• 제목/요약/키워드: Evaluation and Prediction of Energy Consumption

검색결과 22건 처리시간 0.021초

건물 냉방시스템의 예측제어를 위한 인공신경망 모델 개발 (Development of an Artificial Neural Network Model for a Predictive Control of Cooling Systems)

  • 강인성;양영권;이효은;박진철;문진우
    • KIEAE Journal
    • /
    • 제17권5호
    • /
    • pp.69-76
    • /
    • 2017
  • Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

연관 분류 마이닝 기법을 활용한 지식기반 신체활동 평가 모델 (A Knowledge Based Physical Activity Evaluation Model Using Associative Classification Mining Approach)

  • 손창식;최락현;강원석
    • 대한임베디드공학회논문지
    • /
    • 제13권4호
    • /
    • pp.215-223
    • /
    • 2018
  • Recently, as interest of wearable devices has increased, commercially available smart wristbands and applications have been used as a tool for personal healthy management. However most previous studies have focused on evaluating the accuracy and reliability of the technical problems of wearable devices, especially step counts, walking distance, and energy consumption measured from the smart wristbands. In this study, we propose a physical activity evaluation model using classification rules, induced from the associative classification mining approach. These rules associated with five physical activities were generated by considering activities and walking times in target heart rate zones such as 'Out-of Zone', 'Fat Burn Zone', 'Cardio Zone', and 'Peak Zone'. In the experiment, we evaluated the prediction power of classification rules and verified its effectiveness by comparing classification accuracies between the proposed model and support vector machine.

셋백기간 중 건물 냉방시스템 부하 예측을 위한 인공신경망모델 성능 평가 (Performance tests on the ANN model prediction accuracy for cooling load of buildings during the setback period)

  • 박보랑;최은지;문진우
    • KIEAE Journal
    • /
    • 제17권4호
    • /
    • pp.83-88
    • /
    • 2017
  • Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

건축물 신재생에너지원의 이용 현황 및 문제점 분석 (An Analysis of Problems and the Current Status of Renewable Energy System in Buildings)

  • 장향인;성윤복;조영흠;김용식;조재훈
    • 한국태양에너지학회 논문집
    • /
    • 제32권5호
    • /
    • pp.75-82
    • /
    • 2012
  • This study aims to investigate the usage of the renewable energy systems installed in buildings and make suggestions for the effective management. In this regard, a questionnaire survey was conducted on 1)design and construction, 2) operation and management, 3) user satisfaction and improvements about the renewable energy systems in buildings. Findings from this study can be summarized as follows; a lack of the basic information about systems, non-use of energy management systems, the differences in the features by energy source, and a lack of expertise of managers. The requirements to resolved these problems include the integrated management of various electric heat sources including a renewable energy source, an operation schedule based on the prediction of production and consumption, and so on. Furthermore the necessity of multiplex energy sources management system was confirmed and the basic data needed to establish the targets of this system were obtained.

내연기관엔진의 가스혼소발전 경제성 예측모델 개발 (Development of Economic Prediction Model for Internal Combustion Engine by Dual Fuel Generation)

  • 허광범;장혁준;이형원
    • 한국수소및신에너지학회논문집
    • /
    • 제31권4호
    • /
    • pp.380-386
    • /
    • 2020
  • This paper represents an analysis of the economic impact of firing natural gas/diesel and natural gas/by-product oil mixtures in diesel engine power plants. The objects of analysis is a power plant with electricity generation capacity (300 kW). Using performance data of original diesel engines, the fuel consumption characteristics of the duel fuel engines were simulated. Then, economic assessment was carried out using the performance data and the net present value method. A special focus was given to the evaluation of fuel cost saving when firing natural gas/diesel and natural gas/by-product oil mixtures instead of the pure diesel firing case. Analyses were performed by assuming fuel price changes in the market as well as by using current prices. The analysis results showed that co-firing of natural gas/diesel and natural gas/by-product oil would provide considerable fuel cost saving, leading to meaningful economic benefits.

해양환경 예측정보를 활용한 인공지능 분석 기반의 최적 안전항로 연구 (Research on optimal safety ship-route based on artificial intelligence analysis using marine environment prediction)

  • 엄대용;이방희
    • 한국항해항만학회:학술대회논문집
    • /
    • 한국항해항만학회 2023년도 춘계학술대회
    • /
    • pp.100-103
    • /
    • 2023
  • 최근 스마트선박 개발에 발맞춰 정확하고 세밀한 실시간 해양환경 예측정보의 요구가 확대되고 선박에 직접 지원하기 위한 환경이 확보됨에 따라 최적항로 분야에서도 다양한 해양환경을 고려한 정보 생산 및 평가 연구가 필요하다. 스마트선박에서 해양환경의 위험도 및 에너지 소비의 불확실성을 줄이면서 최적항로를 산출할 수 있는 알고리즘은 2단계로 구분하여 개발하였다. 1단계는 해양환경정보들과 선박자동식별시스템(AIS)내에 선박의 위치·상태정보를 결합해 프로파일을 생성하였다. 2단계는 구성한 프로파일 결과를 이용하여 해양환경 에너지맵을 정의할 수 있는 모델을 개발하였고, 약 60만개의 데이터를 반영할 수 있도록 인공지능 머신러닝 기법 중 Random Forest를 적용하여 회귀식을 생성하였다. Random Forest 회귀 모델의 결정계수(R2)는 0.89 를 보였다. 생성한 모델에 2021년 6월 1일~3일의 해양환경 예측정보를 이용하여 Dijikstra 최단경로 알고리즘을 적용해 최적 안전항로를 산출하고 맵에 표출했다. Random Forest 회귀 모델로 산출된 항로는 유선적이고 해양환경 예측정보의 상태를 감안하며 항로를 도출하는 결과를 보였다. 본 연구의 실시간 해양환경 예측정보 기반의 항로 산출 개념은 선박의 이동 경향성을 반영한 현실적이면서 안전한 항로 산출이 가능하고, 향후 경제성, 안전성, 친환경성 평가 모델로 범위로 확대할 수 있을 것으로 기대된다.

  • PDF

CFD 기반 소형 선박의 EEDI 평가 방법에 관한 연구 (Study on the Evaluation Method for EEDI of the Small Vessel using CFD)

  • 박동우
    • 해양환경안전학회지
    • /
    • 제25권5호
    • /
    • pp.627-633
    • /
    • 2019
  • 본 논문의 주 관심사항은 전산유체역학과 기존 모형시험 데이터를 활용하여 주어진 선박의 저항 및 추진성능을 추정하고 그 결과를 이용하여 에너지효율설계지표(Energy Efficiency Design Index, EEDI)를 평가하는 방법을 제시하는 것이다. 대상선박의 모형선 크기에서의 전 저항을 계산하기 위해 점성 유동 해석을 수행하였다. 유동계산은 STAR-CCM+를 사용하였으며 자유표면, 트림과 싱키지를 고려하였다. 점성 유동 해석 결과를 바탕으로 대상선박의 유효동력을 산정하였다. 준 추진효율 계수는 기 보유한 모형시험 데이터베이스를 이용한 추정식 및 유사선박의 시험자료를 활용하여 산정하였다. 최종적으로 EEDI 산정식에 대하여 유체동역학적 결과, 선박의 정보, 사용하는 연료에 대한 $CO_2$의 환산계수, 연료소모량 등을 바탕으로 일반화된 계산 프로그램을 작성하였다.

설비공학 분야의 최근 연구 동향: 2014년 학회지 논문에 대한 종합적 고찰 (Recent Progress in Air-Conditioning and Refrigeration Research: A Review of Papers Published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2014)

  • 이대영;김사량;김현정;김동선;박준석;임병찬
    • 설비공학논문집
    • /
    • 제27권7호
    • /
    • pp.380-394
    • /
    • 2015
  • This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2014. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of heat and mass transfer, cooling and heating, and air-conditioning, the flow inside building rooms, and smoke control on fire. Research issues dealing with duct and pipe were reduced, but flows inside building rooms, and smoke controls were newly added in thermal and fluid engineering research area. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results for thermal contact resistance measurement of metal interface, a fan coil with an oval-type heat exchanger, fouling characteristics of plate heat exchangers, effect of rib pitch in a two wall divergent channel, semi-empirical analysis in vertical mesoscale tubes, an integrated drying machine, microscale surface wrinkles, brazed plate heat exchangers, numerical analysis in printed circuit heat exchanger. In the area of pool boiling and condensing, non-uniform air flow, PCM applied thermal storage wall system, a new wavy cylindrical shape capsule, and HFC32/HFC152a mixtures on enhanced tubes, were actively studied. In the area of industrial heat exchangers, researches on solar water storage tank, effective design on the inserting part of refrigerator door gasket, impact of different boundary conditions in generating g-function, various construction of SCW type ground heat exchanger and a heat pump for closed cooling water heat recovery were performed. (3) In the field of refrigeration, various studies were carried out in the categories of refrigeration cycle, alternative refrigeration and modelling and controls including energy recoveries from industrial boilers and vehicles, improvement of dehumidification systems, novel defrost systems, fault diagnosis and optimum controls for heat pump systems. It is particularly notable that a substantial number of studies were dedicated for the development of air-conditioning and power recovery systems for electric vehicles in this year. (4) In building mechanical system research fields, seventeen studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, and renewable energies, piping in the buildings. Proposed designs, performance performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the evaluation of work noise in tunnel construction and the simulation and development of a light-shelf system. The subjects of building energy were worked on the energy saving of office building applied with window blind and phase change material(PCM), a method of existing building energy simulation using energy audit data, the estimation of thermal consumption unit of apartment building and its case studies, dynamic window performance, a writing method of energy consumption report and energy estimation of apartment building using district heating system. The remained studies were related to the improvement of architectural engineering education system for plant engineering industry, estimating cooling and heating degree days for variable base temperature, a prediction method of underground temperature, the comfort control algorithm of car air conditioner, the smoke control performance evaluation of high-rise building, evaluation of thermal energy systems of bio safety laboratory and a development of measuring device of solar heat gain coefficient of fenestration system.

Simulation analysis and evaluation of decontamination effect of different abrasive jet process parameters on radioactively contaminated metal

  • Lin Zhong;Jian Deng;Zhe-wen Zuo;Can-yu Huang;Bo Chen;Lin Lei;Ze-yong Lei;Jie-heng Lei;Mu Zhao;Yun-fei Hua
    • Nuclear Engineering and Technology
    • /
    • 제55권11호
    • /
    • pp.3940-3955
    • /
    • 2023
  • A new method of numerical simulating prediction and decontamination effect evaluation for abrasive jet decontamination to radioactively contaminated metal is proposed. Based on the Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) coupled simulation model, the motion patterns and distribution of abrasives can be predicted, and the decontamination effect can be evaluated by image processing and recognition technology. The impact of three key parameters (impact distance, inlet pressure, abrasive mass flow rate) on the decontamination effect is revealed. Moreover, here are experiments of reliability verification to decontamination effect and numerical simulation methods that has been conducted. The results show that: 60Co and other homogeneous solid solution radioactive pollutants can be removed by abrasive jet, and the average removal rate of Co exceeds 80%. It is reliable for the proposed numerical simulation and evaluation method because of the well goodness of fit between predicted value and actual values: The predicted values and actual values of the abrasive distribution diameter are Ф57 and Ф55; the total coverage rate is 26.42% and 23.50%; the average impact velocity is 81.73 m/s and 78.00 m/s. Further analysis shows that the impact distance has a significant impact on the distribution of abrasive particles on the target surface, the coverage rate of the core area increases at first, and then decreases with the increase of the impact distance of the nozzle, which reach a maximum of 14.44% at 300 mm. It is recommended to set the impact distance around 300 mm, because at this time the core area coverage of the abrasive is the largest and the impact velocity is stable at the highest speed of 81.94 m/s. The impact of the nozzle inlet pressure on the decontamination effect mainly affects the impact kinetic energy of the abrasive and has little impact on the distribution. The greater the inlet pressure, the greater the impact kinetic energy, and the stronger the decontamination ability of the abrasive. But in return, the energy consumption is higher, too. For the decontamination of radioactively contaminated metals, it is recommended to set the inlet pressure of the nozzle at around 0.6 MPa. Because most of the Co elements can be removed under this pressure. Increasing the mass and flow of abrasives appropriately can enhance the decontamination effectiveness. The total mass of abrasives per unit decontamination area is suggested to be 50 g because the core area coverage rate of the abrasive is relatively large under this condition; and the nozzle wear extent is acceptable.

인공신경망 기반 온실 외부 온도 예측을 통한 난방부하 추정 (Outside Temperature Prediction Based on Artificial Neural Network for Estimating the Heating Load in Greenhouse)

  • 김상엽;박경섭;류근호
    • 정보처리학회논문지:소프트웨어 및 데이터공학
    • /
    • 제7권4호
    • /
    • pp.129-134
    • /
    • 2018
  • 최근, 인공신경망 모델은 예측, 수치제어, 로봇제어, 패턴인식 등의 분야에서 촉망되는 기술이다. 본 연구에서는 인공신경망 모델을 이용하여 온실 외부 온도를 예측하고 이를 온실제어에 활용하는데 목적이 있다. 예측 모델의 성능 평가를 위해 다중회귀모델과 SVM 모델과의 비교분석을 수행하였다. 평가 방법으로는 10-Fold Cross Validation을 사용하였으며, 예측 성능 향상을 위해 상관관계분석 통해 데이터 축소를 수행하였고, 측정 데이터로부터 새로운 Factor 추출하여 데이터의 신뢰성을 확보하였다. 인공신경망 구축을 위해 Backpropagation algorithm을 사용하였으며, 다중회귀모델은 M5 method로 구축하였고, SVM 모델을 epsilon-SVM으로 구축하였다. 각 모델의 비교분석 결과 각각 0.9256, 1.8503과 7.5521로 나타났다. 또한 예측모델을 온실 난방부하 계산에 적용함으로써 온실에 사용되는 에너지 비용 절감을 통한 수입증대에 기여할 수 있다. 실험한 온실의 난방부하는 3326.4kcal/h이며, 총 난방시간이 $10000^{\circ}C/h$일 때 연료소비량은 453.8L로 예측된다. 아울러 데이터 마이닝 기술 중 하나인 인공신경망을 정밀온실제어, 재배기법, 수확예측 등 다양한 농업 분야에 적용함으로써 스마트 농업으로의 발전에 기여할 수 있다.