• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.175-181
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• 2021

본 논문은 단기 에너지 사용량 예측을 위해 인공지능 기반의 접근법에 대해 분석한다. 본 논문에서는 단기 에너지 사용량 예측 기술에 자주 활용되는 지도학습 알고리즘의 한계를 개선하기 위해 강화학습 알고리즘을 활용한다. 지도학습 알고리즘 기반의 접근법은 충분한 성능을 위해 에너지 사용량 데이터뿐만 아니라 contextual information이 필요하여 높은 복잡성을 가진다. 데이터와 학습모델의 복잡성을 개선하기 위해 다중 에이전트 기반의 심층 강화학습 알고리즘을 제안하여 에너지 사용량 데이터로만 에너지 사용량을 예측한다. 공개된 에너지 사용량 데이터를 통해 시뮬레이션을 진행하여 제안한 에너지 사용량 예측 기법의 성능을 확인한다. 제안한 기법은 이상점의 특징을 가지는 데이터를 제외하고 실제값과 유사한 값을 예측하는 것을 보여준다.

• 한국정밀공학회지
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• 제30권5호
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• pp.461-466
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• 2013

In this paper, main mechanism and measurement method of energy consumption for machine tools are investigated by experiment and simulation. To evaluate total energy consumption of the machine tools, standard test workpiece and measuring method and test procedures are suggested. And, improvement of energy consumption evaluation by the motion kinematics theory is used. In addition, to estimate energy consumption of machine tools in design process, mass distribution of the structure and 5 axis motions are investigated and simulated by numerical analysis.

• 설비공학논문집
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• 제15권4호
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• pp.287-293
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• 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.

• 한국지열·수열에너지학회논문집
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• 제12권4호
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• pp.8-14
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• 2016

In this study, the effects of prediction and reset control of outdoor air temperature on energy consumption for central heating system are researched by using TRNSYS program package, and the control performances with the suggested methods of prediction and reset control of outdoor air temperature are compared with the existing ones. As a result, the value of coefficient of determination $R^2$ for the predicted outdoor temperatures is improved and the suggested control method shows maximum 21.8% energy saving in comparison with existing control ones.

• 산업경영시스템학회지
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• 제41권1호
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.795-796
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• 2013

• 한국정보통신학회논문지
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• 제26권4호
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• 설비공학논문집
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• 제27권3호
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• pp.137-145
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• 2015

In this study, the actual energy consumption of the secondary side of District Heating System (DHS) with different hot water supply temperature control methods are compared. Three methods are Set-point Control, Outdoor Temperature Reset Control and Outdoor Temperature Prediction Control. While Outdoor Temperature Reset Control has been widely used for energy savings of the secondary side of the system, the results show that Outdoor Temperature Prediction Control method saves more energy. In general, Outdoor Temperature Prediction Control method lowers the supply temperature of hot water, and it reduces standby losses and increases overall heat transfer value of heated spaces due to more flow into the space. During actual energy consumption monitoring, Outdoor Temperature Prediction Control method saves about 7.1% in comparison to Outdoor Temperature Reset Control method and about 15.7% in comparison to Set-point Control method. Also, it is found that at when partial load condition, such as daytime, the fluctuation of hot water supply temperature with Set-point Control is more severe than Outdoor Temperature Prediction Control. Therefore, it proves that Outdoor Temperature Prediction Control is more stable even at the partial load conditions.

• 한국정밀공학회지
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• 제32권4호
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• pp.329-337
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• 2015

In this paper, a simulation based estimation method of energy consumption of the spindle and feed drives for the NC machine tool during the cutting process is proposed. To predict energy consumption of the feed drive system, position, velocity, acceleration and jerk of the table are analyzed based on NC data and then the power and energy are calculated considering friction force and mass of the stages. Energy consumption of the spindle is estimated based on models from acceleration motion of rotating parts, friction torque and power loss of motors. Moreover, simulation models of cutting power and energy for the material removal along the NC tool paths are proposed.