Search

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery. [ABSTRACT FROM AUTHOR]

Forecasting of long-term annual electricity demand is studied utilizing historical data for electrical energy consumption and socio-economic indicators—gross domestic product, population, import and export values for the case of Turkey between 1975 and 2020. A quadratic model for electrical energy consumption was applied to define the relation between the historical and predicted data. This model used metaheuristic algorithms; genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), artificial intelligence (AI) approaches; neural networks (NN), and adaptive network fuzzy inference systems (ANFIS), and machine learning (ML) applications; all models undergo testing, but the top four models—stepwise linear regression (SLR), NN, Gaussian process regression (GPR) with exponential, and GPR with squared exponential—are selected for additional research to determine the best forecasting model based on their forecasting performance. Comparing the finalized models SLR produced the best forecasting model with a mean absolute percentage error (MAPE) value of 2.36%, followed by GA with 2.97%. Turkey's yearly electrical energy consumption is projected under three possible scenarios through 2030. Finding the most appropriate forecasting model among the models studied for long-term electrical energy forecasting is ultimately the primary goal of this research. Simulations are done on the MATLAB™ platform. [ABSTRACT FROM AUTHOR]

Buildings play a significant role in the rise of energy consumption and carbon emissions. Building operations are responsible for 28% of the world's carbon emissions. It is crucial, therefore, to evaluate the environmental impact of various buildings' operational phase in order to implement sustainable strategies for the mitigation of their energy usage and associated carbon footprint. While numerous studies have been conducted to determine the carbon footprint of conventional building operation phases, there are still a lack of actual data on the operational carbon (OC) emissions of mass timber buildings. There is also a lack of research pertaining to the operational carbon of buildings within larger campuses and their inherent energy usage. This study, therefore, aims to quantify empirical data on the carbon footprint of a mass timber building, using, as a case study, the recent Adohi Hall building, situated at the University of Arkansas, Fayetteville. The study also aims to examine and identify the best energy use scenarios for the campus building under consideration. The research team obtained data on Adohi Hall's energy consumption, fuel input usage, and other utilities (such as water, electricity, chilled water, and natural gas) accounting for the operation of the building from 2021 to 2023, a span of three years. The University of Arkansas Facilities Management (FAMA) provided the data. The study relies on the life cycle assessment (LCA) as its primary approach, with SimaPro 9, Ecoinvent v3.7 database, DataSmart, version 2023.1 and the U.S. Life Cycle Inventory (USLCI) database utilized to model the energy and water consumption of Adohi Hall during the operational phase (B6 & B7). The results indicate 4496 kg CO2 eq emissions associated with the operation per square meter of Adohi Hall over its 50-year lifespan. The study also examines various scenarios of fuel sources leading to carbon emissions and provides insights into reduction strategies during the operational phase of buildings. Among them, the electricity based on a cleaner fuel source diversification, according to realistic expectations and technological advancements projections, results in a 17% reduction in Adohi Hall's OC. Due to the usage of the combined heat and power (CHP) plant on the campus of the University of Arkansas as a complementary source of electricity and heating for Adohi Hall, the resulting carbon emission is approximately 21% (20.73%) less when compared to similar buildings in the same city but outside the campus. The study, therefore, reveals that CHP plant development is a highly effective strategy for building OC reduction. [ABSTRACT FROM AUTHOR]

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables. [ABSTRACT FROM AUTHOR]

The equivalent stress block is recommended for use in the design of reinforced concrete sections to simplify the analysis of the composite behavior of concrete and steel reinforcement. In most current codes, a rectangular equivalent stress block is provided. The design parameters of the equivalent block were recommended many years ago. Due to the importance of the equivalent stress block concept, numerous investigations have been performed to increase its accuracy. In the current paper, an exploration of the rectangular equivalent stress block has been carried out using the energy modeling approach. Energy modeling is a new general approach for studying the behavior of concrete elements. In this method, the energy consumed (work done) can be determined by integrating the force-displacement diagram (in the current study this will be the concrete stress-strain curve in compression). Schematic and equivalent stress-strain curves for concrete in uniaxial compression provided in most current codes and relevant textbooks were considered in this research. The codes taken into account in the current study are ACI-318-19, Canadian Code CSA A23.3-04, Eurocode EC-2, and Chinese standard GB 500 10 - 2002. The energy consumed by these curves for different values of concrete strength has been compared with numerous experimental results. This comparison shows that the results of the equivalent stress block provided in most of the considered current codes are conservative. Applying the energy modeling for the considered experimental stress-strain curves a modified equivalent stress block is recommended for practical use. The results of the proposed equivalent stress block are in good agreement with the experimental ones. The ratio between the predicted total energy engaging the proposed model and the total energy calculated for the experimental results ranges between 0.95 and 1.08 with a mean value equal to unity. [ABSTRACT FROM AUTHOR]

Architects and engineers who seek design decision aid for sustainable building and city design frequently experience decisional conflict and require support across the design process. Building performance simulations have become central to supporting the design process, but little is known about the fit and usability of simulation tools and the factors influencing the implementation of simulation-based design in practice. This paper presents a novel framework for simulation tool usability called USER-FIT. The framework defines usability based on ISO definitions, a review of evidence in the literature, and our experience supporting the development of the framework. USER-FIT provides six measures for modelers and software developers to test how a simulation tool is useful, usable, and satisfactory for the intended users to inform sustainable design decision-making. Further research and testing are needed to support better the acquisition and implantation of usability testing for building performance simulation tools and applications. [ABSTRACT FROM AUTHOR]

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia's grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia. [ABSTRACT FROM AUTHOR]

Indonesia is one of the fastest growing economies in the world, with an electricity system reliant on fossil fuels and renewable electricity contributing a small portion of their growing demand. In this study, an optimization approach is utilized to analyze different policy-driven pathways for Indonesia's long-term electricity planning by formulating different scenarios based on current policy discussions and changes in the power sector. Through an optimization model, several scenarios incorporating multiple changes such as cost reductions of technologies, halting coal capacity beyond the current pipeline, and carbon taxation schemes are built. The findings show the magnitude of transformation needed to achieve net zero goals and consolidate the discussion around halting coal capacity addition beyond currently planned in terms of technical, environmental, and economic aspects. These findings will be useful to electricity sector policymakers and planners as Indonesia continues along its sustainable economic development pathway.

Desalination using renewable energy offers a route to transform our incumbent linear consumption model to a circular one. This transition will also shift desalination from large-scale centralized coastal facilities toward modular distributed inland plants. This new scale of desalination can be satisfied using solar energy to decarbonize water production, but additional considerations, such as storage and inland brine management, become important. Here, we evaluate the levelized cost of water for 16 solar desalination system configurations at 2 different salinities. For fossil fuel-driven plants, we find that zero-liquid discharge is economically favorable to inland brine disposal. For renewable desalination, we discover that solar-thermal energy is superior to photovoltaics due to low thermal storage cost and that energy storage, despite being expensive, outperforms water storage as the latter has a low utilization factor. The analysis also yields a promising outlook for solar desalination by 2030 as solar generation and storage costs decrease.

Abstract The majority of Europe’s building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications.

Energy storage can accelerate the decarbonization of the electrical grid. As useful energy storage technologies are developed, investors and manufacturers want to determine the needs for storage in a wide range of scenarios. In this study, we introduce a strategy for identifying the types of storage that will be most valuable to the grid given specific generation and load profiles. This method estimates the annual minimum number of cycles for each storage, how long each holds the charge, and charging and discharging rates for an idealized system, giving insight into tomorrow's complex systems. We demonstrate the proposed hierarchical approach and quantify how many fewer times wind-driven grids cycle the storage at night compared with solar-driven grids, as well as how winter-dominant wind generation and latitude-tilt solar may reduce the need for seasonal storage. Also, we quantify how higher discharging rates are required for energy storage products that cycle most frequently.

A comprehensive energy audit of a light rail maintenance facility was performed to assess its energy performance and identify potential scope for improvements. The facility's energy use intensity (EUI) for 2022 was 404 kWh/m2—more than double the benchmark EUI for maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the load factor was 0.22—significantly lower than the recommended minimum of 0.75 for an efficient building. The energy audit encompassed an in-depth evaluation of the facility's structural and operational characteristics, comprising HVAC systems, lighting, the building envelope, and energy-intensive machinery. An energy model of the facility was developed to emulate the facility's energy performance in 2022. Following the energy model's validation, an analysis was conducted to identify opportunities for improving energy efficiency. Post-implementation of energy efficiency measures for the facility, the projected annual reductions are 1086 MWh of electricity, 5034 GJ of natural gas, utility savings of USD 162,402, and net GHG emissions reductions of 584 metric tons of CO2e. A subsequent 30% reduction in EUI to 283.6 kWh/m2 could be achieved with an 86% improvement in load factor, that is, increasing it from 0.22 to 0.41. This study emphasizes the need for energy audits and modeling for maintenance facilities to reduce Scope 1 and 2 emissions. [ABSTRACT FROM AUTHOR]

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction. [ABSTRACT FROM AUTHOR]

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot's power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot's energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area. [ABSTRACT FROM AUTHOR]

The majority of Europe's building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications. [ABSTRACT FROM AUTHOR]

Summary: The increasing cooling needs in commercial buildings, exacerbated by climate change, warrant immediate attention. These buildings, characterized by their long lifespans and slow stock turnover, change consumption over time. This study develops simple, interpretable data-driven models using weather- and occupancy-related features to analyze the cooling in different types of co-located buildings. Over five years, our models effectively predict the cooling load across buildings with R-squared values of 81%–87%. Factoring out geography-driven differences, we identify strong heterogeneity within and across different buildings. The average estimated base load cooling varies between 0.50 and 4.4 MJ/m2/day across buildings, with healthcare facilities exhibiting the highest loads and residences the lowest. Consumption increases by 7.6%–9.8% for every 1°C increase in mean daily outside temperature, with up to 27% reductions in offices on weekends. These insights enable diagnoses of inefficiencies, post-retrofitting performance tracking, and proactive planning for climate-related impacts.

Summary: The energy transition is a multinational challenge to mitigate climate change, with a joint reduction target for greenhouse gas emissions. Simultaneously, each country is interested in minimizing its own energy supply cost. Still, most energy system models neglect national interests when identifying cost-optimal transition pathways. We design the European energy system transition until 2050, considering competition between countries in a shared electricity and carbon market using bilevel optimization. We find that national objectives substantially impact the transition pathway: Compared to the model solved using the common centralized optimization, the overall installed capacity increases by just 3% when including national interests. However, the distribution of the installed capacity changes dramatically by more than 40% in most countries. Our results underline the risk of miscalculating the need for national capacity expansion when neglecting stakeholder representation in energy system models and demonstrate the need for cooperation for an efficient energy transition.

The transition towards sustainable and digital cities is a complex challenge with crucial implications for citizen well-being. In this context, the concept of an Urban Digital Twin (UDT) emerges as an innovative resource, although the methodologies and technologies applied are still at an initial stage. This article explores the use of the UDT in the context of energy transition, focusing on the experience of Bologna, an Italian city committed to climate neutrality by 2030. Experimentation in specific case studies demonstrates how the UDT enables data analysis and scenario simulation to optimise energy efficiency and promote decarbonisation, facilitates data collection and integration, overcomes privacy issues, and enables multi-stakeholder governance. Article info Received: 18/03/2024; Revised: 10/04/2024; Accepted: 20/04/2024

Summary: The design of photovoltaic modules for vehicle-integrated photovoltaics (VIPVs) must consider specific operating conditions like partial shading. Module cell interconnection topology must demonstrate resilience to such conditions to maximize energy generation without compromising shadow-free performance, manufacturing complexity, or cost. This study presents a modeling tool for VIPV, calculating effective irradiance on the VIPV surface using Light Detection And Ranging (LiDAR) point clouds to estimate the direct component and sky images for the diffuse irradiance. Subsequently, energy generated by the VIPV module is computed using circuit simulation software. Different module topologies regarding cell number, size, interconnections, and bypass diodes have been analysed. Results show significant daily energy production variations under partial shading conditions for different configurations (up to 41%). While integrating a large number of bypass diodes (160) offers optimal performance, this configuration may be impractical due to manufacturing complexity. However, similar results are achievable with appropriate configurations containing parallel branches and only 8 bypass diodes.

The equivalent stress block is recommended for use in the design of reinforced concrete sections to simplify the analysis of the composite behavior of concrete and steel reinforcement. In most current codes, a rectangular equivalent stress block is provided. The design parameters of the equivalent block were recommended many years ago. Due to the importance of the equivalent stress block concept, numerous investigations have been performed to increase its accuracy. In the current paper, an exploration of the rectangular equivalent stress block has been carried out using the energy modeling approach. Energy modeling is a new general approach for studying the behavior of concrete elements. In this method, the energy consumed (work done) can be determined by integrating the force-displacement diagram (in the current study this will be the concrete stress-strain curve in compression). Schematic and equivalent stress-strain curves for concrete in uniaxial compression provided in most current codes and relevant textbooks were considered in this research. The codes taken into account in the current study are ACI-318-19, Canadian Code CSA A23.3-04, Eurocode EC-2, and Chinese standard GB 500 10 – 2002. The energy consumed by these curves for different values of concrete strength has been compared with numerous experimental results. This comparison shows that the results of the equivalent stress block provided in most of the considered current codes are conservative. Applying the energy modeling for the considered experimental stress-strain curves a modified equivalent stress block is recommended for practical use. The results of the proposed equivalent stress block are in good agreement with the experimental ones. The ratio between the predicted total energy engaging the proposed model and the total energy calculated for the experimental results ranges between 0.95 and 1.08 with a mean value equal to unity.

Urban energy modeling is essential in planning electricity generation and efficiently managing electric power systems. Various urban energy models were developed for several energy-driven applications, including emission reduction, retrofit analysis, and forecasting. Electricity load forecasts help to estimate the load demand and effectively aid in power system operation and balancing. The accuracy of load forecasts at high temporal and spatial resolution can impact system planning and operation. Therefore, it is essential to know the factors that affect the accuracy of these forecasts and how they can be improved regarding the current state of the art. This article reviews the recent literature on data-driven electricity load forecasts in three steps. First, different phases of the review process are explained to select and analyze recent literature on machine learning-based short-term load forecasts. Then various aspects of load forecasting techniques have been reviewed, addressing their advantages, disadvantages, temporal resolution, and performance. Finally, the review covers the current challenges in load forecasting and describes the reasons for performance degradation and lower accuracy. Based on the reviewed literature, it was found that temperature, user load profiles, and proper management of input data highly affect load forecast accuracy. In addition, shortcomings of existing performance evaluation metrics make the applicability of those techniques questionable. Finally, we conclude the review by highlighting the necessary actions to improve load forecast accuracy that are relatively unexplored and can be used as a reference for future research on accurate load forecasts.

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network’s capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

Copyright of Agathon: International Journal of Architecture, Art & Design is the property of DEMETRA CE.RI.MED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the evolving landscape of sustainable energy, efficient management of the hydrogen supply chain is paramount. This study addressed the critical need for decision-making support in this sector, highlighting the development and potential impact of a comprehensive hydrogen supply chain decision database. Utilizing a combination of qualitative and quantitative research methods, the study involved the collection and analysis of data across various stages of the hydrogen supply chain. Emphasis was placed on identifying key decision-making factors, integrating diverse data sources, and employing advanced analytical techniques to enhance the database's utility. The findings revealed significant insights into the hydrogen supply chain, including bottlenecks, efficiency parameters, and potential areas for optimization. The developed database demonstrated its capability to aid in strategic decision making, offering a tool for stakeholders to navigate the complexities of hydrogen supply and distribution. The creation of the hydrogen supply chain decision database marks a step forward in the field, providing a valuable resource for researchers, policymakers, and industry professionals. It underscores the necessity of data-driven approaches in optimizing the hydrogen supply chain, potentially contributing to the acceleration of sustainable energy initiatives. [ABSTRACT FROM AUTHOR]

The world has witnessed a global surge in energy consumption and carbon footprint since the industrial revolution. Data centers are claimed to be the second most significant contributor of the havoc greenhouse gasses. This paper deals with modeling carbon footprint of green data centers. Initially, we use a panel dataset of a green data center that mostly relies on green energy resources for power. Our study reveals that in spite of massive renewable energy usage, the carbon footprint trend of this data center is quite significant. Alongside, due to massive nuclear energy usage in this data center, a hefty amount of nuclear waste is generated causing a global threat to sustainability. This is a novel paper that pinpoints that though green data centers claim they are zero-carbon data centers but the reality is different. We prove that green data centers also emit significant amount of greenhouse gasses and cause danger to sustainability. Alongside, we provide a nuclear footprint estimator that effectively calculates the nuclear emission and carbon footprint from the data center each hour. We also provide a remedy to this entire situation and provide a carbon footprint model in this paper that optimizes the total carbon emission from this green data center. [ABSTRACT FROM AUTHOR]

Increasing energy production and consumption and the need to save and increase its efficiency will lead us to manage the energy system on both supply and demand sides with higher accuracy. This study presents a new model scheme for Mediterranean climate areas' energy systems due to the importance of energy modeling to meet energy systems' needs. This model aims to provide an interactive energy system for two short and medium‐term time horizons (2025 and 2030, respectively) by targeting three main objectives: total primary energy supply, greenhouse gas emissions, and system cost. North Khorasan province in northeastern Iran has been selected for the case study as an example of mountainous and Mediterranean climates. EnergyPLAN has been used as a modeling tool. Accordingly, after examining the study area's current state, its future needs are forecasted using the support vector machine algorithm. The study area's optimal energy system is modeled within the desired time horizons by applying and comparing four different scenarios, including business as usual (BAU), power plant optimization, and demand response with wind or solar power plants. The results show that the part of the energy demand provided by wind scenario's situation is favorable and can meet the study area's future demands. This scenario will reduce CO2 emissions in 2025 and 2030 by 93.87 and 227.78 kilotonnes per year, respectively, compared to the BAU scenario. It is also estimated that this scenario's implementation will save the total annual cost of 8.27 and 156.06 million euros compared to the BAU scenario in 2025 and 2030. [ABSTRACT FROM AUTHOR]

Pakistan is fighting a long‐standing energy crisis with an unbalanced, fossil‐fuel dominated energy mix mired with climatic catastrophes. World Wide Fund for Nature (WWF), in its fight against climate change, has proposed a global energy model that illustrates a shift to renewable energy completely by the year 2050. In this study, we have scaled down that energy model for Pakistan to demonstrate country level implementation of WWF's vision. Scenario‐based energy model of Pakistan is developed in this research using LEAP Software. Four scenarios namely, Business‐As‐Usual (BAU), Alternative and Renewable Energy (ARE), Green Energy (GE) and Advanced Sustainable Energy (ASE) are developed. The BAU scenario is based on government's existing policies and plans, and ASE scenario shows the crux of WWF's Global vision on Pakistan's scale. ARE and GE are intermediate scenarios reflecting a transition from BAU to ASE. Simulation results demonstrated the demand in ASE is halved as compared to BAU and its energy mix is homogeneous and completely renewable. In conclusion, ASE gave country‐level insights into WWF's vision towards the ways to contend climate change. This work inspires country‐scale modeling of WWF's vision for other countries as well. [ABSTRACT FROM AUTHOR]

Summary: Climate change has prompted many communities to set targets for carbon-free power supplies, but they often lack data-driven strategies to achieve them. We present a comprehensive analysis of an entirely renewable electric power system that can maintain operating reliability and resource adequacy using detailed models of the city of Los Angeles power grid. In consultation with the operating utility, the Los Angeles Department of Water and Power (LADWP), and the local community, we develop four supply scenarios across three demand projections to analyze which types of infrastructure and operational changes would achieve reliable electricity at least cost. We find that a reliable, 100%-renewable power system yielding more than $1 billion annually in health and climate co-benefits is achievable. Solar can supply most future energy needs, while combustion turbines that use renewable, storable carbon-neutral fuels are key to maintaining reliability. This study provides a replicable methodology that other jurisdictions globally can follow. Science for society: Communities and businesses nationwide have set ambitious goals to combat climate change by generating 100% of their electricity from carbon-free or renewable energy sources. Until now, there has been no comprehensive analysis of the possible pathways to achieve these goals on the scale needed to power the largest US cities. Here, we present findings from the Los Angeles 100% Renewable Energy Study (LA100), a thorough and wide-reaching assessment of the factors needed to make a fully renewable utility system operate reliably and deliver adequate electricity to more than 4 million residents. Our analysis uses detailed models of the city’s power grid to examine not just renewable resource options and technical solutions related to generation, transmission, and distribution systems but also the balance of supply and demand, variability and reliability, and affordability and viability—all through the lens of changing demographics and climate conditions.

Summary: Fuel cells (FCs) have attracted widespread attention as a highly efficient, clean, and renewable energy conversion technology. Proton exchange membrane (PEM), as one of the core components of FCs, plays a crucial role, and a comprehensive summary of its development is essential for promoting rapid progress in the field of sustainable energy. This article provides a comprehensive review of the development status and research trends of PEMs over the past twenty-eight years, based on statistical analysis and data mining techniques. Price, sustainability, stability, and compatibility issues are the main challenges faced by current PEMs used in FCs research. The current research focuses mainly on the characterization, performance optimization, enhancement mechanisms, and applications of PEMs in FCs. This review provides a systematic summary of PEM materials, serving as a valuable reference for the development, application, and promotion of new PEM materials in FCs.

Summary: China as a major coal-consuming economy faces the challenge of balancing economic development and carbon neutrality goal. This paper incorporates both efficiency-based and equity-based carbon neutrality policies into a numerical model to quantitatively assess how coal reduction under various carbon-neutral policies affects energy mix, economic growth, and industrial structures by 2060. Results show the nationwide coal intensity will ultimately plunge by over 95% from 2017 to 2060, mainly attributed to the coal-phasing-out in most industries. National Gross Domestic Product losses reaches 4,951 billion USD in efficiency-based scenarios by 2060, and the economic losses are even more severe in less developed provinces, especially provinces in Northern China. Although the equity-based policy can reduce the economic burden for the Northern China, the equity-based policy is accompanied by a significant regional shift in coal across the country: eastern coal-intense industries will be relocated northward, leading to increases in embodied coal consumption.

Summary: Fuel poverty, a pressing issue affecting social prosperity, has been exacerbated during the energy crisis triggered by the Russia-Ukraine conflict. This problem can be more severe for off-gas regions. Our study investigates heat pumps (HPs) as a cost-effective alternative to off-gas heating to alleviate fuel poverty in England and Scotland. We analyze regional fuel poverty rates and the associated greenhouse gas emission reduction by replacing all off-gas heating with HPs, observing positive effects under pre-crisis and crisis conditions, with existing government support for HP upfront costs. HP rollout can burden distribution networks especially for certain regions, but our correlation analysis shows that high benefits do not always come with network costs at the regional level, and we identify “priority” regions with low costs and high benefits. These findings provide valuable insights for policymakers to address fuel poverty and reach decarbonization. The methodology is adaptable to other countries with appropriate datasets.

In the backdrop of COVID19 recovery, Pakistan is still struggling to cope with the economic challenges and disruptions caused in the energy supply chain. On one hand where COVID has brought serious socio-economic costs and prolonged delays, it has also provided opportunity for developing countries such as Pakistan to “build-forward-better” their economies in a more sustainable and climate friendly manner. This study particularly highlights the impact of COVID on energy supply and demand sectors of Pakistan, its near- and long-term impacts, and what policy interventions can be adopted to put Pakistan on-track to achieve its Nationally Determined Contributions (NDCs). The economic focus in on “Green Recovery” and what key interventions will foster a rapid transition towards decarbonization in Pakistan. Low Emission Analysis Platform (LEAP) model is used to provide energy sector outlook (2020-2040) of Pakistan under different scenario i.e., Pre COVID growth, Business-as-Usual, Slow Recovery, and Green Recovery from COVID. The results obtained from the model depicts that following a green recovery scenario, Pakistan can reduce around 10 Mtoe (9%) of its total energy use, 53 TWh of electricity, 19 Mt of emissions from demand sectors, and 11 Mt of emissions from the power sector by 2030. For total levelized cost of the power sector, the green recovery scenario represents a generation cost of $13 billion by 2030 which further highlights that energy efficiency could lead to cost savings of approximately $3 billion each year by 2030. Green recovery is however still a daunting task as it would require economic stimulus of $8 billion only to recover to its pre COVID scenario and total investments of $120 billion by 2030.

This paper takes the air conditioning energy consumption in railroad building design as the main object of analysis and uses an orthogonal experimental design method to study the influencing factors and effects of energy consumption in the Middle East railroad construction design. Stochastic working condition simulation is carried out for the typical models of passenger stations in five climate zones to obtain the data set of the passenger station energy consumption model, and the multiple linear regression prediction models of air conditioning energy consumption in different regions are established. Combined with the hour-by-hour average outdoor temperature, hour-by-hour passenger flow and lighting and related equipment data in the waiting hall of a city in July, the air-conditioning cold load of railroad passenger stations is simulated and analyzed. Explore the implementation effect of energy-saving strategies for railroad passenger station air-conditioning system, and for the traditional PID control strategy, put forward the control strategy based on model prediction, analyze the change of centralized air-conditioning system cold load and the change of the actual performance coefficient of the chiller unit under different control strategies. The data show that compared with the traditional PID control, the model prediction control reduces the total energy consumption by 0.2063, which realizes the energy-saving optimization of the air conditioning system for railroad building design.

Summary: A novel value sharing (VS) method is proposed that distributes the energy communities (ECs) value based on the individual contribution to the total surplus/deficit. It considers the load-generation profile of each EC member and allocates a higher share to members who contribute to the EC revenue. The lowest share is received by the members with the highest demand that has to be supplied from the shared generation or from the grid, contributing to the EC cost. Several allocation methods are compared using the fairness index (FI), and, for setting the strategy of the EC using a decision model, as the strategy may vary over time, an objective function is defined as a combination between FI and self-sufficiency index using weighting coefficients. The methodology is implemented as an algorithm that automatically calculates and distributes the gain. For the proposed VS method, the FI is between 0.81 and 1.

Summary: This paper reviews the key information and communication technologies that are necessary to build an effective industrial energy management system considering the intermittence of renewable sources like wind and solar †. In particular, we first introduce the concept of software-defined energy networks in the context of industrial cyber-physical systems aiming at the optimal energy resource allocation in terms of its environmental impact. The task is formulated as a dynamic scheduling problem where supply and demand must match at minute-level timescale, also considering energy storage units. The use of (explainable and trustworthy) artificial intelligence (AI), (informative) networked data, demand-side management, machine-type (wireless) communications, and energy-aware scheduling in industrial plants are explored in detail. The paper also provides a framework for understanding the complexities of managing renewable energy sources in industrial plants while maintaining efficiency and environmental sustainability.

The roasting process of Gari (Gelatinized cassava mash), a shelf-stable cassava product, is energy-intensive. Due to a lack of information on thermal characteristics and scarcity/rising energy costs, heat and mass transfer calculations are essential to optimizing the traditional gari procedure. The objective of this study was to determine the proximate, density, and thermal properties of traditionally processed de-watered cassava mash and gari at initial and final processing temperatures and moisture contents (MCwb). The density and thermal properties were determined using proximate composition-based predictive empirical models. The cassava mash had thermal conductivity, density, specific heat capacity, and diffusivity of 0.34 to 0.35 W m−1 °C−1, 1207.72 to 1223.09 kg m−3, 2849.95 to 2883.17 J kg−1 °C, and 9.62 × 10−8 to 9.76 × 10−8 m2 s−1, respectively, at fermentation temperatures and MCwb of 34.82 to 35.89 °C and 47.81 to 49%, respectively. The thermal conductivity, density, specific heat capacity and diffusivity of gari, ranged from 0.27 to 0.31 W m−1 °C−1, 1490.07 to 1511.11 kg m−3, 1827.71 to 1882.61 J kg−1 °C and 9.64 × 10−8 to 1.15 × 10−8 m2 s−1, respectively. Correlation of all the parameters was achieved, and the regression models developed showed good correlation to the published models developed based on measuring techniques. [ABSTRACT FROM AUTHOR]

Energy usage patterns in mosques are significantly affected during the holy month of Ramadan. During Ramadan, mosques face an increase in energy demand due to the extended operation hours, higher occupancy, and other communal activities. Due to this, it is important to predict energy use for effective energy planning and cost management. This study highlights the importance of predicting the energy useage of mosques during Ramadan. Methods included in this study analyzing historical data trends, conducting surveys with worshippers and mosques staff, and modeling and simulation mosques to forecast energy consumption. This study audits a mosque in a typical residential neighborhood in Kuwait where an energy model is built and calibrated using hourly observations. Based on a calibrated energy simulation model, it was predicted that energy consumption during Ramadan will increase by 2.7%. Additionally, the model evaluated Ramadan's energy consumption impact by comparing Ramadan with no-Ramadan scenarios; the results show that Ramadan contributes almost 8% to energy consumption increases. This initial analysis predicts the energy demand during this holy month so mosques can adequately meet this demand while minimizing costs, reducing environmental impact, and promoting sustainable practices. [ABSTRACT FROM AUTHOR]

Efficient management of the built environment is a top priority globally. In light of climate change and funding deficits, and considering the huge stock of buildings and infrastructure, especially in urban centers, the needs and demands induced can be successfully met through effective and efficient maintenance of existing built assets, rather than building new ones. This is the reason behind the continuous refinement and update of operation and maintenance policies and programs in a process of addressing technological advances in the construction industry to render maintenance as the primary approach to achieving the upgrade of the built environment. Yet, a fast transition to modern maintenance management systems requires an understanding by the construction industry of their mode of implementation and of the benefits involved. This paper contributes to this goal by presenting clear and easy to replicate examples of applying Building Information Modeling (BIM) in the context of the condition-based maintenance strategy. First, the dominant maintenance strategies in the construction industry, including preventive, predictive, reliability-centered, corrective, etc. are presented and explored in relation to their efficiency for construction projects. Then BIM technology is applied to showcase the implementation of condition-based maintenance for improving the energy profile of a building. A BIM model of a simple residential building generated in the context of this research is used as a basis for respective energy models addressing three different scenarios of interventions in thermal insulation and door and window frames. The scenarios are evaluated in terms of energy cost reductions and building's useful life prolongation concerning the energy efficiency aspect. The paper presents to practitioners in the construction industry -- mainly designers and operators -- a clear, straightforward, and replicable method of designing BIM-based maintenance for buildings that highlight issues of concern and facilitates understanding of the mode of implementation, thus enhancing in real terms the industry's efficiency in applying technological advancements in every day's practice. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

A comprehensive energy audit of a light rail maintenance facility was performed to assess its energy performance and identify potential scope for improvements. The facility’s energy use intensity (EUI) for 2022 was 404 kWh/m2—more than double the benchmark EUI for maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the load factor was 0.22—significantly lower than the recommended minimum of 0.75 for an efficient building. The energy audit encompassed an in-depth evaluation of the facility’s structural and operational characteristics, comprising HVAC systems, lighting, the building envelope, and energy-intensive machinery. An energy model of the facility was developed to emulate the facility’s energy performance in 2022. Following the energy model’s validation, an analysis was conducted to identify opportunities for improving energy efficiency. Post-implementation of energy efficiency measures for the facility, the projected annual reductions are 1086 MWh of electricity, 5034 GJ of natural gas, utility savings of USD 162,402, and net GHG emissions reductions of 584 metric tons of CO2e. A subsequent 30% reduction in EUI to 283.6 kWh/m2 could be achieved with an 86% improvement in load factor, that is, increasing it from 0.22 to 0.41. This study emphasizes the need for energy audits and modeling for maintenance facilities to reduce Scope 1 and 2 emissions.

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot’s power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot’s energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area.

Summary: Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

Summary: Low-carbon technology adoption is an essential element of energy transitions toward net-zero emissions around the world. To exploit the full potential of low-carbon technologies, households should ideally co-adopt multiple low-carbon technologies. Whereas previous research primarily investigated predictors of single-technology adoption in isolation, here we focus on the co-adoption of multiple low-carbon technologies, including solar photovoltaics, stationary batteries, heat pumps, and electric vehicles, to examine the interconnections between adoption decisions and the potential of certain technologies to serve as “entry points” for the co-adoption of multiple low-carbon technologies. Based on a sample of 1967 homeowners, we identified unique demographic and psychological variables associated with co-adoption. We moreover observed specific co-adoption patterns across time in that the adoption of one technology increased the likelihood of adopting another technology. This effect, however, was primarily driven by co-adoption in close temporal proximity, pointing to opportunities for targeted policies that support technology bundles.

Summary: Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. The results yield critical empirical relations for evaluating capacity fading under specific testing conditions.