• 한국인공지능학회지
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• 제12권1호
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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.

• Advances in Energy Research
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• 제4권2호
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• pp.107-120
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• 2016

The aim of this research is to apply the eQuest model to investigate the energy conservation in a multifamily building located in Dayton, Ohio by using a Trombe wall and an ammonia ground source heat pump (R-717 GSHP). Integration of the Trombe wall into the building is the first retrofitting measure in this study. Trombe wall as a passive solar system, has a simple structure which may reduce the heating demand of buildings significantly. Utilization of ground source heat pump is an effective approach where conventional air source heat pump doesn't have an efficient performance, especially in cold climates. Furthermore, the type of refrigerant in the heat pumps has a substantial effect on energy efficiency. Natural refrigerant, ammonia (R-717), which has a high performance and no negative impacts on the environment, could be the best choice for using in heat pumps. After implementing the eQUEST model in the said multifamily building, the total annual energy consumption with a conventional R-717 air-source-heat-pump (ASHP) system was estimated as the baseline model. The baseline model results were compared to those of the following scenarios: using R-717 GSHP, R410a GSHP and integration of the Trombe wall into the building. The Results specified that, compared to the baseline model, applying the R-717 GSHP and Trombe wall, led to 20% and 9% of energy conservation in the building, respectively. In addition, it was noticed that by using R-410a instead of R-717 in the GSHP, the energy demand increased by 14%.

• 신재생에너지
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• 제9권2호
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• pp.15-22
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• 2013

The solar energy are an infinite source of energy and a clean energy without secondary pollution. The global solar energy reaching the earth's surface can be calculated easily according to the change of latitude, altitude, and sloped surface depending on the amount of the actual state of the atmosphere and clouds. The high-resolution solar-meteorological resource map with 1km resolution was developed in 2011 based on GWNU (Gangneung-Wonju National University) solar radiation model with complex terrain. The very high resolution solar energy map can be calculated and analyzed in Seoul and Eunpyung with topological effect using by 1km solar-meteorological resources map, respectively. Seoul DEM (Digital Elevation Model) have 10m resolution from NGII (National Geographic Information Institute) and Eunpyeong new town DSM (Digital Surface Model) have 1m spatial resolution from lidar observations. The solar energy have small differences according to the local mountainous terrain and residential area. The maximum bias have up to 20% and 16% in Seoul and Eunpyung new town, respectively. Small differences are that limited area with resolutions. As a result, the solar energy can calculate precisely using solar radiation model with topological effect by digital elevation data and its results can be used as the basis data for the photovoltaic and solar thermal generation.

• 한국태양에너지학회 논문집
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• 제34권1호
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• pp.39-47
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• 2014

Solar applications analysis and building energy performance depend on the quality of the solar resource data available. Unfortunately, most of the weather stations do not measure solar radiation data in Korea, as a reason many researchers have studied different solar radiation estimation models and suggested to apply them to various locations in Korea. In addition, they also studied the impact of hourly global solar radiation on energy performance of an office building by comparing the simulated building energy consumptions using four different weather files, one using measured, and three estimated solar radiation from different models, which are Cloud-cover Radiation Model (CRM), Zhang and Huang Model (ZHM), and Meteorological Radiation Model (MRM), and concluded that there was some impact on energy performance of the building due to the using different solar radiation models. However, the result cannot be applied to all other buildings since the simulated office building for that study only used limited building characteristics such as using fixed values of solar heat gain coefficient (SHGC) and window-to-wall ratio (WWR), which are significant parameters related to solar radiation that affect to the building energy consumptions. Therefore, there is a need to identify how the building energy consumption will be changed by varying these building parameters. In this study, the impact of one measured and three estimated global solar radiation on energy performance of the office building was conducted taking account of SHGC and WWR. As a result, it was identified that the impact of four different solar radiation data on energy performance of the office building was evident regardless SHGC and WWR changes, and concluded that the most suitable solar models was changed from the CRM/ZHM to the MRM as SHGC and WWR increases.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• 설비공학논문집
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• 제27권6호
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• pp.283-292
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• 2015

A model of apartment heating load in a district heating system could be useful in the management and utilization of energy resources, since it could predict energy usage and so could assist in the efficient use of energy resources. The heating load in a district heating system varies in a highly nonlinear manner and is subject to many different factors, such as heating area, number of people living in that complex, and ambient temperature. Thus there are few published papers with accurate models of heating load, especially in domestic literature. This work is concerned with the modeling of apartment heating load in a district heating system in winter, using the reduced least square support vector machine (LS-SVM), and with the purpose of using the model to predict heating energy usage in domestic city area. We collected 23,856 pieces of data on heating energy usage over a 12-week period in winter, from 12 heat exchangers in five apartments. Half of the collected data were used to construct the heating load model, and the other half were used to test the model's accuracy. The model was able to predict the heating energy usage pattern rather accurately. It could also estimate the usage of heating energy within of mean absolute percentage error. This implies that the model prediction accuracy needs to be improved further, but it still could be considered as an acceptable model if we consider the nonlinearity and uncertainty of apartment heating energy usage in a district heating system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3066-3079
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• 2016

Recent years have witnessed a dramatic increase in topology research of wireless sensor networks (WSNs) where both energy consumption and survivability need careful consideration. To balance energy consumption and ensure survivability against both random failures and deliberate attacks, we resort to complex network theory and propose an energy-aware preferential attachment (EPA) model to generate a robust topology for WSNs. In the proposed model, by taking the transmission range and energy consumption of the sensor nodes into account, we combine the characters of Erdős -Rényi (ER) model and Barabasi-Albert (BA) model in this new model and introduce tunable coefficients for balancing connectivity, energy consumption, and survivability. The correctness of our theoretic analysis is verified by simulation results. We find that the topology of WSNs built by EPA model is asymptotically power-law and can have different characters in connectivity, energy consumption, and survivability by using different coefficients. This model can significantly improve energy efficiency as well as enhance network survivability by changing coefficients according to the requirement of the real environment where WSNs deployed and therefore lead to a crucial improvement of network performance.

• 에너지공학
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• 제24권1호
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• pp.69-77
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• 2015

본 연구에서는 보정된 모델링 건물을 대상으로 PMV 변수에 대한 데이터베이스를 구축하였고, 다중회귀분석을 통하여 PMV 회귀모델을 도출하였다. PMV 회귀모델은 민감도 및 데이터 분석을 통하여 단순화하여 단순 PMV 회귀모델을 제시하였다. 단순 PMV 회귀모델과 Fanger PMV 모델에 대한 MAE 및 RMSE 검증을 통하여 단순 PMV 회귀모델이 Fanger PMV 모델을 대체할 수 있는 것으로 분석되었다. EnergyPlus의 EMS(Energy Management System)를 이용하여 보정된 모델링 건물에 PMV 회귀모델 제어를 적용하였다. 단순 PMV 회귀모델과 Fanger PMV 모델 제어의 온열 쾌적도를 비교한 결과, 두 제어 모두 공조기간 동안 약 90% 이상이 온열쾌적 범위를 만족하였고, 온열 쾌적 제어의 특징인 설정 PMV를 만족하는 설정온도에 의하여 제어되는 것으로 나타났다.