Affect Energy Consumption Research Articles

Forecasting electricity consumption using the Principal Component Analysis method

Relevance. The need for accurate forecasting of electricity consumption to improve efficiency and reduce costs at industrial enterprises, which leads to increased competitiveness of goods manufactured by the enterprise. Traditional forecasting methods often do not take into account complex interactions between various factors affecting energy consumption and do not provide the necessary forecast accuracy. The principal component analysis method offers a promising prospect – reducing the volume of processed data (dimensionality) without significant loss of information, which simplifies forecast models while maintaining their accuracy. Aim. To develop an accurate and efficient model for forecasting electricity consumption at industrial enterprises using the principal component analysis method. This model is aimed at eliminating the limitations of traditional forecasting approaches by reducing data dimensionality and increasing the accuracy of predictions, which ultimately improves the efficiency of electricity consumption and reduces financial costs, including those due to forecasting errors. Methods. The principal component analysis method, which allows us to reduce the volume of processed data (dimensionality) by transforming a large set of correlated variables into a smaller set of uncorrelated principal components. The study included the following stages: data import and factor analysis, correlation matrix construction, analysis of selected and accumulated variances for each factor, factor loading matrix construction, dimension reduction, development of a mathematical model using linear regression, and forecast installation and validation. Results. The application of the principal component analysis method allowed us to create a model for forecasting electricity consumption. Its application showed that the first principal component explains 69.65% of the total variance, the second component – ​​17.28%, i. e. together they explain almost 87% of the variance. The developed model provides good agreement between the actual and forecast values ​​of electricity consumption in several time intervals with an average error level within the range of +3 to –5%. This indicates the suitability of the model for forecasting electricity consumption, although some discrepancies indicate the need for its further improvement.

Highway On-Ramp Truck Platooning Based on Deep Reinforcement Learning

The advancement of connected and automated trucks (CATs) offers a novel avenue for the freight industry to optimize fuel efficiency, enhance traffic flow, and bolster safety through platooning. Particularly at highway on-ramps, how to effectively form CAT platoons is a key research topic. Previous studies have not addressed the issue of platooning at on-ramps. Within mixed traffic conditions, the interference from human driven vehicle (HDV) notably complicates the platooning operation. In this process, the position, timing, and speed of CAT merging significantly affect energy consumption and traffic safety. Thus, this work introduces a hierarchical merging strategy with a dynamic mechanism for selecting optimal merging points. Its objective is to facilitate efficient autonomous CAT platooning at highway on-ramps while considering the interference of HDVs. Specifically, it uses a model-free deep reinforcement learning method that guides the platooning process by exploring optimal driving behaviors. It ensures the safety and efficiency of the CAT merging process. Furthermore, we incorporate a realistic vehicle dynamics model within our simulations. The proposed strategy can handle the variation of the CATs’ initial positions and speeds at on-ramps, as well as interference caused by HDVs at highway mainline. The efficacy of the proposed strategy has been verified through a series of simulation experiments. The findings indicate that our strategy is capable of effectively orchestrating CAT platooning maneuvers at the highway on-ramps.

Does Education Spending Affect Energy Consumption in Saudi Arabia? A Bootstrap Causality Test

This investigation explores the causal nexus between educational expenditure and energy utilization in Saudi Arabia spanning 1981-2023, thereby addressing a significant deficiency in the academic discourse concerning the direct influence of educational investments on energy consumption patterns. Through a bootstrap causality examination framed by an Autoregressive Distributed Lag (ARDL) approach, this investigation delves into yearly time series data to highlight educational investment’s immediate and delayed effects on energy consumption. The results indicate that educational spending exerts a lagged yet positive influence on energy consumption, which materializes over time as the populace attains greater technological literacy and industrial operations proliferate. This lagged effect emphasizes the critical nature of long-term educational investments in fostering sustainable energy consumption behaviors, which aligns with Saudi Arabia’s Vision 2030 objectives regarding economic diversification and ecological sustainability. Also, this investigation validates the premise that economic development and oil price volatility have substantial positive repercussions for energy consumption, emphasizing the energy-intensive traits of the Saudi economic landscape and its reliance on oil-related earnings. Additionally, population growth contributes to the escalation of energy consumption, while the ramifications of urbanization remain statistically negligible in the short run. These findings hold substantial implications for policymakers, suggesting that persistent investment in educational frameworks is imperative for attaining enduring energy efficiency and sustainability objectives. The research advocates that enhancing energy literacy and promoting technological advancement through education can facilitate the transition toward a sustainable energy paradigm.

Redefining realistic and stochastic occupancy schedules and patterns for residential buildings in Jordan

Occupant behavior significantly influences building energy consumption, underscoring the importance of understanding the interplay between energy usage and occupant behavior. This integration is vital for evaluating how occupants and their interactions impact building energy consumption patterns. Despite widespread recognition of the importance of behavior-centric approaches in achieving sustainable architecture, there remains a gap in understanding human behavior, particularly in performance-based design. This study presents realistic and stochastic occupancy schedules for residential buildings in Jordan. This was achieved using a mixed-method approach involving a survey, data classification, clustering processes, and a co-simulation approach combining Rhino Grasshopper and Python. The results contributed to refining the general characteristics of each cluster within different age groups, encompassing various patterns of interaction, tolerance levels or levels of sensing, energy conservation consciousness, and occupancy patterns. A significant correlation was also observed between age groups, occupancy schedules, and energy consumption behaviors. The simulation also evaluated the effects of stochastic behavior patterns on the prediction of the energy performance of residential buildings. The results confirm the significant variation in energy use intensity levels between realistic occupancy schedules and fixed schedules for residential buildings. The results also prove that using realistic occupancy schedules for each age group significantly affects energy consumption behavior through occupant-based behaviors. This confirms the pivotal role of dynamic schedules and occupant behaviors in understanding and shaping energy consumption patterns and realistically determining the efficiency of building systems.

A Digital-based Model Proposal for Optimum Building Orientation in Architecture in Ecological Context Nur Sumeyye

In order to make optimum use of the heat energy coming from the sun and the cooling effect of the wind, it is very important that the buildings are positioned on the land in positions that can provide the highest level of user comfort with minimum energy consumption. Determining the layout of the building is a critical design parameter that affects energy consumption. However, when there are vistas such as sea, forest, etc. that are desired to be seen from the first degree interior volumes around the building, this factor becomes decisive in the building orientations. As a result of this, climatic comfort factors such as natural ventilation provided by wind and heat energy from the sun are ignored. While the orientation of the interior spaces towards the landscape is preferred by the users, the climate element and the comfort it provides should be the priority in the architectural design. In this study, an orientation optimization model has been proposed for building orientation based on utilizing vista and climatic data in the context of passive design. With the parametric and numerical-based approach developed in the study, the way to use the vista factor, which has a multivariate and complex structure, as an “input data” has been investigated. In order to test and verify the developed optimization model, “The Canal Istanbul” artificial waterway project area, which is a very up-to-date subject, was chosen as the plot research area. The Canal feasibility studies have been prepared by the authorities. The data obtained from these reports were used quantitatively in the model. In this context, the technical and ecological data of the canal route were investigated from the reports prepared for the Canal Istanbul route. Thus, as a result of the data accessed from various sources, the climate type and ecological characteristics of the region have been moved to a quantitative dimension as numerical data. An optimization model has been developed for optimum building orientation in terms of regional climate, taking into account the zoning decisions and basic planning criteria determined in the focus of these data. The developed model primarily places the building in the desired point position over the 1000 and 5000 zoning plans of the region, with the vista as priority. Afterwards, the model perceives the vista spline that enters the field of view of the building. Then, staying within the boundaries of this spline, it turns the structure in alternative directions and analyzes it according to each direction. At this stage, the model presents a mathematical dataset by comparing the alternative orientations of the building with the “optimal direction”, “prevailing wind direction” and “north direction” angles. Finally, these data are optimized within the model and suggestions are made to the designer to make a choice, based on the results obtained.

Occupancy-based Energy Management System in Campus Building: A Case Study in Electrical Engineering Department Building, ITN Malang

Building energy management systems provide an effective method for energy saving. An occupancy-based energy management system considers the occupants' behavior that affects energy consumption. This paper presents the investigation of different scenarios based on the occupancy to control the energy consumption in the Electrical Engineering Department Building, ITN Malang. The results show that switching off the appliances when the occupants go home in the afternoon reduces the monthly energy consumption by 33.95%. The result indicates that the occupants' awareness regarding energy saving has recently been low. Thus, the occupancy sensor and automatic time scheduling for the appliance control are suggested to be installed.

Empirical insights into India's energy demand: unveiling elasticity dynamics for strategic policy initiatives.

Policymakers and stakeholders can use insights from price elasticity and income elasticities of energy demand to devise targeted interventions that promote energy efficiency, reduce reliance on fossil fuels, and drive the transition to more sustainable and resilient energy systems. This study seeks to examine how changes in income elasticity and price elasticity affect energy consumption over time using the ARDL model and a time-varying parameter technique, state-space Kalman filter. The study's findings reveal that energy prices have a negative and significant impact on energy consumption, whereas GDP per capita and population growth have apositive and significant impact on energy consumption in both the short and long run. The findings underline the importance of economic growth and increasing population in driving growing energy demand in India, which is consistent with the country's energy-intensive development aspirations. The time-varying analysis shows that the price elasticity of energy consumption follows a U-shaped pattern across time. However, from the onset of the new millennium decade, the elasticity of energy consumption with regard to its price has continually decreased, reaching - 0.54 by 2020. Furthermore, the findings imply that the income elasticity of India's energy demand has stabilized around unity, indicating that the country is currently near the middle of the energy intensity-GDP inverted-U trajectory. Given these evolving elasticities, policymakers should take a flexible and forward-thinking strategy to achieve optimal energy consumption levels.

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

Proximal Policy Optimization with Population-based Variable Neighborhood Search Algorithm for Coordinating Photo-Etching and Acid-Etching Processes in Sustainable Storage Chip Manufacturing

In the complex process of manufacturing storage chips, the photo-etching and acid-etching stages play a crucial role, significantly affecting energy consumption and environmental impact. This paper introduces a novel Bi-Level Programming Model for Storage Chip Manufacturing (BLPM-SCM) aimed at optimizing the coordination between these two stages. The upper-level model focuses on minimizing the time it takes to complete wafer production, while the lower-level model seeks to reduce the number of acid-etching tanks used, thereby balancing production efficiency with resource utilization. To address the inherent complexity of the bi-level model, we present a hybrid meta-heuristic algorithm that combines Proximal Policy Optimization (PPO) with a Population-based Variable Neighborhood Search (PVNS) method. The PPO-PVNS algorithm enhances the intensification phase by adaptively selecting shaking and local search strategies, while PVNS supports the diversification phase, ensuring comprehensive exploration of the search space through iterative updates of the solution population. Extensive numerical experiments demonstrate the algorithm's superior performance and generalization capabilities in optimizing the manufacturing process. It significantly improves the coordination between the photo-etching and acid-etching stages, achieving dual optimization of energy consumption and environmental benefits. Furthermore, this study provides valuable insights and decision-making tools for industry practitioners, offering innovative solutions for scheduling optimization in the semiconductor sector and promoting more sustainable and efficient production practices.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Thermal Bending Simulation and Experimental Study of 3D Ultra-Thin Glass Components for Smartwatches.

The heating system is an essential component of the glass molding process. It is responsible for heating the glass to an appropriate temperature, allowing it to soften and be easily molded. However, the energy consumption of the heating system becomes particularly significant in large-scale production. This study utilized G-11 glass for the simulation analysis and developed a finite element model for the thermal conduction of a 3D ultra-thin glass molding system, as well as a thermal bending model for smartwatches. Using finite element software, the heat transfer between the mold and the glass was modeled, and the temperature distribution and thermal stress under various processing conditions were predicted. The findings of the simulation, when subjected to a numerical analysis, showed that heating rate techniques significantly affect energy consumption. This study devised a total of four heating strategies. Upon comparison, optimizing with heating strategy 4, which applies an initial heating rate of 35 mJ/(mm2·s) during the initial phase (0 to 60 s) and subsequently escalates to 45 mJ/(mm2·s) during the second phase (60 to 160 s), resulted in a reduction of 4.396% in the system's thermal output and a notable decrease of 7.875% in the heating duration, respectively. Furthermore, a single-factor research method was employed to study the forming process parameters. By comparing the numerical simulation results, it was found that within the temperature range of 615-625 °C, a molding pressure of 25-35 MPa, a heating rate of 1.5-2.5 °C/s, a cooling rate of 0.5-1 °C/s, and a pulse pressure of 45-55 Hz, the influence on residual stress and shape deviation in the glass was minimal. The relative error range was within the 20% acceptable limit, according to the experimental validation, which offered crucial direction and ideas for process development.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Анализ влияния климатических факторов на потребление электроэнергии предприятиями в неперевозочных видах деятельности ОАО «РЖД»

Purpose: analysis of the costs of energy consumption by enterprises of JSC Russian Railways by type of fuel, identification of the most costly type of energy. Systematization of factors influencing energy consumption. Characteristics of climatic factors affecting energy consumption. The task was set to assess the possibility of using ASKUE data to improve the system for analyzing and planning energy consumption in non-transportation activities of JSC Russian Railways. Analysis of climatic factors affecting electricity consumption by enterprises in non-transportation activities of JSC Russian Railways. Determine the maximum time interval for analyzing energy consumption data. Methods: collecting information on electricity consumption and changes in climate characteristics. The main research method is the method of correlation analysis, which allows us to identify the most important factors influencing electricity consumption. Based on the correlation coefficient, a correlation matrix will be compiled using the Statistica software package. Results: climatic factors have been identified that have the greatest impact on the energy consumption of the enterprise in non-transportation activities of JSC Russian Railways. The maximum enlarged interval for analyzing energy consumption data has been clarified. Practical significance: climatic factors have been proposed, the characteristics of which must be relied upon when planning electricity consumption at non-transportation enterprises of JSC Russian Railways. These factors can be used in the further development of a model for planning energy consumption. The maximum enlarged interval for analyzing influencing factors has also been identified, allowing to obtain accurate results. The possibility of using hourly ASCAE data to assess and identify climatic factors affecting electricity consumption by non-transportation enterprises has been proven.

Assessing carbon capture and storage technology in industrial joint venture

Carbon Capture and Storage (CCS) technology can effectively reduce carbon dioxide emissions from industrial and energy production processes. Yet the commercialization of CCS technology is hampered by financial requirements. Existing research compares the costs and benefits of business models for individual CCS projects, no studies assess the industry and national levels. Here we use a dynamic computable general equilibrium model, constructure the vertical integration and joint venture business models for CCS. This study assesses the emission and economic impacts of joint ventures between China's high-emission industries and their upstream and downstream sectors. We find that adopting the joint venture model in the coal power sector, compared to the vertical integration model, initially exerts a negative economic impact but can mitigate 0.04 % of GDP by 2060. The chemical industry consistently benefits from the joint venture model both economically and in emission reductions. Downstream sectors of the steel and cement industries are unsuitable for participation in joint ventures. In addition, the joint venture model has significant industry linkage effects, affecting energy consumption, the scale and cost of CCS deployment. This work provides important information for the large-scale commercialization of CCS technology.

Challenges and Solutions for Sustainable ICT: The Role of File Storage

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations’ data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in “digital sustainability” and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

Based on the improved fuzzy analytic hierarchy and the TSE-MLR model energy consumption prediction of university: A case study

The accurate prediction of building energy consumption on university campuses is a significant research area. Current studies often focus on predicting the energy consumption of specific building areas or individual equipment, and typically consider only one factor, limiting the accuracy and applicability of the predictions. This study introduces the Time Segmented Energy-Multiple Linear Regression (TSE-MLR) prediction model, which integrates the improved fuzzy analytic hierarchy and the multiple linear regression algorithm. The model is compared with traditional (MLR, BP) and advanced (RNN) models, and their various indexes are discussed and analyzed. By collecting meteorological and energy consumption data from the study site over the past 12 years, the key factors affecting energy consumption on the university campus were identified using the improved fuzzy analytic hierarchy. Subsequently, the TSE-MLR model was trained using energy consumption data from 2010 to 2016 and validated using data from 2017 to 2019. The prediction results of the TSE-MLR model were compared with those obtained through Multiple linear regression, BP neural networks, and RNN. The results demonstrated that the TSE-MLR model significantly reduced the prediction error by 13.8 % and exhibited higher accuracy compared to the other models. Therefore, the TSE-MLR model introduced in this study offers a new and effective approach to predicting university energy consumption and supporting energy management using existing data from university building operations across different periods.

Energy consumption decomposition analysis using European official statistics: Methodology and input data

Deciphering energy efficiency is a critical component for the sustainability and energy policies of the European Union (EU) and its Member States. Decomposition analysis is a key method that helps distinguish real energy efficiency gains from other external factors. This article presents a decomposition analysis method using official EU statistics, its results, and associated limitations. Although separating structural changes and activity levels from energy efficiency poses a challenge, an adapted Logarithmic Mean Divisia Index (LMDI) method was utilised to isolate and highlight energy efficiency. This article outlines this method and how the European official statistics were exploited. Firstly, it examines the factors affecting energy consumption in various sectors within EU-27. Secondly, it examines the factors affecting energy consumption in various sectors. In doing so, it also addresses potential obstacles in data collection, and presents improvements to the LMDI analysis. The findings in this study make a substantial contribution to the fields of national statistics, methodological applications, and energy data analysis in the context of the EU’s energy policies.

A designerly approach to a novel method for combining closed and semi-open spaces in a mid-rise office building envelope to improve energy efficiency and thermal comfort

This article introduces a new method of combining closed and semi-open spaces in a mid-rise office building. This method presented in the paper aims to improve energy efficiency and thermal comfort and emphasize the use of open spaces on all floors and in different directions. Usually, open spaces in buildings such as courtyards, balconies, atriums, and sky_court, despite their positive performance, have limitations in terms of geometry, location, enclosure, and connection with the building. However, this approach has tried to solve these limitations using a modular network in the plan production process. This approach allows the creation of open spaces with various geometries and areas on the floors. This study pays attention to the presence of semi-open space in mid-rise buildings and the creation of porosity in the volume, which reduces energy consumption and increases the comfort of employees. This article introduces an innovative approach to utilizing semi-open space on the floors of office buildings. This approach, by allocating a part of the area of each floor to semi-open space with two different parameters, i.e., “semi-open space with the most connection with the façade” and “semi-open space with the least connection with the facade,” is a framework for the design and optimal placement of the semi-open space in It offers office buildings. This research tries to generate all possible situations to achieve 40 % of open space in the building. By dividing the plan into 5 × 5 modules in the presented simulation model, 320 modules are available, and to convert 40 % of the building into semi-open space, 128 modules should be allocated for this purpose. The models simulated by Ladybug and Honeybee plugins show that in the volumes where the semi-open spaces in the building have a combination of the maximum connection with the facade and minimum connection with the facade, they perform better in energy consumption and thermal comfort. The research results show that a volume comprising closed and semi-open spaces with a suitable porosity ratio can perform best. In other words, the change in volume porosity significantly affects energy consumption and thermal comfort.

Collaborative optimization of multi-physical fields for composite electromagnetic linear actuators

The composite electromagnetic linear actuator features a co-axial integrated design that merges the moving coil and moving iron actuator types. This design leads to significant interactions among electrical, magnetic, and thermal fields during operation, which in turn affect energy consumption and temperature increases. To further improve the material utilization rate of the actuator and optimize its comprehensive performance, such as force density and temperature rise, a multi-physical field collaborative optimization method was proposed. First, the energy consumption and temperature rise mechanisms of the actuator were analyzed. The bidirectional coupling relationships of multi-physical fields during its operation are studied. Simulations and experiments were conducted to establish the rules governing the spatiotemporal evolution of energy consumption and temperature fluctuations under standard operational conditions. Based on this, a system-level collaborative optimization model for the actuator was established. Sensitivity analysis helped segregate the design variables into system-level and independent categories. Subsequently, using a multi-objective genetic algorithm, the optimal parameter settings were determined. The optimization results revealed that the force constant of the main driving components increased by 5.4%, and the average temperature of the coil decreased by 6.3%; additionally, the starting force of auxiliary components increased by 2.3%, and the temperature of the yoke decreased by 0.8%.