• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.1-11
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• 2018

In this work, we propose weather prediction models to estimate hourly outdoor temperatures and solar irradiance in the next day using forecasting information. Hourly weather data predicted by the proposed models are useful for setting system operating strategies for the next day. The outside temperature prediction model considers 3-hourly temperatures forecasted by Korea Meteorological Administration. Hourly data are obtained by a simple interpolation scheme. The solar irradiance prediction is achieved by constructing a dataset with the observed cloudiness and correspondent solar irradiance during the last two weeks and then by matching the forecasted cloud factor for the next day with the solar irradiance values in the dataset. To verify the usefulness of the weather prediction models in predicting a short-term building load, the predicted data are inputted to a TRNSYS building model, and results are compared with a reference case. Results show that the test case can meet the acceptance error level defined by the ASHRAE guideline showing 8.8% in CVRMSE in spite of some inaccurate predictions for hourly weather data.

• Solar Energy
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• v.12 no.2
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• pp.1-8
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• 1992

Identifying characteristics of heating and cooling systems requires estimation of thermal load of specific time interval, especially in cases that its system is operated intermittently, by using thermal storage, of in a partial load condition. Estimating the thermal load, however, needs to forecast hourly weather data variation. Hence, this paper attempts to examine characteristics of hourly ourdoor temperature variation as a preliminary research for the mathematical modeling of the hourly weather variation. Speculating characteristics of daily minimum and maximum temperature occurances, hourly outdoor temperature variation, and daily temperature differences in the increasing range ($07h{\sim}15h$) and decreasing range($15h{\sim}07h$), we were able to analyze changing patterns of daily temperature differences in each range in terms of daily solar amount, cloud ratio, and other weather data. Results from the multiple regression analysis enables us to conclude that daily differences in the increasing range are strongly affected last night temperature itself while the other range's differences are influenced by many weather data, which are solar amount, the variation of cloud, and the maximum temperature of the previous day.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.63-70
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• 2008

This paper resents the spatial electric load forecasting algorithm using the multiple regression analysis method which is enhanced from the algorithm of the DISPLAN(Distribution Information System PLAN). In order to improve the accuracy of the spatial electrical load forecasting, input variables are selected for GRDP(Gross Regional Domestic Product), the local population and the electrical load sales of the past year. Tests are performed to analyze the accuracy of the proposed method for Gyeong-San City, Gu-Mi City, Gim-Cheon City and Yeong-Ju City of North Gyeongsang Province. Test results show that the overall accuracy of the proposed method is improved the percentage error 11.2[%] over 12[%] of the DISPLAN. Specially, the accuracy is enhanced a lot in the case of high variability of input variables. The proposed method will be used to forecast local electric loads for the optimal investment of distribution systems.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Journal of the Korean Solar Energy Society
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• v.23 no.1
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• pp.57-67
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• 2003

This study aims to presents Evaluation Method of Green Remodeling that analyze the value of environment through expense, using the method of life cycle cost and life cycle assessment simultaneously. The results of this study are summarized as follows. Evaluation Model developed in this study can convert economical value of environment into cost by integrating. In addition, the model can apply as a useful tool to estimation of economical design alternative as well as quantification of environmental loads and costs. Evaluation Model presented In this study observe energy consumption and the environmental load emission with qualification, it can forecast effect of environmental cost that cost estimation is expected to be added to energy cost rate by being possible. Synthetically, when Estimation Model and computer program that developed in this study is applies to the construction industry; reasonable management of environmental load is convenient at each step of Green Remodeling. In addition, at preliminary design phase, practical use may be possible by reasonable yardstick about various alternatives and improvement of design alternatives likewise by grasping environmental effect.

• Korean System Dynamics Review
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• v.5 no.1
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• pp.49-71
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• 2004

Demand-side Management can be defined as'any utility activity aimed at modifying customers' use of energy to produce desired changes in the utility's load shape'. Customers benefit by being able to control energy costs and improve quality of life and become more productive. Utilities benefit from DSM's value as a resource that enhances asset utilization and reduces both fuel costs and environmental emissions. The scope of DSM includes load management through rate schedules and conservation by improving energy effciency and using electricity consumption effectively. This paper study the DSM resource evaluation and customer behavior analysis todesign the DSM Program plan in response to customer needs. We develop basic system dynamics model to analysis the customer behavior based on a survey research. The DSM Program participants in the Hi- efficiency Inverter, Electric motor and efficient lighting applicancies operating by Conservation program 2002 become the survey objects. DSM resource evaluation evaluate firstt the distribution potentialities of each machine and then forecast the degree of diffusion. We apply the system dynamic approach to simulate the dynamic DSM market situation at the domestic beginning. This model will give the energy Planner the opportunity to create different scenarios for DSM program planning. Also it will lead to increased understanding of the dynamic DSM market

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.765-777
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• 2011

According to the total pollution load management system, exact prediction and analysis of water quality and discharge has been required in order to allocate the amount of pollution load to each local government. In this study, QUAL2E model was used for comparison with other water quality models and improve the inadequate to forecast future water quality. And Various calibration and verification methods were applied to deal with existing uncertainties of parameter during modeling water quality. For user convenience, A GUI(Graphical User Interface) system named "QL2-XP" model is developed by object-oriented language for the user convenience and practical usage. Suggested GUI system consist of hydraulic analysis, water quality analysis, optimized model calibration processes, and postprocessing the simulation results. Therefore this model will be effectively utilized to manage practical and efficient water quality.