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Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model's forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model's effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models. [ABSTRACT FROM AUTHOR]

The largest influences on a building's cooling and heating load come from heat absorption and loss through building surfaces. Most of the heat gain is absorbed by a structure's roof. Applying insulation to building envelopes is an efficient approach to cut back on heating and cooling energy use as well as the damaging effects of the construction industry on the environment. As a result, it is extremely important to choose the right insulation material and determine the ideal insulation thickness. Eight diverse cities with a range of climatic variables that are spread over several Indian areas (geometric coordinates) are the starting point for this investigation. To overcome the constraints of the various climate typology representing the diverse climatic circumstances present in India, a generic method is proposed, considering the numerous climatic parameters for eight selected Indian locations. The subject of the project is the analysis of a composite insulation layer made from a combination of chosen seven insulating materials, each of which has a unique property. This building roof envelope is examined using ANSYS software and e-Quest building energy modeling. The results of simulations over the course of the whole year show a significant decrease in energy use and CO2 emissions. By altering the thickness of the created envelope, the annual energy need is decreased for each of the seven trials in the range of 30–90%. A decrease in heat flow results in sustainable buildings and a low-carbon future by lowering temperatures by 19 degrees and cutting greenhouse gas emissions by 90% to 96%. [ABSTRACT FROM AUTHOR]

Hotel buildings are currently among the largest energy consumers in the world. Heating, ventilation, and air conditioning are the most energy-intensive building systems, accounting for more than half of total energy consumption. An energy audit is used to predict the weak points of a building's energy use system. Various factors influence building energy consumption, which can be modified to achieve more energy-efficient strategies. In this study, an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year. Energy modeling is conducted by using Autodesk Revit 2025. It was discovered from the results that arranging the lighting schedule based on the ASHRAE Standard 90.1 could save up to 8.22% of energy consumption. And then the results also revealed that changing the glazing of the building into double-layer low-emissivity glass could reduce energy consumption by 14.58%. While the energy consumption of the building could also be decreased to 7.20% by changing the building orientation to the north. Meanwhile, moving the building location to Northern Taiwan could also minimize the energy consumption of the building by 3.23%. The results revealed that the double layer offers better thermal insulation, and low-emissivity glass can lower energy consumption, electricity costs, and CO2 emissions by up to 15.27% annually. While adjusting orientation and location can enhance energy performance, this approach is impractical for existing buildings, but this could be considered for designing new buildings. The results showed the relevancy of energy performance to CO2 emission production and electricity expenses. [ABSTRACT FROM AUTHOR]

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES. [ABSTRACT FROM AUTHOR]

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

This paper examines the optimal integration of renewable energy (RE) sources, energy storage technologies, and linking Indonesia's islands with a high-capacity transmission "super grid", utilizing the PLEXOS 10 R.02 simulation tool to achieve the country's goal of 100% RE by 2060. Through detailed scenario analysis, the research demonstrates that by 2050, Indonesia could be on track to meet this target, with 62% of its energy generated from RE sources. Solar PV could play a dominant role, contributing 363 GW, or 72.3% of the total installed capacity out of over 500 GW. The study highlights that lithium-ion batteries, particularly with 4 h of storage, were identified as the most suitable energy storage option across various scenarios, supporting over 1000 GWh of storage capacity. The introduction of a super grid is shown to reduce the average energy generation cost to around USD 91/MWh from the current USD 98/MWh. These findings underscore the potential of a strategic combination of RE, optimized energy storage, and grid enhancements to significantly lower costs and enhance energy security, offering valuable insights for policymakers and stakeholders for Indonesia's transition to a sustainable energy future. [ABSTRACT FROM AUTHOR]

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

Economic and environmental issues translating into energy costs and pollution within the production environment are increasingly attracting attention. Industrial Energy Efficiency (IEE) is gaining ever-higher importance within production environments. Since cosmetic consumers and companies are becoming increasingly sensitive to sustainability, the cosmetic field is working to reduce the environmental and social impact along the whole supply chain. Furthermore, IEE actions in business processes can lead to several medium- and long-term economic and environmental benefits. This paper is the first work in the literature presenting a real-case application of energy analysis and modeling to achieve better energy performances in a cosmetics production process. Thus, in the body of knowledge, it contributes by providing a real case of good practice to be benchmarked for future IEE interventions in cosmetics manufacturing processes. The work has been conducted by analyzing the production process's energy consumption and developing an energy model of a selected machine (i.e., a turbo-emulsifier). The analysis and modeling performed aimed at assessing the different operational phases of the machine and evaluating the different behaviors of the data. Finally, the results allowed us to propose possible improvements to be applied to the production process to achieve better energy performances. [ABSTRACT FROM AUTHOR]

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error. [ABSTRACT FROM AUTHOR]

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]

Integrating natural ventilation with Earth-to-Air Heat Exchangers (EAHE) is an innovative approach that effectively combines the benefits of both systems to enhance indoor environments while reducing energy consumption and operational costs. Many studies focus on reducing reliance on conventional heating, ventilation, and air conditioning systems by optimizing the use of EAHE and natural ventilation in buildings to improve occupant comfort. However, these studies require actual datasets for systems’ design and optimization. This work aims to provide such a dataset using a deployed EAHE with in-situ measurements. The dataset is available in its original form, allowing users to perform data preprocessing according to their specific needs. Additionally, we offer a processed version of the dataset, which can be used to further investigate the effectiveness of EAHE deployment in buildings and its integration with other passive and active systems, aiming to enhance energy efficiency while maintaining occupant comfort.

The article is dedicated to assessing the energy consumption of a residential building, considering actual natural air exchange rates obtained through experimental measurements. The study investigates the impact of variable air exchange rates on heating loads and overall heating energy consumption. For this purpose, a 3D model of an apartment was developed using DesignBuilder software, and dynamic modeling of energy flows was performed in the EnergyPlus environment. The object of the study was a oneroom apartment in a family dormitory of Igor Sikorsky Kyiv Polytechnic Institute in Kyiv. The calculations were based on field data on carbon dioxide levels, which allowed for the consideration of actual conditions of air infiltration and exfiltration. The research established that the influence of natural air exchange significantly depends on external climatic conditions, such as temperature, wind speed, and direction, which shape the dynamic nature of infiltration and exfiltration. During the modeling, mechanisms of air movement between the apartment's rooms were accounted for, including the changes in air-flow directions depending on wind direction. The study compared the modeling results for different air exchange scenarios: using normative values and actual data, allowing for an evaluation of energy-saving potential. The article presents modeling results alongside normative indicators in accordance with standards such as DBN V.2.2-15:2019, DSTU 9190:2022, EN 15251:2011, and ASHRAE 62.2-2022. The difference in annual heating energy consumption between Ukrainian and international standards reached 18%. The findings demonstrate that under actual air exchange conditions, heating energy savings could amount to 5.4% (3.7 million Gcal) compared to DSTU B EN 15251:2011 standards and 7.7% (5.2 million Gcal) compared to DBN V.2.2-15:2019 standards during the heating season. The article's conclusions confirm the feasibility of considering actual air exchange parameters in the process of building energy modeling to ensure more accurate energy consumption forecasts. [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]

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]

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]

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]

This paper introduces the TulipaProfileFitting.jl package, a tool developed in Julia to generate renewable energy profiles that fit a given capacity factor of full load hours. It addresses the limitations of naive methods in adjusting existing profiles to match improved technology efficiency, particularly in scenarios lacking detailed weather data or technology specifications. By formulating the problem mathematically, the package provides a computationally efficient solution for creating realistic renewable energy profiles based on existing data. It ensures that the adjusted profiles realistically reflect the improvements in technology efficiency, making it an essential tool for energy modelers in analyzing future energy systems.

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.

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.

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]

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

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.

Economic and environmental issues translating into energy costs and pollution within the production environment are increasingly attracting attention. Industrial Energy Efficiency (IEE) is gaining ever-higher importance within production environments. Since cosmetic consumers and companies are becoming increasingly sensitive to sustainability, the cosmetic field is working to reduce the environmental and social impact along the whole supply chain. Furthermore, IEE actions in business processes can lead to several medium- and long-term economic and environmental benefits. This paper is the first work in the literature presenting a real-case application of energy analysis and modeling to achieve better energy performances in a cosmetics production process. Thus, in the body of knowledge, it contributes by providing a real case of good practice to be benchmarked for future IEE interventions in cosmetics manufacturing processes. The work has been conducted by analyzing the production process’s energy consumption and developing an energy model of a selected machine (i.e., a turbo-emulsifier). The analysis and modeling performed aimed at assessing the different operational phases of the machine and evaluating the different behaviors of the data. Finally, the results allowed us to propose possible improvements to be applied to the production process to achieve better energy performances.

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

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: 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: 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.

Cell standby, particularly picocell sleep mode (SM), is a prominent strategy for reducing energy consumption in 5G networks. The emergence of multi-state sleep states necessitates new optimization approaches. This paper proposes a novel energy optimization strategy for 5G heterogeneous networks (HetNets) that leverages macrocell-picocell coordination and machine learning. The proposed strategy focuses on managing the four available picocell sleep states. The picocell manages the first three states using the Q-learning algorithm, an efficient reinforcement learning technique. The associated macrocell based on picocell energy efficiency controls the final, deeper sleep state. This hierarchical approach leverages localized and network-wide control strengths for optimal energy savings. By capitalizing on macrocell-picocell coordination and machine learning, this work presents a promising solution for achieving significant energy reduction in 5G HetNets while maintaining network performance. [ABSTRACT FROM AUTHOR]

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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]