Analysis Of Energy Consumption Research Articles

Energy Consumption Analysis and Efficiency Enhancement in Manufacturing Companies Using Decision Support Method for Dynamic Production Planning (DSM DPP) for Solar PV Integration

Energy Consumption Analysis and Efficiency Enhancement in Manufacturing Companies Using Decision Support Method for Dynamic Production Planning (DSM DPP) for Solar PV Integration

Sustainable Urban Energy Solutions: Investigating the Solar Potential of Vertical Façades Amidst Adjacent Buildings in Kuala Lumpur, Malaysia

This article explores the impact of adjacent buildings on solar exposure for photovoltaic (PV) applications in high-rise urban areas in Malaysia. The rapid urbanization and population density in Malaysia's high-rise areas have increased energy demand, necessitating the exploration of alternative energy sources. Integrating PV systems into vertical façades offers a promising solution for maximizing energy generation in space-constrained urban environments. However, the efficacy of PV systems is influenced by adjacent buildings, including their arrangement, height, and orientation, which affect solar exposure and energy generation potential. Understanding these effects is crucial for optimizing PV system design and placement. This study aims to investigate the relationship between adjacent buildings and solar exposure for PV panels mounted on high-rise buildings in Malaysia. By examining these effects, the research provides valuable insights for urban planners, architects, and renewable energy practitioners involved in sustainable urban development. The findings will inform decisions regarding the integration of PV systems, promoting renewable energy adoption, and reducing the environmental impact of urban areas. The article also discusses Malaysia's climate, energy consumption, and solar radiation potential, emphasizing the need to harness solar energy in a country blessed with abundant sunlight. The challenges faced by current air conditioning systems and building-integrated photovoltaic (BIPV) applications in Malaysia are outlined, along with efforts to address them through energy-efficient systems and awareness campaigns. The research conducted simulations using a 3D model in Kuala Lumpur, Malaysia, considering different heights and distances of adjacent buildings. The software used for simulations was IES-VE, which offers various modules for energy consumption analysis. The results demonstrate how adjacent buildings affect the annual average energy for different scenarios, highlighting the importance of distance and height. The findings provide insights into the monthly average energy variations and emphasize the relationship between adjacent building height and average energy levels. Overall, this research contributes to the efficient utilization of vertical façades for harnessing solar energy in high-rise urban areas, supporting Malaysia's sustainable energy goals while ensuring urban environment liveability and resilience.

An analysis of renewable energy consumption in Visegrád countries

The ‘Visegrád Group’ countries are fundamentally distinct from one another. There is less public backing for green initiatives like climate protection and renewable energy in Western Europe. Since the change began, the region has made substantial efficiency gains. Descriptive statistics and Principal Component Analysis (PCA) were employed to quantify the various energy usage patterns across the V4 countries. Our analysis revealed that in Slovakia, Poland, and the Czech Republic, renewable energy development had accelerated by 149%, 116%, and 87% relative changes, respectively. Hungary has stalled or declined. These results suggest that the V4 countries have made considerable progress in transitioning to renewable energy sources. However, further steps are needed to ensure Hungary can make the same progress as its neighbours. According to the findings of our studies and forecasts, substantial levels of consumption of renewable energy will be observed in the following industries in the year 2022: Electricity usage will rise by 29.78% in Hungary and 22.45% in Slovakia; in the heating and cooling sector, it will rise by 24.60% in Czechia and 16.71% in Poland; and in the transportation sector, it will grow by 8.44% in Hungary and 7.71% in Slovakia. To keep up with expected increases in energy consumption, the V4 countries will need to expand the proportion of their economies that use sustainable and alternative resources. Furthermore, we propose six strategies to tackle increased energy demand and major obstacles to transitioning towards renewable energies. This will ensure energy independence and pave the way to carbon neutrality by 2050. While the EU has high targets for renewable energy, the papers show that the V4 nations each have their own set of ambitious goals.

Comparative Analysis of Energy Consumption (kcal) among Public and Private Secondary School Students in Lagos State, Nigeria

Objective: To describe pattern of dietary energy intake among adolescents from 2021-2022.

Data generation and verification of pipeline transportation based on integration of simulation and DBSCAN algorithm

Through the analysis and mining of historical data, machine learning method can be used to obtain high accuracy prediction effect without establishing a complex physical model in equipment fault diagnosis, operation condition prediction, and pipeline energy consumption analysis. In the oil and gas pipeline system, the machine learning model unable to gain an ideal training effect with the data set, because of confidentiality of data, imperfect data acquisition technology, low frequency of abnormal working conditions, and other factors. In this paper, aiming at the operation energy consumption of a crude oil pipeline, the power consumption of oil pump unit is simulated by software, which can expand the data. The quality of simulation samples has a great effect on the training results. A DBSCAN algorithm based on Mahalanobis distance is proposed to evaluate the reliability of simulation samples and identify abnormal simulation samples, given the characteristics of virtual samples in pipeline transmission simulation, such as no real value control, feature correlation, and high dimension. Examples have shown that the fitting ability of the model can be improved after the simulation samples for eliminating abnormal data are added to the training set, which provides a new method for the generation and verification of simulation samples.

Research on energy consumption analysis of air conditioning and refrigeration equipment based on perceptron model

Abstract This paper mainly studies the energy consumption of air conditioning and refrigeration system from the perspective of the dynamic operation of air conditioning systems and selects the energy consumption of a chiller system, cooling tower system, chilled water pump system and fan coil system, which affect the energy consumption of air conditioning system, as model variables. An integrated learning method based on a multilayer perceptron is proposed to integrate spatial features with temporal features, and a spatio-temporal prediction model for air conditioning energy consumption analysis is established under the Seq2Seq framework, and then the input curves of the four variables with load change under the optimal operation of the air conditioning system under dynamic load are derived. The results show that the deviation between the simulated value and the measured, calculated value of the energy consumption analysis model of air conditioning and refrigeration equipment based on the multilayer perceptron is within 5%, which can effectively and accurately predict and reduce air conditioning energy consumption by 15% to 20%. The maximum energy-saving efficiency can reach 30% when the air conditioning system is in optimal operation under dynamic load, and the energy-saving effect of the chiller, chilled water pump and cooling water pump is relatively significant. The research results of this paper have a certain reference value for reducing the energy consumption of air conditioning systems in practice.

Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model

Autonomous Forklifts (AFs) play a vital role in smart factories, particularly in the transportation of heavy loads. However, their energy consumption poses a significant challenge as they need to operate for extended periods on a single battery charge. Therefore, energy-efficient motion is necessary to raise their availability. The AF’s movement is dynamically determined by a motion planning algorithm within its navigation system. In light of this, this article introduces a strategy to improve energy efficiency during the motion planning phase. This strategy involves a cooperative approach, utilizing the Deep Neural Networks (DNNs) and AF’s kinetic model to achieve this energy-saving goal. Unlike traditional methods that rely solely on the vehicle’s kinematic model, our approach considers an additional factor, incorporating the influence of the vehicle’s kinetic model for a more comprehensive and accurate energy consumption analysis. First, the kinetic model of an AF is developed by considering the effect of the front-powered wheel. Second, the kinetic model is employed within a time-energy optimization technique, aiming to find the AF’s ideal acceleration. This optimization process generates a dataset that covers a range of AF maneuvers and dynamic parameters. Third, a DNNs model is trained using this dataset to predict the optimal acceleration for the AF. Finally, the trained model is integrated into a motion planning algorithm to determine the optimal and acceptable limits for both linear and angular acceleration. Experiments illustrate that the suggested motion planning method can generate trajectories that are both feasible and optimized for energy consumption. This differs significantly from the typical algorithms which generally results in higher energy use by the AF, occasionally leading to the generation of infeasible trajectories.

Simulation calculation and analysis of building energy consumption of multi-story glued laminated timber structures

As the only renewable building material, wood has a strong carbon sequestration effect. Thus, timber structures have the natural advantages of saving energy and reducing emissions. Currently, the research objects of building energy consumption are rarely timber structures. To further control the operational energy consumption of timber structures, this paper takes six-story glued laminated timber beam-column frame and light wood-frame shear wall structures as the research objects. Building energy consumption research is conducted through testing the heat transfer coefficient of the envelope structure, air circulation ratio, and Building Information Modeling (BIM) technology on-site. The results show that the energy consumption of the building is consistent with the current energy consumption of small- and medium-sized office buildings, and the heat gain and loss of the building are mainly due to solar radiation and heat conduction of the envelope, respectively. The airtightness of the building has the greatest influence on the energy consumption of the building, and the type of building structure and window-to-wall ratio have little influence on the energy consumption of the building.

Operation Mode and Energy Consumption Analysis of a New Energy Tower and Ground Source-Coupled Heat Pump System

In order to solve the problems of performance degradation in energy tower heat pump (ETHP) systems under low temperature conditions and soil heat imbalances in ground source heat pump (GSHP) systems in cold regions, a new coupled system of ETHP and GSHP systems (the ET–GSHP system) and its operating mode were proposed. The mathematical model of the system was constructed along with the system’s form and operation scheme. The COP (coefficient of performance) and total energy consumption of the coupled system were then simulated and studied under a number of common operating situations. The heating season is divided into four periods based on varying outdoor ambient temperatures: the first period operates in series mode and has an average outdoor temperature of 2.38 °C; the second period operates in parallel mode and has an average outdoor temperature of −8.56 °C; the third period uses soil source heat pumps to operate separately; and the fourth period operates in series mode and has an average outdoor temperature of −11.32 °C. Operation of the coupled system in four periods was simulated and analyzed, and the operational efficiency and energy saving of the system were analyzed using an actual commercial building in a cold region as an example. The results demonstrate that the ET–GSHP system’s overall energy consumption during the heating period is reduced by 4.34% when compared to the traditional GSHP systems; the system’s COP can maintain a high level throughout the heating period, with an average COP of 3.315; and the soil temperature at the conclusion of the heating period is 25 °C, which is 8.89 °C higher than that of the traditional GSHP system, providing a guarantee of summer heat return. The new ET–GSHP system significantly boosts the efficiency of the system’s operation, achieves effective coupling between various heat sources through multi-stage control, and offers improved energy-saving advantages.

Quantitative and Comparative Analysis of Energy Consumption in Urban Logistics Using Unmanned Aerial Vehicles and Selected Means of Transport

Cities are moving towards sustainable development, which consists of tasks and challenges to improve the quality of life, and minimize energy consumption. The concept of sustainable mobility includes the choice of means of transport other than the car for all journeys, especially short distances, and for the delivery of goods. Due to the growing populations of cities, lack of free space, and high costs of building infrastructure for traditional means of transport, cities are looking for modern solutions that allow for the cheap, fast, and green transportation of people and goods. Urban air mobility is the answer to these problems, and uses eVTOL (electric vertical take-off and landing) aircraft and unmanned aerial vehicle systems (UAVs). The article’s main purpose is to present an energy efficiency analysis using UAVs and electric scooters in the transport of takeaway food, which is a solution that fits into the zero-emission transport policy. The article presents the following research problem: which type of electric transport (scooters/UAVs) shows a lower demand for electric energy when delivering food from restaurants to individual customers? The analysis method was applied using the D’Andrea, Dorling, Figliozzi, Kirchstein, and Tseng energy models. The completed calculations were used to perform a comparative analysis of energy consumption for three adopted scenarios related to energy consumption by drones.

Green hydrogen production and liquefaction using offshore wind power, liquid air, and LNG cold energy

Coastal regions have abundant off-shore wind energy resources, and surrounding areas have large-scale liquefied natural gas (LNG) receiving stations. From the engineering perspectives, there are limitations in unstable off-shore wind energy and fluctuating LNG loads. This article offers a new energy scheme to combine these 2 energy units, which uses surplus wind energy to produce hydrogen, and use LNG cold energy to liquefy and store hydrogen. In addition, in order to improve the efficiency of utilizing LNG cold energy, and reduce electricity consumption for liquid hydrogen (LH2) production at coastal regions, this article introduces the liquid air energy storage (LAES) technology as the intermediate stage, which can stably store the cold energy from LNG gasification. A new scheme for LNG-LAES-LH2 hybrid LH2 production is built. The case study is based on a real LNG receiving station at Hainan province, China, and this article presents the design of hydrogen production/liquefaction process, and carries out the optimizations at key nodes, and proves the feasibility using specific energy consumption and exergy analysis. In a 100 MW system, the liquid air storage round-trip efficiency is 71.0% and the specific energy consumption is 0.189 kWh/kg, and the liquid hydrogen specific energy consumption is 7.87 kWh/kg and the exergy efficiency is 46.44%. Meanwhile, the corresponding techno-economic model is built, and for a LNG-LAES-LH2 system with LH2 daily production 140.4 tons, the shortest dynamic payback period is 9.56 years. Overall, this novel hybrid energy scheme can produce green hydrogen using a more efficient and economical method, and also can make full use of surplus off-shore wind energy and coastal LNG cold energy.

Toward Green Metaverse Networking: Technologies, Advancements, and Future Directions

As the Metaverse is iteratively being defined, its potential to unleash the next wave of digital disruption and create real-life value becomes increasingly clear. With distinctive features of immersive experience, simultaneous interactivity, and user agency, the Metaverse has the capability to transform all walks of life. However, the enabling technologies of the Metaverse, i.e., digital twin, artificial intelligence, blockchain, and extended reality, are known to be energy-hungry, therefore raising concerns about the sustainability of its large-scale deployment and development. This article proposes Green Metaverse Networking for the first time to optimize energy efficiencies of all network components for Metaverse sustainable development. We first analyze energy consumption, efficiency, and sustainability of energy-intensive technologies in the Metaverse. Next, focusing on computation and networking, we present major advancements related to energy efficiency and their integration into the Metaverse. A case study of energy conservation by incorporating semantic communication and stochastic resource allocation in the Metaverse is presented. Finally, we outline the critical challenges of Metaverse sustainable development, thereby indicating potential directions of future research towards the green Metaverse.

Analysis of energy consumption for electric buses based on low-frequency real-world data

Analysis of energy consumption for electric buses based on low-frequency real-world data

Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor Networks

Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor Networks

Analysis of energy consumption on deferred transmission of LoRaWAN down-link frame for IoT in a ship

Analysis of energy consumption on deferred transmission of LoRaWAN down-link frame for IoT in a ship

Efficiency and Energy Consumption Analysis of Infrared-Assisted Drying of Oyster Mushrooms

Efficiency and Energy Consumption Analysis of Infrared-Assisted Drying of Oyster Mushrooms

Analysis of the total energy consumption through hydrogen compression for the operating pressure optimization of an alkaline water electrolysis system

Analysis of the total energy consumption through hydrogen compression for the operating pressure optimization of an alkaline water electrolysis system

Experimental and numerical study on impact resistance of offshore wind turbine CFDST jacket

With the development of large-scale offshore wind turbines (OWT), the jacket foundation widely used to offshore wind turbines are at significant risk of collision damage from vessels and sea ice. In order to improve the impact resistance of hollow steel tube jacket foundation, a new type of concrete-filled double-skin steel tube (CFDST) jacket foundation is proposed. From the analysis of impact force, displacement, acceleration and energy consumption, the peak strain and acceleration of the jacket leg at the impact position are reduced by 57.1 % and 50.4 %, respectively. The deformation mechanism and failure mode of the two foundations under multiple impact loads were obtained by combining LS-DYNA software and test. After two impacts the hollow steel tube jacket was seriously dented and damaged in bending. However, after multiple pendulum impacts on a concrete-filled double-skin steel tube, only the supporting struts were bent, the indentation depth and displacement peak of the jacket leg are reduced by 76.9 % and 72.2 %, respectively, and the entire structure showed excellent impact resistance, overall bending stiffness and energy dissipation capacity. Finally, the experimental basis and theoretical analysis are provided for the application of concrete-filled double-skin steel tube jacket in large offshore wind turbines and the resistance to impact loads.

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

Empowering Owner-Operators of Small and Medium Commercial Buildings to Identify Energy Retrofit Opportunities

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings’ owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves.