Privacy-preserving data collection for demand response using self-organizing map

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.

Optimization of the Air- and Gas-supply Network of a Chemical Plant

Global energy consumption has gone up and will continue to increase into the future as the population increases and more energy is required to provide resources for the growing population. One way to reduce electric power consumption is to reduce phantom loads, which occur when electronic devices draw energy, even when not in use. This seemingly innocuous source of energy consumption has gained attention for its impact on residential bills and the environment. We hypothesize that kitchen appliances' phantom loads, based on online statistics, account for more than 10% of their total electric power consumption. To test this hypothesis, we propose a shifted gamma distribution model to estimate the phantom loads and apply the model to a public dataset of household electric power consumption. Our results showed 10.9% phantom loads for kitchen appliances supporting our hypothesis. Our findings suggest that implementing strategies to mitigate phantom loads becomes imperative to reduce electricity bills and save energy.

Information systems based on the cloud computing model and peer-to-peer (P2P) model are now getting popular. In the cloud computing model, a cloud of servers support thin clients with various types of service like Web pages and databases. On the other hand, every computer is peer and there is no centralized coordinator in the P2P model. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems to realize eco-society. In this paper, we consider a Web type of application on P2P overlay networks. First, we discuss a model for showing how much each server peer consumes electric power to perform Web requests from client peers. Then, we discuss algorithms for a client peer to select a server peer in a collection of server peers so that the total power consumption can be reduced while some constraint like deadline one is satisfied. Lastly, we evaluate the algorithms in terms of the total power consumption and throughput compared with traditional round robin algorithms.

Information systems are composed of various types of computers interconnected in networks. In addition, information systems are being shifted from the traditional client-server model to the peer-to-peer (P2P) model. P2P systems are scalable and fully distributed without any centralized coordinator. Here, it is getting more significant to discuss how to reduce the total electric power consumption of computers in addition to developing distributed algorithms to minimize the computation time and memory space. In this paper, we do not discuss microlevel models like the hardware specifications of computers like low-energy CPUs. We rather discuss a macrolevel model to show the relation of the amount of computation and the total power consumption of multiple peer computers to perform Web types of application processes. We also discuss algorithms for allocating a process to a computer so that the deadline constraint is satisfied and the total power consumption is reduced.

Information systems are composed of various types of computers interconnected in networks. In addition, information systems are being shifted from the traditional client-server model to the peer-to-peer (P2P) model. The P2P systems are scalable and fully distributed without any centralized coordination. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems in addition to developing distributed algorithms to minimize the computation time. In this paper, we do not discuss the micro level like the hardware specification of each computer. We discuss a model to show the relation of the computation and the total power consumption of multiple peer computers to perform Web types of processes at macro level. We also discuss algorithms for allocating a process to a computer so that the deadline constraint is satisfied and the total power consumption is reduced.

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

Behavior of a system using the diurnal range of atmosphere temperature in the summer is characterized by numerical simulation. The proposed system consists of a radiator, a thermal energy storage tank, and a fan-coil cooler. The storage tank is fully stratified. The performance of the system is compared with the case when the storage tank is fully mixed. Parameters for the calculation are assumed from the actual weather and characteristics of the appliances in Japan. Total consumption of electric power is used for evaluating the system performance. From the calculation, it is found that there are optimum conditions to decrease the consumption of electric power as well as the case of mixed tank system. When the radiator is operated during some limited hours, the total consumption of electric power becomes smaller than that when the radiator is continuously operated all night. The consumption decreases as the capacity of the thermal energy storage tank increases. There are also most effective conditions in the flow state of the pumps and the operating temperature of the fan-coil cooler. With a stratified tank, the total consumption of electric power is decreased in the maximum by about 3% compared with mixed one. This system has a possibility to save energy or make the uneven consumption of electric power uniform throughout a 24-hour period.

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.

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.

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

Providing Feedback of power consumption by using Advanced Metering Infrastructure (AMI) and smart meters is considered the best method to lower consumers' power consumption. Automatic analysis of power consumption data that provides information on user activity is essential in order to provide effective feedback in time, and advanced services such as automatic power load control based on context information. The activity should be matched with user's behavior in order to make it understandable for the user and readable for the machine. In this paper, a new method for extracting activities from power consumption data is proposed. By using the concept of activities in daily living (ADLs), the method extracts the activities as tasks from context information such as identification and location of electric appliances and temporal power consumption from the AMI and the smart meters. Using ontology, the tasks are tagged by semantic meta-information in order to be used for sophisticated user friendly services.

In Smart Grids the Information and Communication Technologies (ICT) could be used to better manage both consumption and production of electricity. The increasing presence of renewable energy sources in production and the permeation of novel consumption types (e.g. Plug-in Hybrid Electric Vehicles (PHEV)) will obviously cause the increase the fluctuation of electrical energy. One possible solution to these problems is development of novel methods for investigating electrical power consumption data series. As the existing learning algorithms of pattern classification are suitable for discovering internal structures of large datasets, it is important to generate a training/testing/validation learning database from existing measurements (e.g. from smart meters), actually via segmentation and labeling by hand. In this paper we propose a novel method for the automatic segmentation with a predefined confidence level. The algorithm is based on the generalized Jensen-Shannon divergence (JSD), and it estimates the change-points (CPTs) in electrical power consumption data. Both the method and some recent results in segmenting one household's power consumption data are presented in this paper.

Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.91, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> = 1.7 ·10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> <; 0.05) than with the conventional method ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.81, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> = 2.6 ·10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> <; 0.05) from the initial correlation with the uncorrected data ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.70, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> = 4.5 · 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> <; 0.05). The new method proves therefore to be very efficient for saturated light correction.