Modeling and Analysis of Real-Time Software Based on Resource Timed Communicating Sequential Process

Reliability-Aware Energy-Efficient Scheduling for (m, k)-Constrained Real-Time Systems Through Shared Time Slots

ABSTRACTPollutant abatement systems are widely applied in the coal-fired power sector, and the energy consumption is considered an important part of the auxiliary power. An energy consumption analysis and assessment model of pollutant abatement systems in a power unit was developed based on the dynamic parameters and technology. The energy consumption of pollutant abatement systems in a 1000-MW coal-fired power unit that meets the ultra-low emission limits and the factors of operating parameters, including unit load and inlet concentration of pollutants, on the operating power were analyzed. The results show that the total power consumption of the pollutant abatement systems accounted for 1.27% of the gross power generation during the monitoring period. The wet flue gas desulfurization (WFGD) system consumed 67% of the rate, whereas the selective catalytic reduction (SCR) and electrostatic precipitator (ESP) systems consumed 8.9% and 24.1%, respectively. The power consumption rate of pollutant abatement systems decreased with the increase of unit load and increased with the increase of the inlet concentration of pollutants. The operation adjustment was also an effective method to increase the energy efficiency. For example, the operation adjustment of slurry circulation pumps could promote the energy-saving operation of the WFGD system.Implications: The application of pollutant abatement technologies increases the internal energy consumption of the power plant, which will lead to an increase of power generation costs. The real-time energy consumption of the different pollutant abatement systems in a typical power unit is analyzed based on the dynamic operating data. Further, the influence of different operating parameters on the operating power of the system and the possible energy-saving potential are analyzed.

As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20–50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of real-time systems by 31.1% on average under various workload conditions.

Through a comparative analysis, this paper attempts to summarize the hourly variations of the total energy consumptions and itemized energy consumptions in a culture and education comprehensive building in summer, winter, and the transitional season. The results show a great difference in energy consumptions in the same building in different seasons. Energy consumption is highest in winter. There is a minor difference in energy consumptions between a typical day in summer vacation and a normal work day in summer. The lowest energy consumption is in winter vacation, but a typical day in winter vacation would still consume about half of the energy consumed on a normal working day in winter. Keywords: Comprehensive building; analysis of energy consumption; itemized energy consumption

With the process of non-functional properties research on real-time systems, resource estimation and analysis of realtime systems become a hotspot. Process Algebra is a formal method fit for analyzing the functional properties of real-time systems, but it can not analyze the resource properties. Resource Communicating Sequential Process (RCSP) proposed in this paper can handle it efficiently by extending resource semantics on Communicating Sequential Process (CSP). In this paper, the resource consumption of real-time instructions is mapped into the resource of RCSP, then the resource consumption of realtime systems can be modeled and optimized by using RCSP; the resource optimal checking algorithms are proposed to check the resource satisfiability of instructions and calculate the minimum resource and accumulated resource of real-time systems. This formal method improves the accuracy and efficiency of resource calculation, the calculation results can be used to quantitatively analyze and optimize the resource consumption of real-time systems.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

The rapid expansion of Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) protocol technologies in large-scale Internet of Things (IoT) deployments highlights the need for precise and analytically grounded energy consumption (EC) estimation of battery-powered LoRa end devices (DVs). Since LoRa DV instantaneous EC strongly depends on key transmission parameters, primarily including spreading factor (SF), transmit (Tx) power, and LoRa message packet size (PS), accurate modelling of their combined influence is essential for optimizing LoRa end DV lifetime, ensuring energy-efficient network operation, and supporting transmission parameter-adaptive communication strategies. Motivated by these needs, this paper presents a comprehensive multiple linear regression modelling framework for quantifying LoRa end DV EC during one transmission and reception LoRa end DV Class A communication cycle. The study is based on extensive high-resolution electric-current measurements collected over 69 measurement sets spanning different combinations of SFs, Tx power levels, and PS values. Based on measurement results, a total of 14 multiple linear regression models are developed, each capturing the joint impact of two transmission parameters while holding the third fixed. The developed regression models are mathematically formulated using linear, interaction, and polynomial terms to accurately express nonlinear EC behavior. Detailed statistical accuracy assessments demonstrate excellent goodness of fit of the developed EC multiple linear regression models. Complementary numerical analyses of regression models EC data distribution further validate regression models’ reliability, and highlight transmission parameter-driven variability of Lora end DV EC. The results of numerical analyses for LoRa end DV EC data distribution show that specific combinations of SF, Tx power, and PS transmit parameters amplify or mitigate EC differences, demonstrating that their joint variability patterns can significantly alter instantaneous energy demand across operating conditions. These interactions underscore the importance of modelling parameters together, rather than in isolation. The developed regression models provide interpretable mathematical formulations of instantaneous LoRa end DV EC prediction for transmission at different combinations of transmission parameters, and offer practical value for energy-aware configuration, battery-lifetime planning, and optimization of LoRa network-based IoT systems.

Aluminum production is a major energy consumer and important source of greenhouse gas (GHG) emissions globally. Estimation of the energy consumption and GHG emissions caused by aluminum production in China has attracted widespread attention because China produces more than half of the global aluminum. This paper conducted life cycle (LC) energy consumption and GHG emissions analysis of primary and recycled aluminum in China for the year 2020, considering the provincial differences on both the scale of self-generated electricity consumed in primary aluminum production and the generation source of grid electricity. Potentials for energy saving and GHG emissions reductions were also investigated. The results indicate that there are 157,207 MJ of primary fossil energy (PE) consumption and 15,947 kg CO2-eq of GHG emissions per ton of primary aluminum ingot production in China, with the LC GHG emissions as high as 1.5–3.5 times that of developed economies. The LC PE consumption and GHG emissions of recycled aluminum are very low, only 7.5% and 5.3% that of primary aluminum, respectively. Provincial-level results indicate that the LC PE and GHG emissions intensities of primary aluminum in the main production areas are generally higher while those of recycled aluminum are lower in the main production areas. LC PE consumption and GHG emissions can be significantly reduced by decreasing electricity consumption, self-generated electricity management, low-carbon grid electricity development, and industrial relocation. Based on this study, policy suggestions for China’s aluminum industry are proposed. Recycled aluminum industry development, restriction of self-generated electricity, low-carbon electricity utilization, and industrial relocation should be promoted as they are highly helpful for reducing the LC PE consumption and GHG emissions of the aluminum industry. In addition, it is recommended that the central government considers the differences among provinces when designing and implementing policies.

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.

In the period of acute problems of traditional energy, particularly depletion of fossil fuels, which increase the negative impact on the environment and the rapid growth of energy prices, the increasing urgency acquire alternative energy sources, namely, solar. In this article will consider the analysis of energy consumption and payback time of PV systems. Setting them in the different countries depends mainly on the geographical location, in this connection, they have different energy intensity, if you take two identical systems and pays off in different periods of time. Payback depends on the PV systems except for geography and the economy of the country. For example, in Northern Europe, this would require about two and a half years, while in the south it will be half a year or a little less in Sicily for about a year. Comparing the energy consumption and time of return PV systems in foreign countries and in Russia, we see that the figures in foreign countries is lower than in Russia.

Modelling of energy consumption in electric vehicles is important for operation planning and smart charging strategies. This study proposes an energy consumption modelling and estimation strategy for autonomous electrified shuttles used for public transportation in Linköping. Operational experiences have highlighted the significant influence of weather conditions on the vehicles’ range, and the main complicating factor here is that detailed HVAC system data is unavailable. Therefore, a model is developed for estimating the energy consumption of the shuttles based on a physical formulation of vehicle dynamics and thermodynamics principles under varying weather conditions. The estimation relies on a fusion of predicted meteorological data, such as ambient temperature, and historical shuttle records, including interior temperature, vehicle speed, and discretized State of Charge (SoC). The lack of detailed HVAC operation information, e.g., cooling followed consecutively by heating within the same energy prediction profile, makes energy consumption estimation a challenging task. Even with some dynamic data available specifically for the shuttle’s short waiting time, these challenges render the HVAC system unobservable for nearly identical operations. To overcome this issue, a statistical approach combined with parameter estimation is employed to determine vehicle dynamic parameters and compute the thermal energy consumption and parametrization of the HVAC system. The data-driven model shows a satisfactory fitting level of R 2 with 0.85.

Thermal energy consumption in selected buildings in three residential estates in a city in northeastern Poland was analysed based on recorded data from 2010 to 2021. Multifamily buildings of varying construction technology, floor area, and number of stories were examined. 47 residential buildings were analysed, some of which contained commercial premises related to services for the estate's residents. These buildings meet current thermal protection standards, have unheated basements, and the top floors are generally heated and occupied. Outside air temperature was considered based on archived meteorological data and standard climate data. Electricity and gas consumption in this city was also analysed for selected apartments occupied by one adult, two adults, and a family of four, based on actual consumption. The aim of this article is to obtain better energy consumption estimates in the event of metering device failures, as well as to better forecast energy consumption based on the size of residential buildings and the number of occupants than is possible based on building design data. The findings may be useful in estimating lump sum fees that consider actual energy consumption in the form of heat, electricity, or gas combustion, as well as in planning residential renewable energy sources. The analysis of buildings' thermal energy consumption is particularly interesting because in recent years, there have been no occurred values of calculated outside air temperatures for winter. The calculations consider real outside air temperatures based on historical meteorological measurement data.

The approach that could reduce the energy consumption of water-cooled chillers in office buildings was investigated through experimental and simulation methods. The chiller was modelled in TRNSYS and validated using physical measurements from an operational water-cooled water chiller and fan coil system. The validated model was used to analyse the energy consumption of the water chiller-fan coil system under a high chilled water temperature setpoint (CWTS) in a representative commercial office building. Subsequently, indoor thermal comfort was evaluated using the PMV-PPD model. Finally, the design of experiment (DOE) was employed using a statistical two-level non-randomized factorial design in Minitab to study the effects of high CWTS, number of rows, and tube diameter on the heat and mass transfer performance of the fan coil unit. The results showed that the CWTS can be increased by various degrees from 10 up to 18 °C for energy efficiency for a commercial office building in the tropics. This increase in CWTS would result in a daily energy saving potential of about 5% of the chiller as compared to the existing operational settings without any extra cost. Conversely, the daily energy consumption by the fan coil would increase by about 5.5% by this increment in the CWTS. It was determined that the chiller system can provide comfort even when the CWTS is increased to 14 °C. The DOE analysis showed that under the condition of high CWTS (14 °C), energy consumption that is less than the current energy consumption may be expected from the fan coil system when the number of rows increased from 3 to 6 and the tube diameter increased from 7 to 9 mm.

This study investigates the energy consumption and losses associated with the gas production process in a condensate gas reservoir-type gas storage system. The energy consumption linked to each unit and key equipment was determined by HYSYS simulation, followed by a sensitivity analysis and exergy analysis. The findings reveal that the condensate oil stabilization tower is the primary energy-consuming equipment, responsible for 70.61% of the total energy consumption (3.82 × 105 kJ·h−1/1%). The temperature of the condensate reboiler is identified as the most significant influencing factor. Furthermore, the equipment exhibiting the highest exergy loss is the J-T valve (1.2 × 107 kJ·h−1), which contributes to 25.23% of the total loss. Consequently, to mitigate energy consumption in the gas production system, it is crucial to control the temperature of the condensate oil reboiler. Enhancing efficiency will rely on recovering the pressure energy loss associated with the J-T valve. The field gas gathering system lacks sub-unit energy consumption measurement and flow measurement for key process fluids. This study can provide methodological and data references for optimizing the operation of this condensate oil–gas reservoir-type storage facility.

PurposeThis paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.Design/methodology/approachThe paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.FindingsThe paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.Research limitations/implicationsBecause of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.Practical implicationsThe paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.Originality/valueThis paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.