Research of RBFNN-based Traffic Energy-Consumption Model

The article is devoted to the analysis of the potential for increasing the efficiency of electricity consumption in residential and administrative buildings in the presence of various energy sources and storage systems. This task is relevant in view of the spread of renewable energy sources both in centralized power supply systems and at local facilities and the significant impact of unstable generation on the cost of electricity in the market. In particular, the main consumers of electricity, which can be used as "consumers-regulators", as well as storage systems available for use in buildings, are considered. Taking into account that a significant amount of electricity is used or can be used in building air-conditioning systems, as well as for the needs of hot water supply, the construction of mathematical models of such systems for existing buildings will allow the creation of effective electrical load management systems to optimize energy costs for traditional buildings and ensure autonomy for buildings with close to zero energy consumption. The article describes the priority tasks to be solved to build an energy management system to minimize energy costs and increase the efficiency of the country's energy system and local energy systems of communities. The formulation of the tasks follows from the analysis of information on the actual consumption schedules of residential and public buildings and the analysis of their mutual correlation, which allows using these findings to develop algorithms for monitoring energy consumption and load management on the consumer side. Further stages of the study include additional measurements of actual energy consumption at the facilities with a discreteness of several seconds to an hour, while simultaneously recording potential influencing factors, which will allow creating a digital model of building energy consumption. A preliminary analysis of the research shows that the most promising mathematical apparatus for building such a model is the use of neural networks.

Development of an energy-efficient signal control system that minimizes the total electricity usage of all electrical vehicles (EVs) running through intersections has been considered one of the most effective approaches for addressing the EV energy-efficiency problem when EVs gain significant market penetration. The critical first step in developing such a system is to derive an aggregate energy consumption function, which can serve as an objective function for energy-based signal optimization. The research described here addresses this challenge by deriving an aggregate energy consumption model (AECM) for EVs at an intersection. The proposed AECM was developed by integrating an individual EV’s energy consumption model with a simplified car-following model; this described the trajectory of each EV by assuming that its driving maneuvers could be simplified into four phases: cruising, decelerating, idling, and accelerating. The proposed model has been evaluated by using detailed vehicle trajectory data provided by the Next Generation Simulation program. The evaluation results indicated consistency between the energy consumption estimated by the proposed AECM and the disaggregated individual EV’s energy consumption model. The proposed aggregate energy consumption function is expected to be used for searching for an optimal signal control strategy that could minimize total energy consumption at a signalized intersection.

To better construct an energy consumption model for the asphalt mixture production stage, this study divides the production process into four phases: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. Utilizing measured data from various regions in Anhui Province, the model considers factors such as season, mixture type, and energy consumption type. The study quantitatively compares and analyzes the energy consumption results obtained from three calculation methods: the theoretical method, the quota method, and the energy consumption model method. The results indicate that the total energy consumption shares of the aggregate dust removal, aggregate drying, asphalt heating, and mixture blending phases are 4.23%, 69.50%, 17.75%, and 8.52%, respectively. The primary energy consumption during the asphalt mixture production stage is concentrated in the aggregate drying and asphalt heating phases. The energy consumption model based on on-site measurements effectively reflects the actual energy consumption levels during the asphalt mixture production stage. Moreover, the total energy consumption calculated by the three methods follows the order quota method > energy consumption model method > theoretical method. By constructing an energy consumption model based on measured data, it is possible to more accurately assess the energy usage during the asphalt mixture production process. This helps optimize production techniques, reduce energy consumption and carbon emissions, and provide a scientific basis for sustainable road construction.

While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms. In this work, we analyze the accuracy of a widely-used application-level model that have been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energy-efficient workflow scheduling literature.

Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.

The dynamic process of movement of vehicles in cities is associated with interaction with objects of the road environment. Road conditions, traffic and pedestrians require certain actions from the driver to ensure road safe-ty. Under such circumstances, the reliability and safety of road traffic in human-machine systems mainly depends on the functional state of the driver (provided that the technical component, the subsystem is “car”) and his ability to respond to changes in the traffic situation when obstacles arise (conflict situations). The development of states of tension and fatigue of the driver is associated with an increase in energy costs, which leads to a natural in-crease in the heart rate, respiration and other parameters indicating changes in the energy balance of the driver's body. Eye Tracking technology has been applied to detect attention. Energy costs were determined using a method that takes into account the frequency of the driver's external respiration when driving on the road and in the background. As a result, a model of change in the attention of drivers with driving experience and a model of en-ergy consumption when driving in the evening twilight at a certain level of illumination were obtained. To determine the influence of conflict situations during movement in the evening twilight on the indicator of attention and energy consumption of the driver, a study was carried out in real conditions on city streets. A group of drivers with experience of 3-5 years and 10-16 years performed arrivals in the evening twilight on passenger cars "C" class. Experimental studies of the indicator of drivers' attention and functional state in conditions of evening twilight were carried out on different categories of city streets. The study involved drivers between the ages of 20 and 40. The obtained dependences of the change in the driver's functional state on illumination showed that at low illumination levels in the evening twilight, a rapid increase in the driver's functional state shifts. Thus, the energy consumption of the driver while driving depends on the types of conflict situations and the level of illumination. It was found that for drivers with less than 5 years of driving experience, shifts in functional state were more im-portant than other drivers. Also, such drivers had lower attention indicators compared to experienced drivers with more than 10 years of driving experience. It was found that when the illumination changes at dusk, the energy consumption increases on average to 3.3 kcal / min for drivers with less than 5 years of experience, and for drivers with more than 10 years of experience, it increases by 2.4 kcal / min on average. Keywords: driver, conflict situations, attention, energy costs.

Performance analysis of rotatable energy system of high-altitude airships in real wind field

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Power-based electric vehicle energy consumption model: Model development and validation

Recent statistics of energy consumption by Cloud datacenter show the DCs consumes more and more energy each year that created big challenge in Cloud research. IT industry is keenly aware of the need for Green Cloud solutions that save energy consumption in Cloud DCs. A great deal of attention has been paid to minimize energy consumption in cloud datacenter. However, to understand the relationships between running tasks and energy consumed by hardware we need to propose mathematical models of energy consumption. The models of energy consumption can be help as to saving energy. Both researchers aim to proposed mechanism for energy consumption. In this paper, we analyzed the relationships between Cloud system manager and energy consumption. This paper aims at proposing and designing energy consumption models with mechanism of prediction energy.

Driving range is one of the main obstacles to the wide diffusion of electric vehicles. In order to overcome it without needing to increase battery size and price of the vehicle, one promising solution consists in leveraging advanced driver assistance systems to increase and master the driving range. This paper proposes model-based strategies to predict and optimize the energy consumption of a trip. Before the trip, an energy efficient route is suggested. During the trip a precise prediction of the current driving range is provided, and an optimal speed profile is computed to advise the driver. These strategies take into account the specific vehicle parameters, as well as the topology of the road network in which the vehicle operates, and the real-time traffic conditions. A macroscopic version of the energy consumption model of the electric vehicle is presented in order to use the aggregated real-time data available on typical maps web-services. The road network is modeled as a weighted directed graph adapted to the proposed energy consumption model. The energy driving range and the optimal route are finally obtained by means of a suitable optimal path search algorithm. For eco-driving, a different approach using an artificial neural network has been chosen to enable real-time implementation. As for the human–machine interface, the output of these strategies is finally suggested to the driver via a smartphone application. Experimental results show promising gains as compared to the existing approaches in predicting vehicle energy consumption, in suggesting an efficient route, and in providing eco-driving assistance.

This paper presents a realistic model of the radio energy consumption for Bluetooth-equipped sensor nodes used in a low-duty-cycle network. The model is based on empirical energy consumption measurements of Bluetooth modules. This model will give users the possibility to optimize their radio communication with respect to energy consumption while sustaining the data rate. This paper shows that transmission power cannot always be directly related to energy consumption. Measurements indicate that, when the transmission power ranges from $-$5 to $+$10 dBm, the difference in consumed energy can be detected for each transmission peak in the sniff peak. However, the change is negligible for the overall energy consumption. The nonlinear behavior of the idle state for both master and slave when increasing the interval and number of attempts is presented. The energy consumption for a master node is in direct relation to the number of slaves and will increase by approximately 50% of the consumption of one slave per additional slave, regardless of the radio setting.

Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system's energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

A novel multivariate grey model of energy consumption under carbon trading background and its application