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Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle. The measurements were conducted during the summer season, and two models were suggested to calculate the overall efficiency of the PV/T system. The variance between the results obtained from the two models was minimal, below 5%. For the examined panel type, the following average values were derived: electrical efficiency = 12.01%, thermal efficiency = 47.21%, and overall efficiency = 59.23%. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the most powerful strategies to mitigate the impacts of climate change by reducing energy demand, which in turn reduces the emissions of Green House Gases (GHG), as well as reducing the burden on the supply side renewable generation expansion. Because electric motors systems represent such a large share of the overall electricity consumption (over 50%), large savings potential could be made available by the use of energy-efficient motor systems both in new installations and by accelerating the replacement of old inefficient motors. Since electric motors are very reliable, their lifetime is long (according to recent studies it may exceed 20 years) which translates into a very inefficient existing stock despite worldwide policy efforts. This paper analyzes the current efficiency of the installed stock and the causes for its low efficiency, possible policy options to increase its the efficiency, the role of new technologies and improvements possible by targeting the entire motor system at the time of motor replacement. The paper presents an innovative analysis of the estimated impact of increasing the uptake of high-efficiency motors and motor systems; effective policies could translate into 100 TWh/year in the European Union if additional measures, such as addressing oversizing, proper controls (VSDs) and digitisation, are also implemented. If similar measures were adopted globally, the savings triggered could be at least tenfold reaching over 1000 TWh/year. [ABSTRACT FROM AUTHOR]

Purpose: There is a great interest in developing eco-friendly operations as alternative uses of resources in the university campus, making the employment of technologies more sustainable. Practices such as energy management and efficiency initiatives have been encouraged to meet these sustainability goals. The purpose of this paper is to describe and discuss the main features of the GENIIOT project, a system of energy management using Internet of Things applied in a Brazilian university campus to support energy education practices and to promote lasting energy efficiency measures. The focus of the study is directed at the energy consumption of the air-conditioning system, which is responsible for 40% of the total electricity consumption. In addition, this project also scrutinizes processes such as the building occupants' comfort demands, lighting levels and energy waste control. Design/methodology/approach: A case study at the Unicamp campus was conducted to illustrate the design procedures through qualitative description. This study comprises an overview of the physical plant, hardware and software development, energy data monitoring, management process and energy education. Findings: The GENIIOT project enables energy efficiency actions by concerning the use of air-conditioning equipment based on monitored data, different types of monitored rooms, user's behaviors and their feedback. This initiative requires the engagement, awareness and actions from users combined with investment in energy efficiency to achieve an efficient use of this type of equipment. The proposed approach can be applied in the future to similar situations, inside the Unicamp campus and at other university campuses. In addition, this project can contribute to building efficiency analysis by using the hardware structure developed to monitor facilities and carry out evaluations, providing valuable information for strategic initiatives in energy efficiency projects and research and development programs based on practical experience and promoting a discussion about sustainability aspects in the context of the university campus for energy efficiency. Originality/value: The GENIIOT project is aligned with some of the sustainable development goals, among which sustainability and responsible consumption are identified. Considering the sustainability issues, the economic dimension can be evaluated through a cost–benefit analysis of energy efficiency projects with prudent investment, while the social attractiveness of the project is ensured by education and awareness practices for the community that interacts with the system and learns about a more efficient way of using resources in the campus. Furthermore, there is a more conscious use of natural resources by minimizing waste from the use of electricity and reducing carbon dioxide emissions. GENIIOT is part of Sustainable Campus Project, a living laboratory which aims at developing energy-efficiency-related research activities in the university campus to reduce costs in higher and federal institutions of education in Brazil. [ABSTRACT FROM AUTHOR]

This paper proposes reduction in the consumption of active electrical energy (kWh) by 67%, while maintaining the same mechanical work speed (RPM) at variable torque loads of water and similar fluids using the "Fan Affinity Law" in an innovative way in PMSM-type synchronous motors. The work is carried out comparatively between brushless asynchronous motors with starting loop (motor with a short-circuited loop) versus brushed asynchronous motors and PMSM-type synchronous motors without the need to use Variable Frequency Drives (VFD), simplifying the technology (electronics) and saving costs in an innovative way (R+D+i). The case study was developed on a design applied to a centrifugal air extractor/blower with PMSM/IPM type synchronous motor. The study focused on a specific bibliographical review of ecodesign and energy efficiency in refrigeration and ventilation systems, taking into account a couple of personal works and other general ones by various authors. [ABSTRACT FROM AUTHOR]

In hydraulic systems such as those with centrifugal pumps, VVVF converters are widely used to control flow and pressure. The aim of this paper is to show that proper power drive control mode can increase the energy efficiency of the overall system. For the realization of this research, an experimental setup was used, which consists of two separate centrifugal pump-based systems. Each system consists of: centrifugal pump, frequency (VVVF) converter, command table, pressure sensor, flow sensor, manometer and rotameter. Using manual valves, it is possible to achieve higher value of pressure or flow. A comparison of the efficiency of different control types of both converters was performed, and Schneider Electric Altivar ATV630U07N4 and ATV320U06M2C frequency converters were used. Measurements of electricity consumption were performed for several cases of system settings for U/F Standard, U/F Quadratic, U/F Energy Saving and Sensorless Vector Control mode of electric motor control. At the end, the measurement results were presented and as well as comparisons of the efficiency between two converters. [ABSTRACT FROM AUTHOR]

Although DC power is claimed to be more energy-efficient than AC power for EHD drying, experimental studies on this subject are still insufficient. This research compares the efficiency of DC versus AC (60 Hz frequency) power for EHD drying by using 1 × 1 and 2 × 2 multi-pin discharge electrodes. Energy indicators were determined from the measurements of electric power consumption at the corresponding drying rate of the wet tissue paper. The experiments revealed the key benefits of DC drying, such as enhanced drying rate, higher energy efficiency, and lower specific energy consumption. For the comparable drying flux of 0.2 g/(m2 s), the specific energy consumption of DC drying was in the range from 350 J/g (2 × 2) to 750 J/g (1 × 1) as compared with 2000 J/g (2 × 2) to 5000 J/g (1 × 1) for AC. The specific energy consumption of a 2 × 2 discharge electrode was consistently smaller than the 1 × 1 one, indicating the role of emitters spacing in the process efficiency. The specific energy consumption of both electrodes increased with the drying rate. [ABSTRACT FROM AUTHOR]

Sustainable production is of the most serious concerns that affect production systems. In a manufacturing company, efficient energy consumption, which leads to significant environmental benefits, is an important factor that indicates the performance of sustainable production. This paper proposes a three-stage data-driven model to analyze energy consumption in production systems. The first stage develops energy consumption predictors, the second stage extracts production scenarios, and the last stage predicts the energy consumption for each scenario. We implemented the model in a steel manufacturing plant for investigating the electricity consumption (EC) of Electric Arc Furnace (EAF). First, we developed four groups of predictors where Boosted Neural Network achieved the best result in predicting EAF's electricity consumption (RMSE = 587, R-Squared = 0.859, MAPE = 0.073). Second, we extracted eight distinct production scenarios based on different amounts of input materials through a descriptive data-mining algorithm, K-means. Third, the EC of production scenarios was predicted by the best predictor. Feature analysis showed that Direct Reduced Iron(DRI), ladle age, and scrap grade-3 have the most effect on predicting EC. Scenario analysis illustrated that scenarios with a higher share of DRI cause a higher amount of EC. Contrastingly, input materials with more share of high-grade scrap types lead to more efficient EC. [ABSTRACT FROM AUTHOR]

Energy efficiency interventions in Water Supply Systems (WSSs) need a precise evaluation of the available water flow rates for energy recovery interventions; however, flow meters are generally too costly for being installed in all the gravity adduction pipelines of a WSS. This paper presents a methodology for predicting flow rates in gravity adduction pipelines based on the electricity bill consumption. In this study, the predicted average flow rate is 0.0300 m3∗s−1, being 1.64% lower than the real one. A Pelton turbine has been chosen as energy recovery unit for supplying electricity to a pumping station of a preloading tank where the water is treated to make it drinkable. An energy saving of 475.26 (MW∗h)∗year−1 is achieved, which can be also expressed as 88.87 saved Tonnes of Oil Equivalent (TOE) and 204.36 ktCO 2 not released into the atmosphere. The gross economic saving due to the installation of the Pelton turbine is equal to 94.29 k€∗year−1 and it can be further increased up to 116.51 k€∗year−1 if the energy efficiency certificates issued by the Italian Authorities are considered. The Payback Period (PBP) of the intervention corresponds to 3 years, and a Net Present Value (NPV) after twenty years is approximately 1.4 M€. • A methodology to assess the energy recovery in Water Supply Systems is presented. • The methodology is used in gravity adduction pipelines linked to preloading tanks. • Measured flow rates have been used for validating the proposed methodology. • A Pelton turbine was used as energy recovery unit in a Water Supply System. • The achievable energy recovery potential in this study is 475.26 (MW∗h)∗year-1. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

Industry poses one of the biggest challenges in the renewable energy transition. In this paper, fossil fuels in the European industrial sector are replaced by renewable energy using a novel tool, IndustryPLAN, a planning tool for the assessment of national industrial sectors. In a bottom-up approach, each industry sub-sector is addressed with energy efficiency and fossil fuel replacement measures based on best available and innovative technologies, and in a top-down approach, the fuel and electricity consumption per country is analysed and decarbonised. The results indicate that: 1. Known technologies can decarbonise most of the industrial sector; 2. Costs and efficiencies are improved by energy savings and electrification; 3. Limiting bioenergy consumption is a critical challenge, emphasising the key role of energy savings and electrification, and the alternative of using hydrogen or hydrogen-based electrofuels will make the transition more expensive and induce energy losses. A full transition to renewable energy and a decarbonised industry sector may be possible before 2050, however, this requires that all investments are sustainable from 2030 onwards and that grid electricity is fully decarbonised. This paper presents several pathways toward 100% renewable energy supply in the European industrial sector and discusses the implications of the outlined scenarios. • Four tangible 100% renewable energy scenarios for industry in EU27 + UK are established. • 100% renewable energy in industry is feasible before 2050. • Energy efficiency improvements are essential to limit biomass consumption. • Industry energy transition needs to be prioritised in short- and long-term planning. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

Energy consumption of lift systems is one key issue in modern high-rise buildings, drawing property owners' attention and accounting for 6% of total electricity consumption by end-use in Hong Kong. A Benchmarking Energy Performance Indicator (BEPI), , measured in J.kg−1.m−1 and proposed by the first author of this paper for lift installations is mentioned in the Technical Guidelines of the Building Energy Code , as one emerging good engineering practice. BEPI is a fair parameter for comparing energy performance of lifts irrespective of their brand, model, travel, speed, capacity and control. It considers energy performance due to both the motor drive efficiency and traffic control intelligence. However, one main obstacle to make it statutory is the lacking of a generic monitor able to retrieve three sets of real time data (normally unavailable on an existing lift) to compute the BEPI, namely energy consumption, instantaneous car load and traveling distance, and to collect ample data statistically reliable enough to determine an acceptable threshold value. This paper describes the development of a proof-of-concept prototype BEPI monitor by giving full technical details on the rationale and history of development, the hardware components, software framework, and updated numerical results of one implementation inside the headquarters of Electrical & Mechanical Services Department (EMSD) of HKSAR. Experiences in installing and calibrating those external sensors that make the whole system non-disturbing to normal lift operation, as well as the procedures in computing the BEPI, are discussed in greater details. It has been shown that the BEPI is traffic dependent but the weekly average of it is relatively stable, confirming it may be robust enough for benchmarking purpose. The solution described in this paper may help roll out the monitoring of BEPI for all lifts inside and outside Hong Kong. [ABSTRACT FROM AUTHOR]

Energy drives national economies with a positive correlation. Global environmental concerns, growth of the world population, and emerging of developing economies with more need for energy resources, would change the future of energy markets and human lives. To manage energy shortage and energy-related environmental threats, decision-makers have to decide between three potential solutions [1], to develop supply value [2], to suppress energy (all types) demands, or [3] to adopt a hybrid strategy. This paper is aimed to uncover potential saving capacities to fill the supply-demand gap based on efficiency improvements for the case of Iran as a developing economy with increasing energy needs. The methodology has been designed based on energy balance calculations which have been evaluated and validated through 47 expert panels under 9 major themes. Then, policy sets have been determined and to map how defined policies were interacting in the living energy, a system interpretive structural model was performed to determine direct and indirect relationships among policies. As a result, policymakers can prioritize and set a roadmap to approach the desired future. Findings showed for the case of supply-side Iranian energy management [1] maximizing electricity transmission and distribution efficiency [2], upgrading the consumption standard in equipment and processes (in the terms of energy production, conversion, and transmission), and [3] online monitoring of distribution networks are most basic and top priority policies. While for the case of demand-side "targeting energy subsidies" is the most crucial policy to support the Iranian energy efficiency improvement targets. • Iran's potential saving capacities to fill the supply-demand gap based on efficiency improvements are studied. • Policy sets have been proposed through 47 expert panels containing 9 main themes. • An interpretive structural model was performed to map policy relationships and dedicate a macro-level action plan. [ABSTRACT FROM AUTHOR]

• Propose a method for embedding demand flexibility into building benchmarking. • Utilizes emerging building characteristics and time-series energy data streams. • Apply method to case study of 306 buildings in Southern California, USA. • Demonstrates ability to cluster buildings by attributes of demand flexibility. • Can inform decarbonization performance-specific policy targeting for buildings. The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. The interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the regulatory policy revenue decoupling, which separates utilities' revenue from sales fluctuations, and electricity customers' energy demand and efficiency in the U.S. electricity sector. To this end, we use recent Stochastic Frontier Analysis estimation techniques that account for both persistent and transient energy efficiency. The results show a significant negative correlation between decoupling and electricity consumption. However, the implementation year, which serves as a reference for price adjustments, is associated with increasing electricity demand and decreasing transient energy efficiency. Therefore, utilities seem to anticipate the implementation of decoupling, which partially offsets the benefits. • Decoupling true up plans are associated with a reduction in electricity sales. • The reference year for price adjustment is associated with an increase in sales. • Electric utilities seem to anticipate the implementation of revenue decoupling. • We find no evidence that revenue decoupling fosters energy efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country's public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings, which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance. [ABSTRACT FROM AUTHOR]

Energy saving and industrial pollution have become increasingly important issues, therefore the identification and adoption of more energy efficient machines and industrial processes are now industrial priorities, and worthy topics for further development through academic research. Polymeric materials are a major raw material, finding widespread application to a range of current industrial machine components as well as multiple products and packaging found in our daily life. Polymer extrusion serves as a particular example of polymer processing techniques, representative of others in as much as there are analogous intermediate stages in the processing. Processing techniques which require such intermediate stages include the manufacture of blown film, blow moulding, thermo-forming, and injection moulding. Hence, the study of polymer extrusion is a representative paradigm for a wider range of processing techniques. Since polymer processing is an energy intensive process and accounts for a huge share (maybe more than 1/3) of the materials processing sector, any improvement to the process would contribute significantly to global energy savings. This work presents a review of studies, which focus on, or appertain to, the energy consumption of extrusion related polymer processing applications. Typical energy demand and losses during processing are considered, and possible approaches for improving the process energy efficiency while maintaining the required end product quality are considered. Overall, this work provides a detailed discussion about how and where energy is utilised; how, where and why energy losses occur; and sets out approaches for optimising the process energy efficiency. • This provides a critical evaluation on energy usage of extrusion-related polymer processes. • This discusses possible ways of saving energy in extrusion-related polymer processes. • This presents on advanced process monitoring and control (adoption of Industry 4.0) of extrusion-related polymer processes. • This paper looks into process mechanisms of extrusion-related polymer processes. • This paper discusses on computational process simulation and dynamical systems to control. [ABSTRACT FROM AUTHOR]

The calculation method commonly used in the industry takes into consideration mainly fuel consumption (excluding electricity) and the amount of removed moisture which is reflected in the unit MJ·(Mg·%)−1. This is not a scientific approach because the result will change if the basic moisture is different (drying from 21% to 18% will consume less energy than drying from 18% to 15%). This paper aims to compare the energy consumption of two industrial continuous flow grain dryers powered by LPG and hard coal based on a comprehensive approach to energy efficiency calculation enriched with electricity consumption, flow measurements, and control of grain moisture. It could be useful for manufacturers who could use this method to generate more reliable data in their product datasheets, and it could also be legally regulated as an appropriate tool for calculating the energy consumption of agricultural grain dryers. According to the approach presented in this paper, the S428.CS construction powered by LPG gas had an energy consumption that was 6.14% lower than the DT2532 dryer construction, which used hard coal. [ABSTRACT FROM AUTHOR]

The net energy-saving effect of information and communication technologies (ICTs) remains unclear. This is because while ICTs can be energy efficient and contribute towards energy savings, at the same time, they can be associated with a growing energy footprint due to an increase in the quantity of devices used. Therefore, this paper examines the net impacts of disaggregated ICTs, measured by computing equipment, communication equipment, and software, on energy demand. Specifically, the research estimates the energy cost share equations and derives elasticities of energy use with respect to the disaggregated ICTs. Using a panel dataset of 13 countries within 28 industrial sectors for 13 years, the quantile regression with fixed effects, fixed effects ordinary least squares, the generalised method of moments, and the two-stage least squares techniques are employed. The results demonstrate that disaggregated ICTs reduce industrial energy demand, contributing to greater energy efficiency. For example, the average elasticity of total energy demand with respect to communication devices is −0.58 for the 10th percentile and reduces to −0.17 for the 75th percentile. Similarly, the average elasticity of electricity demand with respect to communication devices is −0.82 for the 10th percentile and −0.15 for the 75th percentile. The analysis shows that the results are robust, while also addressing the potential endogeneity issues. These results provide useful insights to policymakers on the role of digitalisation in addressing climate change, contributing to the low carbon transition, and supporting the global environmental objectives of net-zero emissions. • The net energy-saving effect of ICTs remains unclear in the past literature. • Policymakers want to understand the role of digitalisation on net-zero emissions. • Disaggregated ICTs contribute towards reducing industrial energy demand. • Communication devices reduce the demand for all types of energy. • Computing equipment reduces total energy and non-electric energy demand. [ABSTRACT FROM AUTHOR]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

Room air conditioners (RACs) are one of the high energy-consuming home appliances. Developing a smart solution to evaluate and track the efficiency of RACs is essentially useful for residents to make necessary maintenance or replacement decisions. While smart meters are increasingly installed to monitor electricity use, the application of smart meter data to evaluate the RAC efficiency remains inadequate. In this paper, we present a data-driven framework to identify non-inverter window RACs with low energy efficiency by analyzing smart meter data from the university student hall rooms. In this framework, we first applied the extreme gradient boosting (XGBoost) method to predict a RAC's hourly electricity consumption. Then we measured the effect of outdoor temperature on the XGBoost prediction of hourly RAC electricity consumption using the Shapley Additive Explanation method to interpret the RAC's efficiency. We conjectured that the RAC efficiency is normal if the predicted hourly electricity consumption is significantly correlated with the outdoor temperature. In contrast, the RAC efficiency is low if the outdoor temperature changes have little impact on the predicted electricity consumption. Finally, we applied the K-Means clustering algorithm to separate the RACs into the "low efficiency" and "normal efficiency" categories based on each's pattern of outdoor temperature's impact on electricity consumption. Our cross-validation result showed that the XGBoost model can achieve an average R2 score of 0.50 and an average root mean squared error of 0.20 kWh. We used RAC replacement records to validate our framework of interpreting the RAC's efficiency. On average, RACs having low efficiency consumed 25.69% more electricity per hour. Overall, our data-driven framework can contribute to extending the value of smart meters for RAC efficiency evaluation. Meanwhile, the smart meter data-driven framework can be improved, and more validation is needed in the future. • We created a data-driven framework to assess the room air conditioner's efficiency. • An XGBoost model was developed for hourly electricity consumed by air conditioners. • We clustered air conditioners based on Shapley value of outdoor temperature. • Air conditioners with low efficiency were identified and validated from clustering. • Identifying air conditioners with low efficiency can save electricity. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES. [ABSTRACT FROM AUTHOR]

• We estimate the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. • The average value of efficiency is 0.491, and the average rebound effect is 48%. • We find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. • The rebound effect varies across before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China's household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change. [ABSTRACT FROM AUTHOR]

Buildings contribute to about 40% of direct and indirect greenhouse gas emissions and more than a third of energy consumption globally. Sustainable building standards and efficiency standards are two measures to reduce such emissions and energy consumption in buildings. While better standards improve the sustainability of new buildings, the impact of these actions depends on the ability to improve the existing building stock. Understanding the dynamics of adopting efficiency and sustainable building measures is essential for setting policy targets and mechanisms. This paper proposes a system dynamics model for assessing the energy savings potential from adopting sustainable building and energy efficiency measurements in buildings in systems with incomplete and uncertain information, using prospective scenarios. The model combines a dynamic diffusion model with an aggregate model for the building stock. The model is calibrated for estimating energy savings in the residential and commercial building stock of the four main metropolitan areas of Colombia: Bogotá, Medellín, Cali, and Barranquilla. Simulation results show that the maximum energy savings potential is approximately 12% but could be below 8% if rebound effects are considered. Results show that energy savings are limited if there are no policies for existing old buildings. [Display omitted] • We developed a system dynamics model for adoption of sustainable buildings standards. • The model estimates the potential energy savings at the city level. • We used the model for testing different scenarios in four Colombian cities. • Current Colombian standards should be extended to consider an old-buildings policy. • Colombian policies require city-specific incentives and planning. [ABSTRACT FROM AUTHOR]

• A novel sliding/rotating overhangs is presented and evaluated for US residential buildings. • Both energy efficiency and environmental impacts of the dynamic overhangs are evaluated. • PV-integrated control strategies are considered to operate the dynamic overhangs. • The PV-integrated dynamic overhangs can result in net-zero building designs. The paper evaluates the benefits of integrating PV arrays with sliding and rotating overhangs to both generate electricity and reduce heating and cooling thermal loads for housing units in US apartment buildings. Optimal controls to operate the sliding-rotating overhangs are considered to minimize annual net energy demands with and without the integrated PV arrays. A series of sensitivity analyses is performed to assess the impact of design and operating conditions on the energy performance as well as the environmental impacts of the sliding-rotating overhangs. The analysis results clearly indicate that when integrated with PV modules, sliding overhangs can significantly reduce the energy demand for US housing units especially when they are set at the optimal angles specific to the building location to maximize their electricity generation and solar shading effects. Specifically, it is found that sliding overhangs can not only reduce energy demand but also can achieve net-zero energy conditions for US apartment units with large windows and located in mild climates even when monthly adjustments are applied to operate the shading systems. For instance, 9.2% of the total energy use of an apartment housing unit located in Boulder, CO, can be reduced using the sliding-rotating overhang with no-PV. In San Francisco, CA, more than 100% of the apartment housing unit energy demand can be avoided using PV-integrated sliding-rotating overhangs. To minimize design and operation complexities, it is recommended to consider sliding-only overhangs set at the latitude of the location to reduce both heating and cooling demand as well as increase PV array output. For instance, it is found that sliding overhang set at the latitude angle can be sufficient to achieve net-zero status and even a carbon-neutral operation for an apartment housing unit located in San Francisco, CA, even when adjusted on a monthly basis. [ABSTRACT FROM AUTHOR]

The efficient use of water will make a notable contribution to ensure energy efficiency in Turkey to reach its National Energy Efficiency Action Plan goals. From the water for electricity perspective, this is the first study to investigate the effects of water consumption on electricity consumption in Turkey's provinces over the period 2008–2018. We apply the Spatial Durbin and Spatial Autoregressive Models to address the spatial dependence and spillover effects among neighboring provinces. This study's core findings reveal that: (1) A 1% increase in water consumption raises electricity consumption by more than 0.10% on average, which puts pressure on electricity consumption. (2) Water consumption has positive spatial effects. (3) Electricity prices are negatively related to electricity consumption. (4) Urbanization has a reducing effect on electricity consumption supports energy efficiency. Overall, this paper fills the water-energy nexus gap in Turkey. This study's results can provide decision-makers a different perspective on the importance of efficient water use in improving energy efficiency. • The determinants of electricity consumption are estimated for Turkey. • We analyze the spatial effects of water abstraction on electricity consumption. • We use spatial panel data econometric models to control for spatial dependence. • Increase in urbanization and electricity prices drive less electricity consumption. • Joint management of water and electricity is needed to provide energy efficiency. [ABSTRACT FROM AUTHOR]