Consumption Model Research Articles

Dynamic energy optimization strategy to provide appropriate thermal comfort and fresh air under occupants’ different activity types

An effective strategy for optimizing indoor air distribution in stratum ventilation heating systems under different types of human activity has been proposed, in order to provide appropriate thermal comfort while exploiting the potential for energy savings. The stratum ventilation was optimized using strategy 1 (the optimization strategy of three ventilation performance indicators based on VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)), the strategy 2 (the optimization strategy with constraint based on VIKOR) and the strategy 3 (the optimization strategy aimed at energy conservation), respectively. These strategies provide optimal indoor air distribution within a stratum ventilation system that is influenced by various factors (i.e. supply air temperature, supply air vane angle, supply air velocity, metabolic rate, clothing insulation and outdoor air temperature). Firstly, the parameters of the stratum ventilation system are obtained using Computational Fluid Dynamics (CFD) software. In addition, using the simulation results as input variables, predictive models of ventilation performance (i.e., PMV, local mean air age (LMAA) and energy consumption) was developed using Artificial Neural Network (ANN). Finally, by optimizing the proposed ANN based strategy 1, strategy 2 and strategy 3, the ventilation performance is output. The proposed strategy 1 provides a comfortable indoor environment and saves energy, and compared to the strategy 1, the strategy 3 saves more energy while ensuring that thermal comfort meets the requirements. In addition, considering the optimization of clothing insulation while providing appropriate thermal comfort also indirectly saves energy. Moreover, these strategies also work well as the outdoor air temperature changes.

Power overwhelming: the one with the oscilloscopes

Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs.Graphical abstract

Evaluation of Physiochemical, Phytochemicals, Antioxidant, Micronutrients Properties of Bangladeshi Crude and Commercial Rice Bran Oil

Rice bran oil was extracted by product of milling of <i>Oryza sativa </i>seeds. The focus of the study was to determine the physiochemical, phytochemicals, micronutrients and antioxidant potentiality of rice bran oil and effect of refining process. The oil is extracted from bran of three popular rice varieties (BRRI-28, BRRI-29, Rajvogh) of Bangladesh. The acid value, peroxide value, iodine value and saponification value of extracted oils were observed in the range of 14.03-16.82 mgKOH/g, 26.68-34.50 meqO<sub>2</sub>/kg, 71.61-84.70 gI<sub>2</sub>/100g and 175.13-180.26 mgKOH/g respectively. Atomic absorption spectrophotometric method was used for determination of micronutrients. The concentrations for copper, zinc, iron and chromium were found to comprise between 0.03-0.25, 0.25-0.39, 17.97-64.47 and 0.96-4.87 ppm respectively. It was observed that BRRI-29 showed the greatest DPPH radical scavenging activity with the IC<sub>50</sub> value of 187.55µg/ml, followed by BRRI-28 (IC<sub>50</sub>- 284.04), Rajvogh (IC<sub>50</sub>- 325.56) and refined oil (IC<sub>50</sub>- 465.12). All the samples acted upon nitric oxide free radical according to following order- BRRI-29 (IC<sub>50</sub>- 286.4)> BRRI-28 (IC<sub>50</sub> -416.81) >Rajvogh (IC<sub>50</sub> -453.95)> refined oil (IC<sub>50</sub>- 557.86).All the extracted and refined oils were found to have considerably good antioxidant activity in a dose dependent manner. The extracted oils were found to contain more phenolic and flavonoid content than refined oil that serve as antioxidants. Some literatures show that, RBO contains tocotrienol and oryzanol which absent in soybean oil which regarded to be the best in perspective of antioxidant potentiality and categorize rice bran oil as promising edible oil. Comparative to crude bran oil commercial refined oil were lower in these parameters which shows the effect of refining process. Further investigation should be conducted on animal model (in vivo study) for safe human consumption. The knowledge would be useful in recommending various ways of using the oil in food industry, as well as in households.

Stochastic optimal control problem of consumption and pension insurance with uncertain lifetime and its application to real-life data

We consider a continuous-time model of optimal consumption and pension insurance for a consumer with an uncertain lifetime. In the model, the consumer earns a stochastic wage income during her working life and optimally allocates her income between personal consumption, pension insurance, and securities with a deterministic dynamic return. Due to the weak development of the stock market in developing countries, employees' income comes mainly from wages and interest on savings from banks, that are discussed in this paper. The consumer's utility and bequest functions are constant absolute risk aversion (CARA). By characterizing the optimality condition of the consumer's problem using the Hamilton-Jacobi-Bellman equation, we find the optimal consumption and pension insurance as a function of wealth in closed form. We consider an application of the model while estimating its key elements using real-life data on age-specific population size, labor income, and interest rates. We show that as the absolute risk aversion for consumption increases, consumption and wealth move in the opposite direction. We also present a novel finding that wealth and consumption can be negatively related across consumers with different levels of consumption risk aversion.

Influence of selected factors on the energy consumption of frozen logs subjected to thermal treatment in veneer production

ABSTRACT This study puts forward an approach for calculating the specific energy consumption and its components required for heating of frozen logs for veneer production, depending on the logs’ initial temperature and moisture content. The approach is based on the use of two personal models: unsteady model of the temperature distribution along the radius of frozen logs during their thermal treatment and stationary model of the energy consumption of logs subjected to such treatment as a function of its four multifactorial components. The influence of all combinations between five values of the initial temperature of frozen logs from −1 oC to −40 oC and three values of their moisture content from 0.4 kg·kg−1 –0.8 kg·kg−1 on the specific energy required to heat and placticize beech logs with a diameter of 0.4 m at operating temperature of the steaming or boiling medium of 80 oC was investigated. The obtained results show that this energy changes in the range from 46 kWh·m−3 (at −1 oC and 0.4 kg·kg−1) to 114 kWh·m−3 (at −40 oC and 0.8 kg·kg−1) The approach could be applied to calculate the energy and its components required for carrying out of the thermal treatment of various frozen capillary-porous materials.

Hygrothermal Performance of Enclosures in a Storage Room and Energy Efficiency in Environmental Control

ABSTRACT This study aims to provide energy-efficient strategies for collections care in the face of financial and environmental challenges. Focusing on paper-based enclosures in storage rooms, the research proposes strategies to enhance energy efficiency by relaxing tight environmental controls. The five-stage process began with a standard test of hygrothermal properties and buffering capacity of the enclosures. In the second stage, a computer model was developed using the first stage results. This model was used in a series of specially designed tests simulating the transient heat, air, and moisture transfer between the room and enclosure to determine an acceptable room environment in which the enclosure conditions could meet collections care standards. The modelling results were used, in the third stage, to develop an AI model that predicted the enclosure conditions using room temperature and relative humidity (RH) inputs. In the final stage, a model for energy consumption of air-conditioning system operations was developed to predict across various control accuracies. Results indicate that the tight maintenance of room RH levels can be relaxed from 47–53% to 33–65%, with a corresponding reduction in energy consumption of approximately 16–18%. This relaxation, while maintaining enclosure conditions in line with conservation standards, was demonstrated through a simulation case at the National Library of Scotland. This study underscores the broader applicability of the developed procedure, asserting its relevance for cultural institutions seeking energy-saving potential through customized enclosure models.

Energy Consumption Modeling for Heterogeneous Internet of Things Wireless Sensor Network Devices: Entire Modes and Operation Cycles Considerations

Energy Consumption Modeling for Heterogeneous Internet of Things Wireless Sensor Network Devices: Entire Modes and Operation Cycles Considerations

Evaluation of the effectiveness of energy sustainability measures through the dynamic energy consumption model

Evaluation of the effectiveness of energy sustainability measures through the dynamic energy consumption model

Performance Analysis on the Safety and Automation System of a Modified Cutting Machine: Repurposing Initiatives

A circular economy is an alternative to the traditional linear economy. The concept reduces waste by recovering resources at the end of a product’s life cycle and repurposing them to reduce environmental impact. The repurposing of machines is a model of production and consumption that involves re-pairing, refurbishing, and recycling to reduce waste. This project aims to improvise and repurpose existing mangosteen peeling machines into another suitable manufacturing process. The designed potato-cutting machine uses the existing conveyor system of a customised mangosteen peeling machine. The cutting mechanism incorporates safety and automation systems using through-beam infrared and capacitive proximity sensors. For the safety system, detecting objects within a range of 2.0 cm to the conveyor region indicates that the machine is unsafe to operate. The system will stop the machine and send audio and visual warnings to notify the users. Based on the trial runs the machine cuts the potato with approximately 1-tenth of the time needed in manual labour. The most significant benefit, however, is the overall savings in production costs, which are more than 85%. This is achieved as the machine re-purposes more than 50% of the components from the existing mangosteen peeling machine, such as sensors, control panel boxes, and PLC system. Consequently, this has helped minimise the overall cost of new equipment while encouraging a circular economy approach to engineering practices.

Consumption patterns in prehistoric Europe are consistent with modern economic behaviour.

Have humans always sold and purchased things? This seemingly trivial question exposes one of the most conspicuous blind spots in our understanding of cultural evolution: the emergence of what we perceive today as 'modern' economic behaviour. Here we test the hypothesis that consumption patterns in prehistoric Europe (around 2300-800 BCE) can be explained by standard economic theory, predicting that everyday expenses are log-normally distributed and correlated to supply, demand and income. On the basis of a large database of metal objects spanning northern and southern Europe (n = 23,711), we identify metal fragments as money, address them as proxies of consumption and observe that, starting around 1500 BCE, their mass values become log-normally distributed. We simulate two alternative scenarios and show that: (1) random behaviour cannot produce the distributions observed in the archaeological data and (2) modern economic behaviour provides the best-fitting model for prehistoric consumption.

APPLICATION OF THE METHOD OF GROUP ARGUMENTS ACCOUNTING FOR BUILDING MODELS OF SHORT-TERM FORECASTING OF GAS CONSUMPTION BY THE POPULATION

Problem statement. One of the most important tasks in optimizing the operation modes of urban gas distribution systems is to develop a short-term strategy for such modes. Processing of planned and statistical indicators of gas consumption modes is one of the key methods for ensuring the optimal functioning and normal operation of gas supply systems. The most promising modeling method for solving forecasting problems is the method of self-organization of models implemented as the method of group arguments accounting (MGАA). Short-term forecasting of gas consumption is the main source of information for decision-making in the process of planning consumption regimes and operational dispatch control. The MGАA method, also known as the Ivakhnenko method, is a powerful tool for obtaining highly accurate forecasting models and is gaining importance for forecasting in energy resource management and planning their use. The solution to such a problem is relevant, as it makes it possible to analyze the volume of natural gas consumption by the population for the future period. The purpose of the article is to build mathematical models for forecasting gas consumption using the method of group arguments accounting based on input data. Conclusion. To select the best models the following parameters are recommended: tomorrow's gas consumption as a function (main criterion) and arguments (additional criterion) - gas consumption to date, air temperature to date, yesterday's temperature and temperatures for the past 3, 5, 7 days, wind speed and day of the week. The resulting mathematical models of natural gas consumption by the population can be used to analyze the volume of natural gas consumption. The method allows us to find a single optimal mathematical model that best solves the problem.

Development of A Sustainable Consumption Model of Corn Plant Through A Circular Economic Approach (Case study of the processing program of corn stalks into alternative fuel to replace coal for CSR SIG Tuban Factory)

In Tuban, farmers have great potential to manage corn cob waste into alternative fuels, but there is still a lack of experience and awareness of waste management. This research aims to develop an educational model that can increase the utilization of waste. The method used is Design-Based Research (DBR) with a qualitative approach, including problem identification, needs analysis, model development, and model testing through workshops. The survey results show that most farmers have not managed corn cob waste properly. The workshop succeeded in increasing farmers' knowledge about waste management and producing alternative fuel products. The educational model developed is effective in increasing farmers' awareness and skills in waste management. In conclusion, this sustainable consumption education model is effective in increasing the use of corn cob waste as an alternative fuel. For long-term success, it is necessary to pay attention to the aspects of technology, flexibility of participation, and freedom of expression in the Generation Z farming community.

Clustering Open Data for Predictive Modeling of Residential Energy Consumption across Variable Scales: A Case Study in Andalusia, Spain

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock.

Energy consumption prediction of coal-fired unit boiler system based on different operating states

ABSTRACT The energy consumption of the coal-fired unit’s boiler system varies significantly when accommodating flexible peak shaving demands. To aid staff in comprehending the boiler’s operational status and optimize its performance, a prediction model of the energy consumption of the boiler system was established based on the different states of the unit operation. First, a dataset of boiler energy consumption under variable load was established based on theory of fuel-specific consumption, and the Mean Impact Value (MIV) algorithm was used to simplify the input features of the model. Second, the Aquila Optimizer (AO) with tent map, adaptive t-distribution, and opposites learning mechanism was introduced to determine the parameters in the prediction model. On this basis, the sliding-window method was used to classify the operating states based on the load of the unit, and the original dataset without operating state distinction, the steady state operating data, the load uplink data, and the load downlink data were used to establish Models 1–4, respectively. The result shows that Model 1 outperforms Model 2 with 24.45% and 18.22% lower aMAE and aRMSE, respectively. compared to Model 3, it shows a decrease of 24.07% and 16.98%. Compared to Model 1, Model 4 shows a reduction of 20.52% and 18.91% in aMAE and aRMSE, respectively. This indicates that distinguishing different operating states to establish boiler energy consumption prediction models can obtain better prediction accuracy.

Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization

The multifaceted challenge of carbon dioxide (CO2) emissions during building operations, construction, and material production perpetuates energy shortages and climate adversities. Urgent imperatives underscore the need for effective energy reduction and carbon mitigation, driving the building sector toward a paradigm shift in clean, low-carbon evolution. However, the complex multidimensional and nonlinear characteristics of influencing factors of building energy consumption make it difficult in precisely understanding the spatial and temporal relationships between the features in the predictive modeling of buildings. Therefore, a novel Graph Wavenet (GWN) based on the spatio-temporal attention fusion mechanism (STA-GWN) is proposed for spatio-temporal graph prediction modeling of structures, which can capture hidden spatial relationships using an adaptive dependency matrix. To overcome the drawbacks of standard predefined adjacency matrices and their inability to capture faraway dependencies, a spatio-temporal attention fusion mechanism is introduced to extract latent spatio-temporal features dynamically. By combining extracted features, building energy consumption can be predicted more precisely. Moreover, the proposed model becomes more interpretable, making it easier to reveal how spatio-temporal features relate to building energy consumption. Lastly, the proposed method is applied to energy conservation and emission reduction in the building industry. A predictive model for electrical energy consumption during two public datasets with an operational phase of buildings is established to analyze and optimize energy utilization in residential structures that rely on electricity. The experimental results demonstrate that the STA-GWN model surpasses other deep learning methods in terms of prediction accuracy and robustness. Moreover, the proposed model can offer optimal solutions for building energy processes, reducing approximately 201.44 kg (Kg) and 64.08 Kg of CO2 emissions in the appliances energy prediction dataset and the individual-household-electric-power-consumption (IHEPC) dataset, respectively.

Research on time-energy optimal trajectory planning of articulated heavy-duty robot

Articulated heavy-duty robots are more and more widely used in the construction environment. Aiming at the problems of low working efficiency and high energy consumption of this kind of robot, a multi-objective adaptive trajectory planning method combining path optimization and trajectory optimization is proposed to improve the working efficiency of the robot and reduce energy consumption. Firstly, based on kinematics and dynamic analysis considering friction, the energy consumption model of robot trajectory is constructed. Then, according to the posture of the key points in the known workspace, the optimal path points in the joint space are obtained by a path point solution method considering the joint load characteristics. On this basis, a multi-objective model considering running time and energy consumption is established to plan the interpolation trajectory of the joint space of the quintic B-spline curve. Finally, constrained by the velocity, acceleration and torque of the joint of the manipulator, the optimal solution is obtained by using the elite non-dominated sorting genetic algorithm (NSGA-II), and the optimal weight factor is selected by a multi-objective adaptive optimization method to obtain the optimal position-time series. The experimental results show that compared with the quintic polynomial trajectory optimization method, the energy consumption of the proposed method is reduced by 10.90% under the same working efficiency. The research results of this paper provide a new optimization idea for other target trajectory planning.

Optimization for total energy consumption of drone inspection based on distance-constrained capacitated vehicle routing problem: A study in wind farm

Facing large-scale, intensive offshore wind farms in wide and deep sea areas, total inspection energy consumption (TIEC) optimization is a key issue to be addressed in unmanned aerial vehicle (UAV) inspection. This paper focuses on the TIEC optimization of UAV automatic inspection of offshore wind farms based on the assistance of mobile edge computing (MEC). To tackle this issue, a system model using a UAV to inspect offshore wind farms automatically and an energy consumption model of UAV assisted by MEC servers and near-earth orbiting satellites for inspecting wind turbines are proposed. Based on this, a combinatorial optimization problem based on the distance-constrained capacitated vehicle routing problem (DCVRP) for minimizing the TIEC of UAV inspection was constructed. To solve the optimization problem, an improved hybrid heuristic algorithm is proposed based on the K-means clustering, smallest enclosing circle (SEC), and Lin-Kernighan-Helsgaun (LKH-3) algorithms. The algorithm solves the optimum location and number of automated UAV airports and minimizes the TIEC for UAV inspection. The simulation experimental results show that in an offshore wind farm with 86 wind turbines, compared with other existing schemes, this scheme requires only four automated UAV airfields and can save at most 51.8% of the TIEC.

Energy Consumption Outlier Detection with AI Models in Modern Cities: A Case Study from North-Eastern Mexico

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context.

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