Consumption Model Research Articles

Demand response strategies for air-conditioning systems based on direct load control: Considering envelope structures

In recent years, the surge in rural air-conditioning (AC) electricity consumption has led to an overall increase in peak loads in both winter and summer, presenting a major challenge to the reliability and stability of low-voltage distribution networks. Demand response (DR) strategy not only relieves the peak load, but also reduces the energy consumption and achieves the purpose of energy saving and emission reduction. Among the many DR strategies, direct load control (DLC) stands out as an effective way to quickly reduce building AC loads. Meanwhile, the building envelope is also one of the important factors affecting the AC load. A typical rural house in the Xi’an area of Shaanxi Province was used as a case study. A ground source heat pump system was developed, installed and used as an experimental platform in order to aid the study. Therefore, in this study, the combination of external envelope and DLC strategies was investigated to establish a simulation model of energy consumption in rural building complexes. Two DLC load control strategies were implemented under different external envelope conditions for annual AC energy simulation. The results show that the thickness of the external insulation layer has the greatest impact on the AC load of the building compared to the window-wall ratio and orientation. With a thickness of 0 mm–75 mm insulation, the DCCM strategy can effectively reduce AC energy consumption by 16%–18%; With a thickness of 0 mm–75 mm insulation, the TSCM strategy can effectively reduce AC energy consumption by 5%–8%.

Investigating the influence of multiplicative noise on available resources: a stochastic production–consumption model incorporating a logistic-like weak Allee effect

Investigating the influence of multiplicative noise on available resources: a stochastic production–consumption model incorporating a logistic-like weak Allee effect

Pore scale modeling on microbial hydrogen consumption and mass transfer of multicomponent gas flow in underground hydrogen storage of depleted reservoir

As first effort in literature, this study proposed a mathematical model for microbial hydrogen consumption of underground hydrogen storage in the depleted reservoir, considering phase-change mechanism in growth-decay of bio-film, methanogen reaction, and mass transfer in the multicomponent flow. Effects of the growth-decay mechanisms of methanogens on pore structure, permeability and gas components, were simulated and analyzed in 2D and 3D porous models. The results indicated that: 1) The potential risk of the clogging of the pore space by microbial community was confirmed. 2) The initial volume fraction of microbial saturation (VFm) has a great effect on the growth and decay mechanism of methanogen which adopts the hydrogen as the energy source. 3) The optimized decay coefficient of the biomass could be achieved to decrease the loss of hydrogen and its purity. 4) The reduction rate of porosity and normalized permeability (KN) was believed to be related to decay-growth mechanism of microbials and the pore structure. The model with a lower porosity would lead to a higher saturation of biofilm-water layer (BWL), as well as a higher reduction of the porosity. The required time to reach the maximum BWL was the same, when assuming the growth process of methanogen was homogeneous and isotopic.

Comparing and Parameterizing the Electrical Energy Consumption Models in SUMO

This paper examined the performances of the current four battery models in SUMO. The possibility of expanding the model parameterization was also investigated and the corresponding extension was carried out for PHEMlight. Accordingly, the models can be compared more fairly. Three scenarios were used, namely the Worldwide harmonized Light vehicles Test Cycle, a constant high-speed highway scenario and an area scenario with a relatively complex traffic situation. The results show that all models can address recuperation and propulsion, and deliver the similar result at very low acceleration. The models based on average vehicle data generally tend to deliver higher battery consumption than the models with individual vehicle type-specific parameterization, especially PHEMlight5, while HBEFA4 only has one electric vehicle class and is therefore not sensitive to various vehicle characteristics. Moreover, the model by Kurczveil and López (EVM) seems to tend to have the lowest consumption of all models.

Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions

The widening gap between real-world vehicle energy consumption and modeled predictions can be attributed to discrepancies between actual ambient temperatures and assumptions made in laboratory tests. This study collected a detailed, extensive dataset comprising 25,640,666 records of real-world vehicle operating (speed, acceleration, etc.) and fuel consumption data alongside 124,938 hourly meteorological profiles (temperature, relative humidity, etc.). High-resolution fuel consumption rates (FCRs) based on ambient temperature were developed, and adjustment factors were introduced based on ambient temperature and vehicle specific power (VSP) binning. Fuel consumption factors (FCFs) were compared across different temperatures by incorporating VSP distributions and the adjusted FCRs, revealing larger FCFs at extreme temperatures compared to moderate ones. Fuel consumption inventories, both with and without temperature adjustments, were evaluated. The results indicated a 6% underestimation of annual fuel consumption in Beijing when disregarding temperature adjustments. The variation was observed across months (in July and August, underestimations can reach 11%) and across VSP bins (larger impact in low VSP bins). The relationship between FCR and ambient temperature is similar to a quadratic curve, with the lowest consumption occurring at 10 °C–20 °C. The FCF adjustment factor does not vary across speed intervals in cold weather and remains stable at approximately 1.15 at −10 °C, but it drops from 1.25 to 1 as speed increases from 5 to 100 km/h in hot weather. This study underscores the importance of considering ambient temperature in vehicle energy consumption modeling and the necessity of temperature-adjusted approaches for accurate fuel consumption estimations.

Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17 %, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

Alcohol consumption during pregnancy differentially affects the fecal microbiota of dams and offspring

Microbiota imbalances are linked to inflammation and disease, as well as neurodevelopmental conditions where they may contribute to behavioral, physiological, and central nervous system dysfunction. By contrast, the role of the microbiota in Fetal Alcohol Spectrum Disorder (FASD), the group of neurodevelopmental conditions that can occur following prenatal alcohol exposure (PAE), has not received similar attention. Here we utilized a rodent model of alcohol consumption during pregnancy to characterize the impact of alcohol on the microbiota of dam-offspring dyads. Overall, bacterial diversity decreased in alcohol-consuming dams and community composition differed from that of controls in alcohol-consuming dams and their offspring. Bacterial taxa and predicted biochemical pathway composition were also altered with alcohol consumption/exposure; however, there was minimal overlap between the changes in dams and offspring. These findings illuminate the potential importance of the microbiota in the pathophysiology of FASD and support investigation into novel microbiota-based interventions.

Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly in rapidly developing regions with harsh climates. This study examines the seasonal variation of water and electricity consumption in residential buildings in Doha, Qatar, exploring the interconnectedness of land use/land cover (LULC) and socio-demographic characteristics with household water and electricity consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation and Bootstrap analysis) and advanced geostatistical models, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), to analyze and monitor the spatial and seasonal variations of water and electricity consumption. The methods involved assessing the relationship between land surface temperature (LST), household water-electricity consumption, and analyzing the impact of demographic variables. Key findings indicate significant spatiotemporal variations in consumption influenced by changes in LULC and demographic characteristics such as household size and structure. The findings highlight the need for integrated urban planning and energy policies that consider the impacts of LULC and demographic changes to enhance energy efficiency and sustainability in urban settings. Furthermore, the results underscore the importance of addressing the complex interplay between urban development and resource consumption in policy-making.

Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

IDENTIFICATION OF OPERATING MODES OF THE DEFROSTING GARAGE OF THE LOAD STATION OF THE INDUSTRIAL ENTERPRISE IN DYNAMIC CONDITIONS

Purpose. At the current stage of operation of the defrosting garages of the freight station of the industrial enterprise, the defrosting of wagons with bulk raw materials takes place in conditions of a changing environment, which leads to low efficiency of the interaction of railway transport and the unloading complex. To solve this problem, it is necessary to identify the modes of operation of the defrosting garages of the cargo station of the industrial enterprise in dynamic conditions, to analyze the consumption of natural gas for defrosting. Research methods. The following methods were used during the research: synthesis and analysis for the study of the issue and scientific publications regarding the state and ways of identifying the modes of operation of the defrosting garages of the cargo station of the industrial enterprise; experiment planning - to obtain a multidimensional, diverse array of statistical data; theory of probability and mathematical statistics - for data processing, obtaining correlation dependences of indicators of identification of modes of operation of defrosting garages of a cargo station of an industrial enterprise in dynamic conditions. The results. The work is devoted to the solution of the scientific and technical problem of identifying the operating modes of the defrosting garages of the cargo station of the industrial enterprise in the conditions of a changing environment. The work evaluates the modes of operation of the defrosting garages of the cargo station of the industrial enterprise when defrosting bulk raw materials.The influence of external factors on the consumption of natural gas during defrosting of bulk raw materials in wagons was evaluated. On the basis of research conducted in the process of defrosting bulk raw materials in wagons, two temperature modes of operation of garages were identified, which determine different gas consumption. Basic, regulatory mode with a constant defrosting temperature set by technological standards and maximum gas consumption. Periodic, with reduced gas consumption, is characterized by a decrease in temperature after 6-7 hours and subsequent defrosting using accumulated heat, which ensures a decrease in gas consumption. The possibility and expediency of wide practical use of accumulated heat in the process of defrosting bulk raw materials in wagons has been established. The temperature and time parameters of the process of defrosting bulk raw materials in wagons using accumulated heat are proposed, which ensures a significant reduction in energy consumption. Increasing the efficiency of the defrosting garages of the freight station of the industrial enterprise was achieved on the basis of the development of a method and model of natural gas consumption when defrosting bulk raw materials in wagons using accumulated heat. The scientific novelty consists in the identification of the operating modes of the defrosting garages of the cargo station of the industrial enterprise in the conditions of a changing environment, which is based on a multidimensional, diverse array of experimental data. Practical significance.The presented theoretical provisions regarding the modes of operation of the defrosting garages of the freight station of the industrial enterprise made it possible to develop a method and model of natural gas consumption during defrosting of bulk raw materials in wagons using accumulated heat.

Forecasting operation of a chiller plant facility using data-driven models

In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10-second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models.

Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption

Abstract Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.

Energy‐efficient adaptive dependent task scheduling in cooperative vehicle‐infrastructure system

AbstractIn the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

Oral pre- and early postnatal cannabis exposure disinhibits ventral tegmental area dopamine neuron activity but does not influence cocaine preference in offspring in mice.

Cannabis consumption has increased from 1.5% to 2.5% in Canada between 2012 and 2019. Clinical studies have indicated effects of prenatal cannabis exposure on birth weight, substance use, and neurodevelopmental disorders, but are confounded by several difficult to control variables. Animal models allow for examination of the mechanism of cannabis-induced changes in neurodevelopment and behavior, while controlling dose and timing. Several animal models of prenatal cannabis exposure exist which provide varying levels of construct validity, control of dose, and exposure to maternal stress. Using a voluntary oral consumption model, mouse dams received 5 mg/kg Δ9-tetrahydrocannabinol (THC) whole cannabis oil in peanut butter daily from gestational day 1 (GD1) to postnatal day 10 (PD10). At GD1, GD18, PD1, PD10, and PD15, maternal plasma was collected; pup brains were collected from GD18 onward. Pup brains had higher levels of THC and cannabidiol at each time point, each of which persisted in maternal plasma and pup brains past the end of treatment (PD15). Male and female adolescent offspring were examined for changes to ventral tegmental area (VTA) dopamine neuron activity and cocaine-seeking behavior. Prenatal and early postnatal (GD1-PD10) cannabis-exposed male, but not female mice had decreased gamma-aminobutyric acid (GABAergic) input, depolarized resting membrane potential, and increased spontaneous firing of VTA dopamine neurons. Cannabis-exposed offspring showed faster decay of N-methyl-D-aspartate (NMDA) currents in both sexes. However, no differences in cocaine-seeking behavior were noted. These data characterize a voluntary prenatal cannabis exposure model and demonstrates VTA dopamine neuronal activity is disinhibited in offspring.

Заходи з енергозбереження в метрополітені (на прикладі біметалевих контактних рейок)

The purpose of this study is to develop methods and tools for long-term forecasting of electricity consumption based on an improved integrated method for predicting electricity demand, taking into account the peculiarities of electric use of the Kyiv Metro and its impact on the city's electricity consumption. The scientific novelty of the results obtained is the consideration of technological potential with measures for electric saving, pouring into Level II and Level III in the energy consumption model, which takes into account both the overall potential of electric saving (from structural and technological shifts) in the country as a whole, and the features at the regional level (the city of Kyiv) in the types of economic activity in the provision of transport services (Metro). During the work, material was collected on measures for electricity saving in the metro and forecasts for the development of this type of service provision (passenger transportation). To achieve sufficient energy consumption, it is important to introduce energy-efficient technologies that will reduce electricity costs without losing productivity and passenger service quality. Bimetallic contact rails are more efficient in terms of transmitting electric current and reducing energy losses. This innovation opens up new opportunities for the sustainable development of urban transport and contributes to the creation of more efficient and convenient conditions for city residents. Measures are provided to protect the structures and equipment of the section that is being designed and built, the metro line from electric corrosion. Calculation of electricity saving potentials at the regional level (Kyiv) made it possible to more accurately take into account the volume of implementation of energy saving measures, since such programs are developed at the regional level. These estimates are used to determine the technological potential of energy saving. The technological potential of electricity saving will reach the level of 600 million kWh by 2040, while maintaining the pace of economic development in Kyiv. Keywords: Metro, electric saving, electric saving potential, forecasting, energy efficiency, electricity.

Mapping research in the Journal of Innovation & Knowledge to sustainable development goals

This study critically evaluates the contributions of the Journal of Innovation & Knowledge (JIK) toward advancing research aligned with the Sustainable Development Goals (SDGs), employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for systematic literature review. We meticulously examine JIK’s interdisciplinary research patterns, thematic focuses, and topic evolution, emphasizing the journal’s engagement with SDG 9 (Industry, Innovation, and Infrastructure), SDG 8 (Decent Work and Economic Growth), and SDG 4 (Quality Education). By utilizing advanced techniques such as co-citation mapping and BERTopic modeling, this study identifies six major topics within the JIK, aligning with specific SDGs: “EcoTech Revolution” highlights ‘green technology,’ “Entrepreneurial Innovation” focuses on ‘innovation management,’ “Economy 4.0” centers on ‘digital transformation,’ “Effectual Innovation” involves ‘technological innovation capability,’ “Collaborative Economy” addresses ‘sharing economy sustainability,’ and “Green Innovation” explores ‘eco-innovation.’ These topics underscore JIK’s role in advancing interdisciplinary research across key areas such as technology, entrepreneurship, the digital economy, practical innovation applications, collaborative consumption models, and environmental sustainability. Our findings highlight gaps, particularly in addressing SDG 5 (Gender Equality) and SDG 15 (Life on Land), presenting opportunities for JIK to diversify its research agenda toward a more inclusive sustainability discourse. Contributions from a global array of authors underscore JIK’s international impact and its role in fostering diverse sustainability perspectives.

Greek Postwar Plastics Consumption as a Female Cultural Phenomenon

This research is historical in nature and concerns the capitalist expansion of the West in post-war Greece using as ‘Trojan Horse’ the plastic products that flooded the domestic market and were associated with new technologies, typologies and a brand-new way of living. The mass import of synthetics, synonymous with postwar American and European abundance and prosperity, is connected with the almost total overthrow of the strictly traditional social institutions, habits and customs of the Greeks, as well as with the emergence of a new model of cultural consumption based on female purchasing power which was at the same time associated with the female emancipation and independence, especially in the 1960s. The series of subversive developments that sealed, mostly anonymously, the formation of taste, class culture, but also triggered the birth of the domestic plastics industry, unfolds methodically and it is also connected to the political aspect of the ‘western cultural invasion’. Through this research we will be able to identify the introduction of plastic products into the Greek market, society and culture with the aim of prevailing in an aesthetic, practical and symbolic way. This research will also shed light on a totally neglected field of modern Greek cultural and material history connected with the synthetic materials transforming power as regards the female emancipation through consumption.

AN EVALUATION METHOD OF AN ENERGY CONSUMPTION AS AN OPERATION PARAMETER IN A CYBER-PHYSICAL SYSTEM

The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device's lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user.

Energy consumption and surface roughness modelling for multi-objective optimisation of machining processes

Energy consumption and surface roughness modelling for multi-objective optimisation of machining processes