Energy Usage Research Articles

Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption

Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.

A consortium blockchain-edge enabled authentication scheme for underwater acoustic network (UAN)

The Internet of Things (IoT) allows for automated operations in diverse fields, such as agriculture monitoring, pollution monitoring, health care, and underwater monitoring. The Internet of Underwater Things (IoUT) observes the underwater environment, assists in exploration, mitigates disasters, and monitors some factors including temperature, pressure, and pollution. The IoUT relies on a network of intelligent underwater sensors that send data to surface base stations and IoT devices for storage and analysis. Nevertheless, these systems face security risks as they operate in unattended environments. Many authentication methods depend on a centralized third party, leading to higher computation costs and energy usage. To mitigate security risks, autonomous underwater devices need secure connections and authentication. This paper suggests a decentralized authentication mechanism for UAN to safeguard against unauthorized access and ensure secure data storage in the cloud. The proposed mechanism prioritizes robustness, transparency, and energy efficiency. The suggested solution incorporates an architecture based on edge and cloud layers, utilizing customized blockchain technology for secure storage and processing of data. The security of the proposed solution has been thoroughly examined through formal analysis utilizing the Real or Random (ROR) Oracle model and Scyther tool. Informal analysis further confirms the solution’s resilience against various malicious attacks. Additionally, performance and comparative analysis demonstrate that the proposed solution surpasses existing schemes.

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.

Decentralized peer-to-peer energy trading in microgrids: Leveraging blockchain technology and smart contracts

Microgrids are regarded as vital components in contemporary realm of energy system improvement, resilience, and sustainability. In this paper a novel decentralized peer-to-peer energy trading system leveraging blockchain technology is proposed. The proposed model not only demonstrates the implementation of blockchain technology in microgrids but also transforms the energy sector by emphasizing decentralization, security and efficiency. This research aims to enhance the energy trading system by including smart contracts written on the Ethereum network. Energy Token and Demand Response contracts are integrated to enable dynamic interactions inside microgrids, which leads to a transparent, secure, and efficient energy trading system. Moreover, automation of energy transactions and elimination of intermediaries ensure cost effectiveness and utilization of excess energy potentially reduce dependency on the main grid. A microgrid system is designed in Simulink for distributed energy trading. Energy credits are represented by standard ERC-20 digital token and then demand response contract dynamically adjusts rewards and energy prices based on energy production and consumption. the ERC-20 contract manages the token transaction and then demand response contract enforces governance rules. The purpose of a demand response contract is to enable the automatic and efficient control of energy usage in response to changing demand and supply conditions. The use of the Web3 library further facilitates a direct and smooth connection between the blockchain network and microgrids. The results demonstrate the successful implementation of both smart contracts, making trading possible at noon when the combined generation from solar array and energy management system exceed the energy demand.

Accelerating chiller sequencing using dynamic programming

Effective management of heating, ventilation, and air conditioning (HVAC) systems is crucial for enhancing building energy efficiency. Chillers, a significant component of HVAC systems, are responsible for more than 50 % of energy usage of the whole cooling plant, underscoring the importance of optimizing chiller sequencing. Although Model Predictive Control (MPC) has demonstrated efficacy in enhancing chiller performance, its widespread adoption is hindered by its high computational complexity. This study introduces a dynamic programming approach coupled with a 2-step optimization method to expedite chiller sequencing optimization while upholding MPC’s real-time control capabilities. The MPC method implemented in this study integrates a load forecasting model with an artificial neural network (ANN) based chiller model. Through simulation tests utilizing historical HVAC operational data from a commercial building located in Shanghai, the proposed chiller sequencing strategy achieves a 9.73 % decrease in energy consumption. Moreover, the dynamic programming approach, especially in conjunction with the 2-step optimization process, significantly reduces the MPC solution time at each control step from 165 min to 3.61 s, making it a viable solution for real-time MPC deployment. Additionally, the research delves into the influence of the chiller model’s complexity, the optimization strategy, and control horizon on computational efficiency. This research provides a feasible solution to implement chiller sequencing in real buildings to pick up the low-hanging fruits in an effective way.

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000°C-h and with an annual energy consumption of 44-64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000°C-h, with an annual energy consumption of less than 10 kWh/m2.Considering the IPCC's RCP8.5 warming scenario, October shows a 20-40% increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Impact of various fibers on mode I, III and I/III fracture toughness in slag, fly Ash, and silica fume-based geopolymer concrete using edge-notched disc bend specimen

There is ongoing research aimed at developing cement-free concrete that not only exhibits enhanced mechanical properties but also incorporates environmentally sustainable materials. Geopolymer represents a novel inorganic cementitious material recently developed, which facilitates utilising resources derived from solid waste from industrial operations. Geopolymer is considered an ecologically sustainable substitute for Ordinary Portland cement. It significantly reduces energy usage and minimizes carbon dioxide emissions, contributing to environmental sustainability. This study investigates the combined influence of granulated blast furnace slag, fly ash and silica fume on geopolymer concrete (GC) fracture resistance. This research aims to assess the fracture toughness of GC under modes I, III, and I/III loading conditions. Four distinct fiber types, comprising both short and long steel fibers and polypropylene fibers at 1.5 % dosage, were utilized to mitigate brittleness and enhance the ductility of the material. In addition, the microstructure of GC was analysed using X-ray diffraction and scanning electron microscopy. Findings reveal that the inclusion of short and long polypropylene fibers in GC increased mode I fracture toughness by 20.98 % and 29.62 %, respectively, compared to the fiber-free specimen, with long fiber showing superior performance due to its enhanced crack-bridging ability. Steel fibers provided a more pronounced improvement, with short and long fibers increasing mode I fracture toughness by 77.77 % and 109.87 %, respectively, attributed to their capacity to hinder crack propagation and enhance fracture toughness. The long fibers exhibited an excellent fracture resistance than the short fibers and mode III is more critical than the mode I loading.

A hybrid forecasting model for general hospital electricity consumption based on mixed signal decomposition

Current research into electricity consumption forecasting for General Hospital still has considerable scope for further development, particularly in its failure to incorporate hospital-specific energy usage characteristics as input variables. This study explores the impact of the usage frequency of sizeable medical equipment on the electricity demand of general hospitals. It proposes a hybrid forecasting algorithm that integrates the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Variational Mode Decomposition (VMD) for signal decomposition with the Hyperband-LSTM deep learning algorithm to enhance prediction accuracy. ICEEMDAN is employed for preprocessing the power consumption series, while VMD is used for the secondary decomposition of high-frequency signals within the series. The Hyperband Pruner is utilized to efficiently adjust the hyperparameters of the LSTM, which is then used for electricity consumption forecasting. The predictive performance of the developed method is assessed by comparing it with 15 different forecasting models. The results indicate that the proposed method demonstrates superior forecasting performance. Applying the model to a real-case scenario, it has reduced the hospital’s electricity consumption by about 15%, providing a referable energy management solution for other medical institutions.

Effects of Electric Vehicles on Energy Sharing for Optimal Sizing of Solar PV and Battery Energy Storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.

Accurate and robust ammonia level forecasting of aeration tanks using long short-term memory ensembles: A comparative study of Adaboost and Bagging approaches

As wastewater treatment aeration systems are embracing innovative solutions to data management for operational sustainability, deep learning approaches like long short-term memory (LSTM) networks become imperative. However, how to enhance LSTMs to forecast aeration status through ensemble learning is still in its infancy. This study tackles this challenge by comprehensively comparing two ensemble learning algorithms, AdaBoost and Bagging. Both one-step and multi-step predictions were compared using performance metrics like Z-score derived from aeration set-points. The robustness of models was evaluated under quantified extreme events, such as sudden spikes in ammonia concentration. The results indicate that while AdaBoost-LSTM models slightly outperformed Bagging-LSTM models in one-step-ahead predictions, their true advantage lies in enabling precise decisions for switching aeration blowers on or off, which could avoid excess energy usage of aeration systems. This advantage was even more pronounced in multi-step forecasting. In 4-step-ahead prediction, the AdaBoost-LSTM model attained an optimal precision of 92.77%, marking an 8.88% improvement over the Bagging-LSTM model. Furthermore, AdaBoost-LSTM models showed greater resilience to fluctuations in ammonia levels, ensuring continued stable aeration. Therefore, AdaBoost-LSTM ensembles demonstrate greater suitability for accurate and robust ammonia forecasting of aeration tanks, leading to sustainable operation and target costs/energy savings.

Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability

This study critically evaluates whether the current and projected generation of renewable energy can meet the escalating global demand for electricity from digital data growth. Our modelling forecasts reveal a concerning trend: despite the expansion in renewable energy capacities, they are likely insufficient to satisfy the burgeoning electricity needs of the digital data sector. More alarmingly, there is a real risk that the demand for digital data could soon exceed feasible electricity production capabilities. This paper underscores the urgent necessity for a data-centric sustainability approach across all supply chains, sectors, industries, and nations. Such measures are crucial to increase efficiency, cut energy usage, and transition towards a decarbonized digital ecosystem, thereby supporting the global pursuit of a sustainable, net-zero future. This research highlights a critical junction in energy policy and digital infrastructure planning, urging immediate action to reconcile digital advancement with ecological sustainability.

Sustainability in Technology and Green IT

For the modern acceleration pace of tech development, especially the sustainability of green IT, significantly relates to it. Green IT refers to the use of resources at the computer machine level in an environmentally friendly way for the minimization of energy usage and carbon emissions as well as adverse impacts on the environment caused by IT-related activities [Murugesan, 2008]. The rising digital services also contribute to increased energy consumption from data center and cloud services, thus emitting more greenhouse gases [Baliga et al., 2011]. The paper deals with renewable energy in IT systems, the recycling of e-waste, sustainable design of a data center, and how computing can be made more energy-efficient [Hanne & Falk, 2010; Khan & Qureshi, 2018]. The study identifies whether practical applications and examples of Green IT solutions show benefits and challenges in real life. Industrial case studies. The study found that despite so much scope for Green IT to reduce the negativity in most developments, Environmental Impact of the IT sector: All these issues, such as high cost, scalability and regulatory, should be reduced [Murugesan, 2008; Berkhout, 2014]. In order to promote sustainability across IT industries, further technological developments and global policy reforms will be essential [Murugesan, 2008; Hanne & Falk, 2010; Khan & Qureshi, 2018].

Optimal Design of Water Distribution System Using Improved Life Cycle Energy Analysis: Development of Optimal Improvement Period and Unit Energy Formula

Water distribution systems (WDSs) are crucial for providing clean drinking water, requiring an efficient design to minimize costs and energy usage. This study introduces an enhanced life cycle energy analysis (LCEA) model for an optimal WDS design, incorporating novel criteria for pipe maintenance and a new resilience index based on nodal pressure. The improved LCEA model features a revised unit energy formula and sets standards for pipe rehabilitation and replacement based on regional regulations. Applied to South Korea’s Goyang network, the model reduces energy expenditure by approximately 35% compared to the cost-based design. Unlike the cost-based design, the energy-based design achieves results that can relatively reduce energy when designing water distribution networks by considering recovered energy. This allows designers to propose designs that consume relatively less energy. Analysis using the new resilience index shows that the energy-based design outperforms the cost-based design in terms of pressure and service under most pipe failure scenarios. The implementation of the improved LCEA in real-world pipe networks, including Goyang, promises a practical life cycle-based optimal design.

ANALISIS KANDUNGAN SULFUR, NITROGEN, DAN KLORIN DALAM BIOPELLET BERBASIS LIMBAH PADAT KAYU PUTIH DAN GONDORUKEM

In recent years, a transition towards renewable energy sources has begun to reduce fossil energy usage. One alternative renewable energy source is biopellets, which have gained public interest as an environmentally friendly fuel. Biopellets are a type of renewable energy derived from biomass, which, if it emits too much carbon, can lead to pollution. Carbon emissions are also related to combustion efficiency, as complete combustion can help reduce carbon emissions. This study aims to determine combustion emissions and combustion efficiency of biopellets made from eucalyptus solid waste with the addition of rosin to optimize the use of raw materials as renewable energy. The study employs an experimental laboratory approach with a factorial design to test the impact of sulfur, chlorine, and nitrogen content on the combustion efficiency of biopellets made from eucalyptus waste and rosin. Results indicate that the sulfur and chlorine content remain above the French standard (ITEBE), at 0.31% and 1.12%, respectively, while nitrogen content is below the standard at 0.48%. The resulting combustion efficiency ranges between 9.569% and 9.701%.

Beyond Lux: methods for species and photoreceptor-specific quantification of ambient light for mammals.

Light is a key environmental regulator of physiology and behaviour. Mistimed or insufficient light disrupts circadian rhythms and is associated with impaired health and well-being across mammals. Appropriate lighting is therefore crucial for indoor housed mammals. Light is commonly measured in lux. However, this employs a spectral weighting function for human luminance and is not suitable for 'non-visual' effects of light or use across species. In humans, a photoreceptor-specific (α-opic) metrology system has been proposed as a more appropriate way of measuring light. Here we establish technology to allow this α-opic measurement approach to be readily extended across mammalian species, accounting for differences in photoreceptor types, photopigment spectral sensitivities, and eye anatomy. We develop a high-throughput method to derive spectral sensitivities for recombinantly expressed mammalian opsins and use it to establish the spectral sensitivity of melanopsin from 13 non-human mammals. We further address the need for simple measurement strategies for species-specific α-opic measures by developing an accessible online toolbox for calculating these units and validating an open hardware multichannel light sensor for 'point and click' measurement. We finally demonstrate that species-specific α-opic measurements are superior to photopic lux as predictors of physiological responses to light in mice and allow ecologically relevant comparisons of photosensitivity between species. Our study presents methods for measuring light in species-specific α-opic units that are superior to the existing unit of photopic lux and holds the promise of improvements to the health and welfare of animals, scientific research reproducibility, agricultural productivity, and energy usage.

Enhancing the energy and environmental efficiency of the city's infrastructure based on optimizing its fuel and energy balance

The article considers the direction of increasing the energy and environmental efficiency of the city's energy infrastructure through the modernization of existing thermal power plants (TPP) and combined heat and power (CHP) plants using combined-cycle gas turbine (CCGT) plants. Fuel and energy balances of electric power plants have been constructed, and the energy and environmental effects of the use of CCGT have been assessed. The modeling of energy and environmental indicators for the thermal circuits of the CCGT was conducted using the Thermoflex, GT PRO software (Thermoflow) and the ISC Manager program developed at the National Research University "Moscow Power Engineering Institute (MPEI)". The analysis of CCGT efficiency in urban energy applications is based on modeling and optimizing the city’s fuel and energy balance (FEB). For this purpose, the OptiTEB program developed at the Department of PTS at the National Research University "MРEI" is used. Constructing fuel and energy systems in cities and regions is a pressing task that enables the planning of a strategy for the development of the urban fuel and energy complex (FEC), with an assessment of the magnitude of harmful emissions, including greenhouse gases. The article presents the results of work on establishing scientific foundations for modern heat supply systems, exemplified by a mathematical model of the Moscow thermal power plant and its optimization, taking into account the projected development of electric transport infrastructure over the coming decades, improving the thermal protection characteristics of newly constructed buildings, and the potential increase in the use of renewable energy sources (RES). The growth in the share of electric transport should be linked to an increase in the share of renewable energy sources (RES). Simply increasing the number of electric vehicles without a significant rise in renewable energy usage in the city's energy balance will not result in a reduction of carbon dioxide and harmful substance emissions. Expanding electricity generation through CCGT, alongside the growth of renewable energy use in the city, can lead to a significant decrease in carbon dioxide emissions.

Enhancing thermal performance of phase change materials in building envelopes

AbstractPhase change material in conjunction with a passive latent heat thermal energy storage approach is a potentially effective way to solve the growing concerns about building energy usage. This research examines the thermal performance of building envelopes in Miskolc, Hungary. The factors impacting the thermal performance of phase change material integrated into the building envelope were assessed. The findings indicate that the enthalpy and melting temperature of the phase change material significantly impact the effectiveness of phase change material-based walls. It was determined the optimal location of the phase change material layer is possible. This determination is intricately related to the thermal properties of the phase change material and the prevailing environmental conditions.

ENHANCING OPERATIONAL EFFECIENCY IN CRUSHERS THROUGH THE USE OF AN INDUSTRY 4.0 BASED CRUSHER CONTROL SYSTEM

In the aggregate and mining industry, an excessive flow rate of raw material from the feeder, caused by irregularities in the raw material being processed by crushers, can lead to blockages or excessive strain on the crusher. Conversely, a low flow rate of raw material can result in high energy consumption by the crusher, despite operating at a low capacity. The issues encountered in the first group result in excessive energy usage in the secondary and tertiary groups. The study focuses on a system that utilizes artificial intelligence and is based on industry 4.0 principles. The system aims to maintain production in the crusher within a specific range by controlling the flow rates of the feeders using an algorithm. This control is done automatically without the need for user intervention. The system optimizes energy consumption while maximizing production capacity and ensures uninterrupted operation. The system was developed during the installation phase at an aggregate pilot plant in the Kahramanmaraş Evri region. It assesses the material capacity data using a belt scale on the crusher, feeder, and output conveyor. This data is then compared to the limit values stored in the database, and the system generates an information signal to initiate the required control actions. Based on this matching result, it sends information to the inverter, coordinates the production cycle, manages and documents the process stages using a structured learning system and artificial intelligence logic.The installation procedure was conducted using two distinct density gradation inputs. As a consequence of the reporting, the records in the report were compared during both the active and inactive states of the system. The project achieved an efficiency of 22% in terms of energy consumption per unit capacity. Based on the whole yearly energy usage, a total of 368609.7 kg of carbon emissions were averted. The facility's aggregate crushing capacity was increased by 40%.

High-speed voltage level-up shifter with wide range conversion for system-on-chip applications

Abstract The purpose of this investigation is to demonstrate a level shifter circuit that enhances voltage switching capacity while simultaneously reducing latency by employing a cascaded current mirror, input, and output inverters. We improved our design using multi-threshold Complementary metal oxide semiconductor (MTCMOS) technology by adding stackable transistors, super-cutoff mechanisms, and cascaded techniques. The level shifting between voltage ranges of 0.3 V and 2.0 V is accomplished by the proposed level shifter. Our design has been fine-tuned to meet the needs of system-on-chip applications by meticulously optimizing performance characteristics like energy usage, latency, and space utilization. The proposed level shifter has an area of 6.936 µm2 and a power consumption of 17 nW, with a delay of 90.64 ps. The proposed work has been done on the Cadence Virtuoso Tool using 45 nm technology. Our suggested level shifter is more effective than earlier methods in terms of latency and conversion range. In addition to delivering considerable reductions in space and power consumption, the suggested design promises a 50-fold increase in operating speed.