Calculation Of Energy Consumption Research Articles

Fuel Cell Electric Vehicle Characterisation under Laboratory and In-use Operation

Fuel Cell Electric Vehicles (FCEVs) are one of the main options considered as possible future alternatives to conventional vehicles. The paper evaluates a light-duty FCEV under both laboratory and on-road conditions. Hydrogen consumption and vehicle efficiency were measured over various driving and temperature conditions. Results show hydrogen consumption between 0.975kg/100km and 1.5kg/100km, close to the officially declared value. A new extended formulation of vehicle efficiency is proposed to consider different energy streams in the vehicle. Vehicle indicative efficiency remained between 50 and 60% on the majority of cases. The paper also investigates the feasibility of applying a fuel cell system of similar characteristics for commercial urban and rural applications, the Vehicle Energy Consumption Calculation TOol (VECTO), as a possible alternative solution to a battery electric equivalent vehicle.

Analysis of Energy Consumption and Economy of Regional Gas Tri-Supply Composite System

With the development of Chinese society, there is an increasing demand for emissions reduction and the stable operation of the power grid. Regional comprehensive energy supply systems have entered the public’s view owing to their advantages of reducing capacity, unified dispatch, improving efficiency, and reducing energy consumption. This paper focuses on a system under construction in Chongqing, which adopts a combined gas tri-supply (combined cooling, heat, and power, CCHP) and dynamic ice storage cooling system as the research object. By establishing a mathematical model for the simulation research, this study examines the start–stop priority sequence of the gas tri-supply subsystem and the heat pump subsystem under the ice storage priority strategy in winter and summer and proposes corresponding optimization solutions. By comparing the annual operating energy consumption of the system, we conclude that the gas tri-supply composite system has good economic efficiency and peak-shaving capability, indicating that regional gas tri-supply composite systems have great application potential in the future. The proposed optimized operation strategy and simulated energy consumption calculation provide theoretical guidance for the construction and operation of both this project and similar projects.

Energy-saving design of underground metro vertical alignment with deep reinforcement learning

The design of metro vertical alignment greatly affects train traction energy consumption. This study develops a deep reinforcement learning (DRL) algorithm for an energy-saving design of underground metro vertical alignment and analyzes the influence of train traction energy consumption calculation factors (CFs) on energy-saving design. First, a metro train operation simulation model is adopted using the approximate integral method, and the value range of CFs as input parameters is determined. Second, the 'agent', 'state', 'action', and 'reward' of the metro vertical alignment environment and the corresponding alignment constraints are determined. Third, a Dueling Double Deep Q Network (D3QN) is constructed to design the energy-saving metro vertical alignment. The proposed model is applied to a real-world metro case study, and the influence of CFs (design speed, limiting gradient in reverse slopes, and passenger capacity) on energy-saving design is analyzed. Finally, compared to manual design, the D3QN-based design outperforms GA-based design and DQN-based design, and demonstrates a remarkable energy savings of 6.17%.

Calibration of simulation model to analyze hospital building energy performance

This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry.

Optimizing the dimensional ratio and orientation of residential buildings in the humid temperate climate to reduce energy consumption (Case: Rasht Iran)

Today, the increase in the number of cities and greenhouse gas increases has led to the intensification of global warming. The construction industry has had a significant impact on this crisis. Therefore, using passive solutions such as optimizing building proportions and proper orientation in any climate can significantly help reduce building energy consumption and greenhouse gases. The research aims to optimize the proportions of residential buildings and their orientation to minimize the building's energy consumption in the city of Rasht with a moderate climate. For this purpose, a three-story building with dimensions of 1:1 was selected, and the energy consumption of the building, including the load of cooling, heating, and lighting, was calculated in the north-south and east-west dimensions up to the ratio of 1–10. This research has used validated software to calculate energy consumption. Then, the most optimal mode was selected in terms of proportionality; the energy consumption of the building with a different orientation compared to the geographical north was calculated for each degree up to 360°. The results showed that the energy consumption of the building with a ratio of 1:4 with east-west elongation was 155.29 kWh/m2, which has the lowest energy consumption compared to other modes and is also compatible with the construction and building industry. Also, the building with zero-degree orientation to the geographic north and east-west elongation has the lowest carbon dioxide production and annual energy consumption, which are 2107 kg/m2 and 146.28 kWh/m2, respectively. By using two primary parameters, i.e., building orientation and aspect ratio, this research presents a valid innovation in optimizing the energy consumption of buildings. This research is focused on the detailed analysis of the environmental performance of a building in Rasht climate and determining the best form and orientation to reduce energy consumption and environmental efficiency.

Guidelines for data collection on energy performance of higher-education buildings in Egypt: a case study

ABSTRACT Reliable energy analysis of buildings relies heavily on high-quality data leading to proper indicators. Previous studies have highlighted the importance of data quality in analyzing energy usage in residential and non-residential buildings in order to transform declarations to actions, optimise energy efficiency policies and monitor progress and failures in countries. Collected data must adhere to national and international standards for energy performance in buildings. This study aims to provide practical guidelines for effectively collecting and preparing data suitable for evaluating energy performance in Egyptian higher-education (HE) buildings. The guidelines are developed based on a comprehensive case study, considering data availability in typical educational facilities. Architectural and civil engineering drawings, construction specifications, and occupancy details are accessible. However, actual monthly electrical and natural gas consumption data for individual buildings are lacking. To address this, the study proposes the creation of detailed datasheets for each building, encompassing all energy sources and their electrical and power specifications, such as equipment, machinery, and HVAC systems. These datasheets were utilized to calculate energy consumption and energy usage indicators (EUI). The findings demonstrate that the datasheets enable adequate assessment of energy usage in various spaces within educational buildings, including staff rooms, lecture halls, and laboratories. This facilitates the identification of areas in need of targeted energy efficiency improvements. Notably, the study reveals that electricity consumption in the Faculty of Engineering building is significantly influenced by PCs, laboratories, lighting, and air conditioning.

MODELING THE PROCESS OF SALING MEAT IN HORIZONTAL DRUMS

The article presents new mathematical models necessary for establishing optimal modes for massaging meat in horizontal drums that are completely wrapped. The results of their analytical investigation of the requirements for extracting salted meat products with increased yield and tender structure were presented. The new results of analytical-theoretical and experimental studies of the trajectories of the movement of pieces of meat in slowly rotating horizontal drums are presented, the conditions and rational modes of their use during massaging of meat with different structural and mechanical properties, which are used in the production of salted meat, are established products It has been proven that the equipment, the working organs of which have the shape of a drum with a smooth surface, the presence of activator devices of different shapes in the middle of the drum, allows you to speed up the process of massaging meat with a blow when falling from a height. The resulting mathematical models make it possible to calculate energy consumption for massaging meat, to positively influence the technology of salted meat products. The new results of analytical-theoretical and experimental studies of the trajectories of the movement of pieces of meat in slowly rotating horizontal drums are presented, the conditions and rational modes of their use during massaging of meat with different structural and mechanical properties, which are used in the production of salted meat, are established. products It has been proven that the equipment, the working organs of which have the shape of a drum with a smooth surface, the presence of activator devices of different shapes in the middle of the drum, allows you to speed up the process of massaging meat with a blow when falling from a height. The resulting mathematical models make it possible to calculate energy consumption for massaging meat, to positively influence the technology of salted meat products.

Techno-exergy-economic assessment of humidification-dehumidification/reverse osmosis hybrid desalination system integrated with concentrated photovoltaic/thermal

Desalination is a process used to generate water for human consumption, irrigation, or industrial purposes. Assessment of desalination plants is crucial for freshwater productivity, energy consumption, exergy destruction, and economic feasibility calculations. Therefore, the study aims to conduct a technical, exergy, and economic analysis of a concentrated photovoltaic/thermal hybrid humidification-dehumidification reverse osmosis desalination system. The photovoltaic/thermal provides the electrical and thermal requirements for the hybrid desalination unit. Theoretical models for each unit are developed and integrated using the Engineering Equation Solver software. The sensitivity of system performance parameters was investigated under different climatic and operation factors. The analysis shows that the system produces 860 L per hour at the lowest specific energy consumption and water cost (3.3 kW-hours per cubic meter and 0.916 United States dollars per cubic meter), respectively, with a cost payback period of only 12 years compared to other green-powered desalination systems. The highest destruction portion of 84.6 % is at photovoltaic/thermal, while the largest cost portion of 68 % is at reverse osmosis. Productivity, energy consumption, and water cost are influenced by feed salinity, while destruction is most affected by solar irradiance. Ambient temperature, wind speed, and cooling fluid flow rate slightly affect the techno-exergy-economics of the system.

Integrative function of proprioceptive system in the acute effects of whole body vibration on the movement performance in young adults.

The proprioceptive system coordinates locomotion, but its role in short-term integration and recovery of motor activity in imbalance of motor patterns and body remains debated. The aim of this study is investigating the functional role of proprioceptive system in motor patterns and body balance in healthy young adults. 70 participants (aged 20.1 ± 0.3) were divided into experimental groups EG1 (n = 30), EG2 (n = 30), control group (CG, n = 10). EG1 performed single WBV session on Power Plate (7 exercises adapted to Functional Movement Screen (FMS). EG2 performed single session of FMS Exercises (FMSE). CG didn't perform any physical activity. All participants performed pre- and post-session of FMS and stabilometric measurements. FMS total score in EG1 increased by 2.0 ± 0.2 (p0 < 0.001), this was significantly differed (p0 < 0.001) from EG2 and CG. Acute effects of WBV and FMSE on rate of change and standard deviation (SD) of pressure center (COP) were shown in all groups during Static Test (p0 < 0.01). SD increased (p0 < 0.01) in Given Setting Test in EG1 and EG2, and in Romberg Test (p0 < 0.001) in EG1. Length, width and area (p0 < 0.01) of confidence ellipse, containing 95% of the statokinesiogram points, decreased in Static Test in EG1; width and area (p0 < 0.01) decreased in EG2 group. Significant (p0 < 0.01) decrease in Given Setting Test was in EG1, and significant (p0 < 0.01) increase was in Romberg Test (open eyes) in CG. Maximum amplitude of COP oscillations: significantly (p0 < 0.01) decreasing along X and Y axes in EG1 and EG2, and along Y axis in CG during Static Test; along Y axis (p0 < 0.01) in all groups during Given Setting Test. Significant differences were identified (p0 < 0.01) in calculated energy consumption for COP moving during all stabilometric tests. However, inter-group differences in COP after acute WBV and FMSE sessions have not been identified. Acute WBV session eliminates the deficits in motor patterns which is not the case after acute FMSE session, which, according to our integrative movement tuning hypothesis, is due to high activation of integrative function of proprioceptive system. Efficacy of WBV and FMSE on COP performance indicates a high sensitivity of postural control to different levels of proprioceptive system activity.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN ENERGY CONSUMPTION CALCULATIONS TO REDUCE EXCESS GENERATION IN THE CONTEXT OF UKRAINE'S RECOVERY

In authors' opinion, the relevance of implementing artificial intelligence in the calculation of energy consumption in order to reduce excess generation lies in several key aspects: 1) efficient use of resources (by analysing data and predicting energy consumption patterns using artificial intelligence, the operation of the energy system can be optimised, ensuring efficient use of energy resources and avoiding excessive electricity generation); 2) reduction of losses (artificial intelligence can help identify and eliminate problematic segments in energy systems, leading to a reduction in energy losses during transport and distribution); 3) consumption forecasting (artificial intelligence can predict and respond to energy consumption peaks, ensuring the stability of energy supply and avoiding overloading of energy systems); 4) resource conservation and emissions reduction (efficient management of energy consumption using artificial intelligence can lead to reduced fuel consumption and greenhouse gas emissions, promoting more sustainable and environmentally friendly development). Artificial intelligence is a field of computer science that deals with the creation of programs and systems capable of performing tasks that typically require human intellectual abilities. These systems can exhibit cognitive functions such as image recognition, language understanding, decision making, self-learning and planning. Artificial intelligence uses methods and techniques from computer science, mathematics, linguistics, philosophy and other fields to design and implement intelligent systems. Artificial intelligence in the energy sector is the application of AI methods and technologies to optimise energy production, transmission, distribution and consumption. This includes the development of algorithms and systems that can automatically analyse large amounts of data, predict energy demand, optimise energy processes, maintain the stability of energy networks and reduce energy losses. The application of artificial intelligence to energy can help increase the efficiency of energy production, reduce environmental impact and improve the reliability of energy systems. The subject of the study is the introduction of artificial intelligence in the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery. The research methods for introducing artificial intelligence into the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery are a system of general scientific and special methods of scientific knowledge. The purpose of the study is to determine the possibilities of introducing artificial intelligence into the calculation of energy consumption to reduce excess generation in the context of Ukraine's recovery. Results. Investing in the use of artificial intelligence to calculate energy consumption in order to reduce excess generation in the context of Ukraine's recovery can be done through various investment instruments, including: venture capital (investments in start-ups and companies developing artificial intelligence technologies to optimise energy consumption); project financing (financing of specific projects using artificial intelligence to analyse and optimise energy consumption); corporate investments (investments in the development of in-house artificial intelligence systems for energy efficiency management at industrial enterprises); stock market (investments in shares and bonds of companies specialising in the development and implementation of innovative technologies for the energy sector); crowdfunding (raising funds from individual investors on platforms dedicated to the development of artificial intelligence projects in the field of energy efficiency).

Influences of parameters of film on acoustical absorption performance of copper film-copper fiber composite structure

Fiber porous materials excel at sound absorption, but their effectiveness suffers at thin thicknesses. This study proposed a novel copper film-copper fiber porous material (FFM) structure to overcome this limitation. We designed a composite material with improved sound absorption, by coating a thin copper film onto an existing copper fiber matrix. This innovative approach enables effective sound absorption when the sample thickness is small. Our analysis revealed that FFM structures are better than the individual copper film and fiber porous material regarding sound absorption, particularly near their resonance frequency. Notably, a 2 mm-thick FFM structure boasts an average sound absorption coefficient of 0.35, a remarkable fourfold increase compared to the individual copper fiber matrix (0.08). The further investigation explores the influence of copper film thickness and bond area percentage on acoustic performance. We successfully validate our simulation model against experimental data, enabling us to calculate energy consumption ratios for various design parameters. This provides research ideas for optimizing FFM structures for specific noise reduction applications.

Investigation of the removal of diclofop methyl herbicide by peroxy electrocoagulation process and kinetic and cost analysis

Pesticides containing chlorine, which are released during agricultural activities, are chemical substances that mix with surface and underground waters and have toxic, carcinogenic, and mutagenic effects on the entire living ecosystem. Due to their chemically stable structure, conventional water and wastewater treatment techniques such as coagulation, flocculation, and biological oxidation do not entirely remove these chemical substances. Therefore, before releasing them into the environmental receptor, these chemical substances must be transformed into harmless products or mineralized through advanced oxidation processes. When we look at the literature, there are not many studies on methods of removing diclofop methyl from aquatic media. Our study on the removal of diclofop methyl herbicide from aquatic media using the peroxy electrocoagulation method will provide the first information on this subject in the literature. In addition, this treatment method will contribute significantly to filling an important gap in the literature as an innovative approach for diclofop methyl removal. Moreover, peroxy electrocoagulation, which produces less sludge, provides treatment in a short time, and is economical, has been determined to be an advantageous process. The effects of conductivity, pH, H2O2 concentration, current, and time parameters on the removal of diclofop methyl were investigated using a GC–MS instrument. Kinetics, energy consumption, and cost calculations were also made. Under the optimum conditions determined (pH = 5, H2O2 = 500 mg/L, NaCl = 0.75 g/L, current density = 2.66 mA/cm2), the peroxydic electrocoagulation process resulted in a diclofop methyl removal efficiency of 79.2% after a 25-min reaction. When the experimental results were analyzed, it was found that the results fitted the pseudo-second-order kinetic model.

Multi-objective optimization approach for coverage path planning of mobile robot

Abstract Coverage path planning (CPP) is a subfield of path planning problems in which free areas of a given domain must be visited by a robot at least once while avoiding obstacles. In some situations, the path may be optimized for one or more criteria such as total distance traveled, number of turns, and total area covered by the robot. Accordingly, the CPP problem has been formulated as a multi-objective optimization (MOO) problem, which turns out to be a challenging discrete optimization problem, hence conventional MOO algorithms like Non-dominated Sorting Genetic Algorithm-2 (NSGA-II) do not work as it is. This study implements a modified NSGA-II to solve the MOO problem of CPP for a mobile robot. In this paper, the proposed method adopted two objective functions: (1) the total distance traveled by the robot and (2) the number of turns taken by the robot. The two objective functions are used to calculate energy consumption. The proposed method is compared to the hybrid genetic algorithm (HGA) and the traditional genetic algorithm (TGA) in a rectilinear environment containing obstacles of various complex shapes. In addition, the results of the proposed algorithm are compared to those generated by HGA, TGA, oriented rectilinear decomposition, and spatial cell diffusion and family of spanning tree coverage in existing research papers. The results of all comparisons indicate that the proposed algorithm outperformed the existing algorithms by reducing energy consumption by 5 to 60%. This paper provides the facility to operate the robot in different modes.

Axial Crushing Theory and Optimization of Lattice-Filled Multicellular Square Tubes.

A lattice-filled multicellular square tube features a regular cross-sectional shape, good energy consumption, and good crashworthiness, which is suitable for the design of energy absorbers in various protection fields such as automobiles, aerospace, bridges, etc. Based on the super folding theory, two reference planes are set to refine the energy consumption zone of the super folding element in this study. The energy consumption calculation of convex panel stretching is involved, and the critical crushing force formula is introduced in this study. Meanwhile, the calculation method from a single-cell square tube to a multicellular thin-walled square tube is extended and the structural optimization is investigated, in which the NSGAII algorithm is used to obtain the Pareto front (PF) of the crashworthiness performance index of the square multicellular tubes, the Normal Boundary Intersection (NBI) method is adopted to select knee points, and the influence of different cross-sectional widths on the number, as well as the thickness, of cells are discussed. This study's results indicate that the theoretical value is consistent with that obtained from the numerical simulation, meaning that the improved theoretical model can be applied to predict the crashworthiness of multicellular square cross-sectional tubes. Also, the optimization method and study results proposed in this study can provide a reference for the design of square lattice multicellular tubes.

Upcycling use of red mud-based solid waste in engineered cementitious composites: Properties, activation mechanism, and life-cycle assessment

In this study, a low-carbon engineered cementitious composite (ECC) was produced by combining alkali-activated red mud (RM)/ground granulated blast-furnace slag (GGBS) with polyvinyl alcohol (PVA) fibers as the reinforcement. Both calcium aluminosilicate hydrate (C-A-S-H) and sodium aluminosilicate hydrate (N-A-S-H) gels are detected in the reaction products. A higher modulus of sodium silicate in the activator results in a higher degree of reaction of silicates in the samples, but not promising a higher mechanical property. The potential mechanism of iron activation in the alkali-activation RM-based ECC system was revealed. The four-point bending test results indicate a large deflection ability and multiple cracking characteristics of the composite. Life cycle assessment of this system is also presented by calculating energy consumption and carbon emissions. This study not only sheds light on the feasibility of using RM in the preparation of high-toughness alkali-activated ECC but also provides a promising method to upcycle industrial solid wastes in a highly effective way.

Analysis of energy intensity indicators in production of mineral fertilizers

The article presents the results of the calculation of the energy consumption of the main and auxiliary technologies for the production of mineral fertilizers, interrelated as a single energy technology system (ETS). At the stage of the primary analysis of fuel, electricity and heat consumption indicators of individual technologies of the mineral fertilizers industry, the task was set to determine the energy intensity of specific technologies depending on intra-system distribution of flows of fuel and energy resources (FER) between the ETS production facilities for the output of target products.On the basis of a mathematical model, according to the averaged specific consumption rates of fuel and energy resources of the main and auxiliary technologies for the production of one ton of product, the values of specific energy consumption for target technologies for the production of mineral fertilizers were obtained. The analysis of the calculated indicators of energy consumption enabled to identify the most energy-intensive technologies.. It is shown that in the total energy consumption of the target technology, the share of energy consumption of auxiliary industries is up to 97% of the total energy intensity. Besides, the paper cosiders the key factors that determine the level of energy efficiency of industry enterprises under study.Through the example of an existing technology for the production of mineral fertilizers of one technological line with a capacity of 27.5 t/h (in terms of the finished product), the calculation of energy consumption was performed. The paper states that the modernization of the technology for the production of mineral fertilizers through the utilization of thermal SERs emitted into the environment would reduce the energy consumption of the technology by 6.33%.The mathematical model used in the study makes it possible to determine the values of energy consumption of technologies in the mineral fertilizer production industry with the greatest accuracy. The analysis of the data obtained shows that the studied technologies of the industry are characterized by irrational consumption of fuel and energy resources and, therefore, need a more thorough and systematic approach in determining energy saving reserves and ways to rationalize them in the energy technology system (ETS).

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

PurposeBecause of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.Design/methodology/approachThe proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.FindingsThe predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.Originality/valuePSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

PROPANE-BUTANE SUPPLY FOR COKE OVEN AND BLAST FURNACE GAS ENRICHMENT IN REHEATING FURNACES

This article proposes organizational and technical measures to reduce energy consumption of re-heating furnacessection of hot rolling mill 1700 in hot rolling shop No.1 of “ArcelorMittal Temirtau” JSC, such as fuel combustionquality management, performed and presented calculation of energy consumption per unit of production. The industrial experiment was performed there. As it is known, one of the features of re-heating furnaces is the fact that the minimum specific fuel consumption takes place when burning it with the maximum possible pyrometric effect. To solve the research tasks, a fundamentally new 3D model of combustion products motion with the possibility of parameters correction to calculate and optimize the energy-efficient operation of the furnace was applied. Experimental data were obtained on high-precision stationary and portable equipment, metering devices passed state inspection, which excludes the unreliability of the data obtained.

Energy Consumption Characteristics Study of Surface Meteorological Observation Station in North and South China

Surface meteorological observation is one of the most important sources of primary data for the meteorological sector and are crucial to the accuracy of weather forecasting. China’s meteorological authorities have built a dense network of surface observation stations, which require a considerable amount of electrical energy to keep the network running normally. There has been a lack of analytical studies dedicated to the energy consumption of surface meteorological observation stations domestically and internationally. This paper selects measured energy consumption data of 4 surface meteorological observation stations in two typical provinces (autonomous regions) in the north and south China over the past two years, and proposes, for the first time, to use the average power of the surface meteorological observation station, adopts the electrical energy consumption calculation method, carries out energy consumption calculation and analysis by month, sub-region and season, derives the characteristics of energy consumption changes of the surface meteorological observation station, and proposes energy saving and consumption reduction measures.

Green ICT Methodology for Energy Consumption Calculation in ICT Architecture Components

As the usage of information and communication technology (ICT) solution have been exploded in the last decades, the research community exhibits a deficiency in terms of methodologies for accurately quantifying the energy consumption of individual ICT components. This paper presents a novel methodology aimed at addressing the existing research gap by offering a comprehensive analysis of energy consumption ranges for ICT components, measured in Watt. The methodology being proposed involves the classification of ICT components into distinct classes, namely sensors, gateways, servers-platforms, and databases, while these classes are further subdivided into sub-classes. The calculation of energy consumption places emphasis on particular components within each sub-class, encompassing CPUs, RAM, GPUs, hard drives, network switches, cooling systems, and communication units. Compared with existing green ICT standards where mainly focused on strategy-related recommendations, the methodology utilises manufacturers’ specifications for standard operational procedures in order to compute energy consumption. The objective of the methodology is to extend the current research and provide countable energy consumption attributes of various component types, that are used in the ICT architecture, supporting the decision-making process of architects regarding environmentally sustainable and high-performance device selection. This study concludes with the development of a tree diagram for calculating the energy consumption of an ICT system.