배출계수 개발 및 배출량 산정 체계 고도화를 통한 건설기계의 연식, 출력 및 기종별 대기오염물질 배출량 산정

At Olkaria II power plant located in Kenya, the brine from the separator is re-injected back into the ground. This study analyzes a flash geothermal-solar hybrid cycle using the working fluids n-butane, n-pentane, cyclopentane, hexamethyldisiloxane and toluene in order to utilize the brine. The studied working fluids have no potential to deplete the ozone or cause global warming. The working fluids were analyzed in terms of net power output and pump power required. This was done by heating Therminol VP-1 fluid to a maximum temperature of 400 °C using the parabolic trough solar collector and varying the working fluids mass flow rate, Therminol VP-1 flow rate and pressure, and the brine pressure and flow rate. They were varied to investigate their effect on the power output of the Organic Rankine Cycle. The working fluid with higher net power output was selected as a suitable working fluid for the hybrid cycle. The selected working fluid was used to determine the average hourly power output in each month of the hybrid system using the irradiance of Olkaria II power station. N-butane showed the best results in net power output hence it was selected as a suitable working fluid as compared to the other working fluids. When the hybrid system was analyzed using n-butane to obtain the hourly average power output in each month, the results revealed that the month of February has the highest hourly average power output where the Steam Turbine has an average hourly power output of 10.48 MW and Binary Turbine net power output of 18.56 MW. The minimum average hourly power output was obtained in the month of June where the Steam Turbine has an average hourly power output of 9.08 MW and Binary Turbine net power output of 16.88 MW.

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.

Endurance performance is a common criterion used to evaluate training or dietary interventions. However, to accurately appraise the effects of an intervention, the endurance performance measure must be reliable. The purpose of the investigation was to establish the reliability of a 1-h endurance performance test. Twenty trained female subjects (peak VO2 = 47.4 +/- 7.2 ml.kg-1.min-1) completed two trials in which they had to generate the highest power output possible throughout 60 min of cycling. Heart rates (HR) and ratings of perceived exertion (RPE) were also recorded during these two trials. All tests were conducted on a wind-braked cycle ergometer set up to closely resemble the subject's own cycle. The trials were separated by 1 wk, conducted on the same day of the week, and completed at a similar time of the day. The average power outputs (+/-SD) for the two trials were 180.0 (+/-18.1) W and 180.0 (+/-20.6) W. The results revealed that average absolute power output, HR, and RPE were not significantly different between trials. The intraclass correlation coefficient (one way ANOVA) for average absolute power output was 0.97, the coefficient of variation was 2.7%, and the SEM was 3.4 W. These results suggest that under controlled conditions average absolute power output during a 1-h endurance test is a reliable measure for trained female cyclists.

Since the underlying demand for network capacity can be satisfied only by extremely increasing transmission bit rate, processing speed, and switching capacity, it definitely will lead to increased power consumption of network nodes. Energy crisis is global crisis nowadays and it has brought up many problems. Power consumption is becoming a crucial issue in designing high-performance network devices. This paper studies the power consumption model of a kind of Optical Packet Switching (OPS) node based on Semiconductor Optical Amplifiers (SOA). Our results show that by dynamically adjusting the bias current of SOAs, the power consumption on an OPS node can be reduced considerably, especially when the traffic distribution across multiple ports is not balanced and inter-nodal distances vary. The total electrical power consumption can be cut off significantly. We also show the power consumption can be further reduced by jointly optimizing the thermoelectric cooler current.

Since the underlying demand for network capacity can be satisfied only by extremely increasing transmission bit rate, processing speed, and switching capacity, it definitely will lead to increased power consumption of network nodes. Energy crisis is global crisis nowadays and it has brought up many problems. Power consumption is becoming a crucial issue in designing high-performance network devices. This paper studies the power consumption model of a kind of Optical Packet Switching (OPS) node based on Semiconductor Optical Amplifiers (SOA). Our results show that by dynamically adjusting the bias current of SOAs, the power consumption on an OPS node can be reduced considerably, especially when the traffic distribution across multiple ports is not balanced and inter-nodal distances vary. The total electrical power consumption can be cut off significantly. We also show the power consumption can be further reduced by jointly optimizing the thermoelectric cooler current.

Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors

Presence and occupancy detection in residential and office environments is used to predict movement of people, detect intruders, and manage electric power consumption. Specifically, we are developing methods to improve demand side electrical power management by reducing electrical power waste in unoccupied spaces. In this paper, we conduct an extensive analysis on the applicability of using a WiFi router's electrical power consumption in different types of environments to determinate the number or people present in a space. We show the importance of a moving average filter for electrical load time series data, confirm the correlation between control packets and increased minimal router power consumption, and present our results on the accuracy of our approach. We conclude that a WiFi router's power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.

Multi-Level Stacked Regression for predicting electricity consumption of Hot Rolling Mill

Our study uses a small area estimation method to estimate the average and inequality of per capita kWh consumption for small areas in Vietnam. It shows evidence of a large spatial heterogeneity in the electric power consumption between districts and provinces in Vietnam. Households in the mountains and highlands consumed remarkably less electricity than those in the delta and coastal areas. Notably, we find a U-shaped relationship between the inequality of electricity consumption and economic levels in Vietnam. In poor districts and provinces, there is very high inequality in electricity consumption. Inequality is lower in middle-income districts and provinces.

Research on the Correlation of Monthly Electricity Consumption in Different Industries: A Case Study of Bazhou County

The development of local, accurate emission factors is very important for the estimation of reliable national emissions and air quality management. For that, this study is performed for pollutants released to the atmosphere with source-specific emission tests from the semiconductor manufacturing industry. The semiconductor manufacturing industry is one of the major sources of air toxics or hazardous air pollutants (HAPs); thus, understanding the emission characteristics of the emission source is a very important factor in the development of a control strategy. However, in Korea, there is a general lack of information available on air emissions from the semiconductor industry. The major emission sources of air toxics examined from the semiconductor manufacturing industry were wet chemical stations, coating applications, gaseous operations, photolithography, and miscellaneous devices in the wafer fabrication and semiconductor packaging processes. In this study, analyses of emission characteristics, and the estimations of emission data and factors for air toxics, such as acids, bases, heavy metals, and volatile organic compounds from the semiconductor manufacturing process have been performed. The concentration of hydrogen chloride from the packaging process was the highest among all of the processes. In addition, the emission factor of total volatile organic compounds (TVOCs) for the packaging process was higher than that of the wafer fabrication process. Emission factors estimated in this study were compared with those of Taiwan for evaluation, and they were found to be of similar level in the case of TVOCs and fluorine compounds.

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

Homomorphic encryption for smart grids has been investigated in many studies. It is possible to estimate the total power consumption in an area without knowing the consumption data of individual households. In the case of demand response (DR), it is important to calculate the total electric power consumption in an area because DR reports are published accordingly to reduce peak power consumption when the demand is high. However, the published data may reveal private information about residents, such as the timings of specific activities (leaving from and returning home), and device details. To overcome this problem, we propose a method specialized to enable energy providers to securely share electric power consumption data. The proposed method uses a self-organizing map (SOM), which is an unsupervised learning method. In order to share power consumption data while preserving privacy, the SOM is shared without the raw data. In this framework, a target accuracy of nearly 3% is achieved, while actual data are not published by any company.

Selection of emission factor standards for estimating emissions from diesel construction equipment in building construction in the Australian context

In the context of big data, an in-depth understanding of the potential correlation between electric power consumption and pollutant emission concentration of industrial enterprises is of great significance for effective prediction and monitoring of pollutant emissions. In this paper, considering the properties of nonlinearity and complexity in the data of electric power consumption and pollutant emission of industrial enterprises, we apply cross-correlation statistic test, detrended cross-correlation analysis and multifractal detrended cross-correlation analysis to study the correlation between them. Our numerical example based on an industrial enterprise shows that there is indeed an association between electricity consumption data and pollutant emission concentration of the enterprise. Moreover, the correlations between them demonstrate long memory and multifractality. Our research can help to further predict the pollutant emission using the electric power big data.