy Engineering
• /
• v.23 no.3
• /
• pp.66-71
• /
• 2014

Oil is obviously vital for economic growth and industry development. This paper attempts to explore whether or not there is a inverted-U relationship between oil consumption and economic growth. To this end, we employ a panel data analysis with fixed effect or random effect models using the set of data from 61 countries for the year 1990-2008. In conclusion, a statistically significant inverted-U relationship between per capita consumption of oil and per capita GDP is found. However, the level of per capita GDP at the peak point of per capita oil consumption is estimated to be 65,072 in 2005 international constant dollars, which is much larger than economic scales of sampled countries. Thus, as per capita GDP grows, per capita oil consumption is predicted to increase until eventually reaching the peak.

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

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.14 no.4
• /
• pp.1-6
• /
• 2018

In this study, the energy consumption of the typical floor was compared by the total energy comsumption of the building in highrise building. In gerneral, many researchers are studying on the typical floor in highrise buildings for avoiding complexity in energy simulation. But few papers are studied on energy consumption along the floors. In the model bulding, the energy consumption data were acquired by BEMS system in 2011. According the data, the total net energy consumption was $193.99kWh/m^2$ for all area and the total net energy consumption was $247.61kWh/m^2$ for HVACR area. The total electricity and gas energy are used 47.7% for heating and cooling, 33.5% for lighting and plug, 12.9% for conveyance power and 5.9% for restaurant. In comparison of only ground floor, amount of energy consumption in the lobby is 10%, and 90% of total energy consumption is used in the typical floor. For this result, energy simulation on the typical floor is acceptable for calculating the total energy consumption in the highrise building.