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In the European Union, only 1% of the building stock is renovated every year. According to the EU strategy, around 75% of the existing building stock needs to be renovated by 2050. Energy efficiency programs mainly support residential and public building stocks; this article considers military dormitories as a type of unclassified building. It is very important to improve energy efficiency to reduce energy consumption and improve the microclimate in these buildings, since the staff is there 24/7. This paper analyzes the energy consumption and measures the indoor air quality in 13 nonrenovated military dormitories. The personnel in unclassified buildings have limited options for remote work in the case of COVID-19 outbreak. Thus, the retrofitting and maintenance of such buildings must be planned carefully. There is a significant lack of IAQ measurements in unclassified buildings. This study presents a wide analysis of energy consumption, indoor air parameters, and occupant satisfaction. On the basis of real data, four retrofitting scenarios were evaluated in IDA ICE dynamic simulation software. The simulation results showed that, in the case of a deep renovation scenario, the theoretical energy savings could be 77.6–79.3% of the used energy. This paper discusses the solar energy potential of onsite energy production for increasing the efficiency and energy supply resilience of unclassified buildings. The results of this study can be applied to other countries with climate conditions similar to Latvia. [ABSTRACT FROM AUTHOR]

Today's residential electricity management depends heavily on the Internet of Things (IoT). It is still challenging to create practical, affordable smart condition monitoring, protection, and control systems for residential distribution networks. The performance of the developed system is evaluated for a variety of residential electric loads with different energy consumption patterns. In this paper, a photovoltaic tracking system and also a cleaning system will be connected to a smart board. All input data will be gathered and sent over Bluetooth using a working prototype built on an Arduino board. The mobile application is also used to let individual users monitor the system in real-time, alert users about electrical system issues, display their energy use in real time, and provide other essential alerts and warnings. [ABSTRACT FROM AUTHOR]

Since modern times, the increase in shipborne equipment has brought tremendous pressure to the energy supply system. Establishing an accurate and reliable energy consumption model that reflects the dynamic characteristics of the system will provide an essential theoretical reference for energy efficiency optimization. This paper proposes a modeling method that considers both the dynamic characteristics and energy consumption characteristics of the system, based on the power bond-graph theory. Firstly, the transmission principle and energy transfer process of hydraulic and electric helicopter traction devices are analyzed. Then, the energy consumption is analyzed, and the state equation and energy equation of the system are established. Finally, the simulation tests are carried out. The results show that the proposed dynamic modeling method is reasonable and effective and can well reflect the dynamic characteristics and energy consumption characteristics of the system. [ABSTRACT FROM AUTHOR]

Energy efficiency represents a key resource for economic and social development, providing substantial benefits to different stakeholders, ranging from the entities which develop energy efficient measures to everyone in society. In addition to cost savings, multiple benefits can be achieved by supporting a better alignment between energy issues and strategic business priorities: e.g., improved competitiveness, profitability, quality, etc. Thus, energy efficiency can be a strategic advantage, not just a marginal issue, for companies. However, most firms, especially small and medium enterprises (SMEs), face many problems and, in some cases, hostility when trying to effectively implement energy efficiency actions. The most dominant barriers are the access to capital and the lack of awareness (especially in terms of life cycle cost effects). The supply chain viewpoint represents one of the main opportunities for overcoming those barriers and improving energy performance even for weaker companies. Since the current literature on energy efficiency and practical approaches to ensure energy efficiency mainly focus on energy performance on a single-firm basis, this paper aims to provide a systematic review of papers on the integration of energy efficiency in supply chain design and management published in academic journal, thereby defining potential research streams to close the gaps in the literature. A number of literature reviews have been published focusing on specific aspects of sustainable or on green supply chain management; however, to the best of our knowledge, no review has focused on the energy efficiency issue. Firstly, the present paper shows how considering energy consumption in supply chain management can contribute to more energy-efficient processes from a systemic point of view. Then, the review methodology used is defined and the sampled papers are analyzed and categorized based on the different approaches they propose. From these analyses, potential future research streams are outlined. [ABSTRACT FROM AUTHOR]

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building's energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house. [ABSTRACT FROM AUTHOR]

Most of the energy used to operate a cellular network is consumed by a base station (BS), and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC) algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS) in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network. [ABSTRACT FROM AUTHOR]

With the steady growth of energy demands and resource depletion in today's world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal's robustness and accuracy when compared to the baseline methods. [ABSTRACT FROM AUTHOR]

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers. [ABSTRACT FROM AUTHOR]

With the rapid progress of socio-economic development and urbanization in China, a wide variety of literature has focused on the phenomenon of energy-consumption disparity, which in turn could be related to numerous fundamental energy dilemmas that China must deal with now or soon, including energy use inefficiency, regional energy shortage, insufficient use of green energy, etc. However, in most cases, only a tendency scenario is discussed, while identifying which factors are more likely to affect the improvement of energy supply/use has not yet been fully explored. Therefore, this paper attempts to explore differences in energy consumption in specific, household-level aspects. Based on the information provided by Chinese General Social Survey (CGSS2015), the Gini coefficient and the Lorentz asymmetry coefficient are used to measure the difference among various categories of energy type and end use within and between different geographical sub-groups. The findings show that: (1) household energy-consumption behaviors exhibit a complicated effect on the overall level of energy-consumption difference; (2) from the supply side, energy users show the most significant difference in biomass energy consumption, while from the demand side, the contribution of space heating to the difference in total energy consumption is the highest; (3) a great urban–rural difference in energy consumption is generally observed with its difference within rural areas being much greater than in urban areas; (4) the eastern, middle and western regions weight differently in explaining the overall difference of energy consumption. These findings provide meaningful materials and references for policymakers in China to understand the overall situation of individual energy consumption to a great extent, and to locate key points to reform the current energy policy framework. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems. [ABSTRACT FROM AUTHOR]

Vietnam's economy has been growing rapidly in the last 20 years, leading to significant increases in energy consumption as well as in carbon emissions. Most electricity is consumed by loads of industry and construction due to the country's socio-economic development strategy. An energy saving strategy cannot be achieved if the industry factories lack energy consumption data. The installation of energy monitoring systems can help to improve energy efficiency by supplying daily, monthly, and yearly energy consumption reports. Moreover, major energy-consuming enterprises in Vietnam must implement solutions for energy-efficient use as prescribed in the Law on Energy Efficient Use. Therefore, this study aimed to determine the impact of an energy monitoring system as an improvement solution for energy efficiency in a typical major energy-consuming enterprise in Vietnam. The study's results, after six months, show that the total saved electricity after installing the power monitoring system was 191,923 kWh. The company saved approximately 19.584 USD and reduced emission to the environment by 139 tons of CO2. In addition, the return on investment time of power monitoring systems is about 14 months, while the annual energy costs of the factory can be reduced by about 9.62% per year. Therefore, power monitoring systems should be promoted in factories with different scales to control energy wastage in the domestic industry field. [ABSTRACT FROM AUTHOR]

The functioning of Ukrainian national gas sector is directly dependent on the processes of fuel and energy resources consumption and trends in domestic and foreign markets. Nowadays, the majority of approaches and methods are formed with the obligatory use of expert assessment methods, which, in its turn, predetermines relatively subjective judgments and results. In the process of conducting a comprehensive analysis of financial and economic indicators and those reflecting the results of economic activity of gas distribution network operators functioning in the western region of Ukraine, the following approaches have been used in our study with the involvement of: Altman's two-factor model; Altman's five-factor model; Lis's bankruptcy prediction model; Richard Taffler's model; Beaver's coefficient; Tereshchenko's model and Matviychuk's model; however, the existing models for diagnosing bankruptcy of enterprises are characterized by ambiguity; as for example, if Lis's model indicates a low bankruptcy level, then other models prove the opposite situation; domestic diagnostic models need to be improved, as they were developed in the early 2000s and disregard current trends in functioning of enterprises. Since the existing models for diagnosing the bankruptcy of enterprises are characterized by ambiguity, the authors proposed and approbate their own approach to determining the level of competitiveness of gas distribution network operators. A feature of the proposed methodology is taking into account modern trends in the functioning of enterprises, taking into account the peculiarities of the activities of gas distribution network operators, and the market stage. A tangible advantage of this approach is the ability to identify the presence or likelihood of critical events at an early stage. [ABSTRACT FROM AUTHOR]

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved. [ABSTRACT FROM AUTHOR]

Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country's revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity. [ABSTRACT FROM AUTHOR]

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people's activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity--travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity--travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers. [ABSTRACT FROM AUTHOR]

Reduced energy consumption is a key aspect of the green building. Nonetheless, research indicates that there is a performance gap between the predicted and the actual energy performance once buildings are occupied, which implies a cost deviation from the anticipated energy cost performance. However, the cost deviation also might result from lower or higher energy rates than expected. As an appropriate research methodology for existing theory testing, case study research strategy was adopted to empirically examine the earned value management (EVM) approach to measure the actual life cycle cost performance of energy in green buildings. With slight methodological and terminological adaptations, it is found that the EVM approach can be applied to conduct a holistic cost performance measurement of the actual energy consumption in green buildings. The strength of the earned value approach is that it allows for detecting whether the energy cost saving or overrun results from lower or higher energy consumption, or from actual energy rate variations. The earned value approach allows for quantifying each cost variance independently, which is a significant aspect of actual energy cost performance measurement in green buildings. [ABSTRACT FROM AUTHOR]

This article provides the results of the first attempt to substantiate the rates of energy consumption and energy savings during the well-drilling process. It is proposed to accept the electrical energy consumption rate as a planned indicator of consumption of resources in the production of a unit of work of a specified quality. For a comprehensive assessment of the efficiency of use of energy resources during production of similar or substitute work, we determined specific energy consumption (designed, planned, actual), which includes direct consumption of all energy types in the production of a unit of work for basic and auxiliary production and operational needs, including from the consumption volume of mentioned resources and energy supply. Classification of consumption rates of electrical energy according to its action period and size scale has been proposed. The composition and amount of energy consumption rates during well-drilling have been described. The technological, expedition, and group rates of electricity consumption for drilling wells during design, survey, and technological work were determined. Electricity input items in drilling operations have been determined. For the first time, a set of measures to reduce energy costs during drilling is shown. The diagram developed for assessment of well performance allows prediction of technological and expedition rates of electricity consumption. Its creation, knowledge/understanding, implementation, analysis, and assessment by means of telemetry will allow evaluation of energy consumption when simulating drilling operations with varying drilling parameters. It will also provide an opportunity to select optimal technological or expedition rates of electricity consumption, taking account of drilling components at each depth: maximum drilling rate under constant weight on bit or maximum penetration. Based on the diagram, organizational and technical measures are proposed to save energy resources and reduce their consumption. The proposed measures allow saving up to 30–40% on energy costs. [ABSTRACT FROM AUTHOR]

Energy labels are a powerful instrument to influence the electricity consumption of appliances and lighting devices in households. However, the real consumption data depend on a number of different factors, including marketing policies, purchase preferences, technology development, and last but not least behavioural habits. While white appliance consumption trends tend to change over a longer period, the use of entertainment devices changes quickly. A number of different devices (digital versatile disc (DVD) player, decoder, game console, home theater, video recorder) are normally connected to the main television set, and these devices change rapidly, and, at the same time, new behaviors are emerging. There is an increasing gap between, on one hand, the higher consumption of televisions and connected devices and the number of regulations developed for their regulation, and, on the other hand, the lack of knowledge on the real onsite consumption. In order to fill this gap, in 2017, a measurement campaign was promoted and developed in some households in northern Italy. The consumption of 28 main televisions and 14 entertainment systems was measured on a daily basis for at least two weeks. Standby consumptions were measured as well. On the basis of outcomes evaluated, it results that these devices are responsible for 9.3% of total electricity consumption as an average of 5.6% for televisions and 3.7% of the attached devices. Standby consumption is still considerably high (3.6% of the total electricity consumption), especially for satellite decoders. Some interesting correlations were studied highlighting the effect of the introduction of the energy labels or the increasing size of the TV over time. The main results obtained were compared to those of previous monitoring campaigns launched in Italy. [ABSTRACT FROM AUTHOR]