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With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Despite its high share in national energy demand, the tertiary sector is an undifferentiated residual in the energy balances of most countries. To develop effective policy measures for enhancing energy efficiency, policymakers need a sound understanding of how the energy demand is structured. To date, only a few studies assess energy demand in the tertiary sector quantitatively. This paper aims to close this gap by introducing an integrated model that transparently breaks down the energy demand according to statistical subsectors, energy carriers and end-uses. The model combines a technology-based bottom-up with a top-down approach and applies it to a case study on Germany based on survey data from companies. Model validity is analyzed using a set of indicators. The results for Germany show that the model is consistent with the national energy balance showing an aggregate deviation of only 1%. The results for 2019 indicate that electricity demand in Germany's tertiary sector is dominated by lighting (35%), information and communication technologies (33%) and the provision of mechanical energy (9%), while fuel demand is mainly due to space heating (78%), followed by mechanical energy (15%). [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

Energy poverty remains a significant challenge in developing nations, as exemplified by the situation in Indonesia. The connection between energy poverty and insufficient financial resources to fulfill basic energy requirements is significant. Remittances hold the potential to address energy poverty. This study seeks to explore the contribution of remittances to alleviating multidimensional energy poverty among households in Indonesia. Data for this research are sourced from the Indonesian Family Life Survey (IFLS) from 2007 to 2014. This study uses traditional migrant-sending districts as an instrumental variable to address endogeneity concerns arising from the reverse causality between remittances and energy poverty. The findings indicate that remittances from migrant workers can help alleviate multidimensional energy poverty in Indonesia, as demonstrated by the two-stage least-squares (2SLS) methodology. Households receiving assistance use the funds to increase their energy consumption and alleviate energy deprivation by directing resources toward electricity, appliances, and communication services. Additionally, the study highlights that economic disparities play a role in shaping the relationship between remittances and energy poverty. [ABSTRACT FROM AUTHOR]

The transformation towards cleaner energy consumption in Togo is progressing at a slow pace due to a combination of unfavourable socioeconomic, demographic, and spatial factors that favour traditional fuel use over clean and efficient energy. This study tries to quantify the inequality in modern energy access by applying the inequality of opportunity framework. We use a parametric approach to the 2015 Togolese Living Standard Survey; our results show that, on average, 40.76% of inequalities in household modern energy access and consumption in Togo are due to unequal circumstances beyond their control. A Shapley-value decomposition shows that inequalities of opportunity are greater for older persons and women than for youths and men, respectively. Moreover, these inequalities are more pronounced in rural areas and poor regions, especially the Savanna and Kara regions. Consequently, policies aimed at reducing inequality of opportunity in modern energy access and consumption in Togo should emphasise demographic factors, such as gender, age composition, and geographic location of households. [ABSTRACT FROM AUTHOR]

Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient are calculated and compared. Experiments showed that the optimal number of clusters is 4. This study demonstrates the potential of using a fuzzy C-means clustering algorithm in identifying emerging types of electricity consumption behavior, which can help power system operators and policymakers to make informed decisions and improve energy efficiency. [ABSTRACT FROM AUTHOR]

With the large-scale development and utilization of renewable energy, industrial flexible loads, as a kind of load-side resource with strong regulation ability, provide new opportunities for the research on renewable energy consumption problem in power systems. This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning, aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy, and achieving the safe, stable and economical operation of power grids. Firstly, according to the evaluation index of renewable energy consumption characteristics of line active power, the power grid is divided into several partitions, and the inter-zone tie lines are taken as the optimization objects. Then, on the basis of partitioning, a two-layer active power optimization model considering the power constraints of industrial flexible loads is established. The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day; the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of the minimum operation cost of the partition, so as to reduce the line over-limit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads. Finally, through simulation experiments, it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid. [ABSTRACT FROM AUTHOR]

The economic development of any nation depends on energy production and consumption, thereby, needing more exploration from researchers. The current article thus makes an attempt to examine the role of REW and non-REW energy on the economic development in China. The current article has used renewable energy production (REP), and renewable energy consumption (REC) to measure renewable energy. The current research has used fossil fuel energy consumption, energy use, energy import, and electric power consumption to measure non-renewable energy. The present study has taken secondary data from world development indicators from 1986 to 2020. The article has applied the nonlinear autoregressive distributed lagged model to evaluate the association among the understudy constructs. The findings indicated that REP and REC, fossil fuel energy consumption, energy use, energy import, and electric power consumption are linked with economic development in a positive manner in China context. The study guides the regulators in establishing the regulations related to economic development through both type of energy resources. [ABSTRACT FROM AUTHOR]

Background and aim: Traffic-related ultrafine particle pollution near highways is associated with adverse health. Reducing exposure by use of portable air purifiers in homes is one approach to reducing this risk. However, the reaction of residents to having air purifiers in homes is not well studied. Methods: Within the framework of our randomized crossover trial of air purifiers in homes near a major highway, we collected data about participants' use and reactions to air purifiers using questionnaires at their 30-day and 90-day home visits, recorded electricity consumption using HOBO monitors, and conducted structured interviews with participants. Results: Nearly all 150 participants reported running the air purifiers virtually 24 h every day in both their living room and their bedroom in the prior month. The units' HOBO electricity use, from a subset of 45 participants, supported the participants' responses from the questionnaire. Approximately 80% of participants reported setting their air purifier on the medium setting. Tolerance to air purifier noise increased significantly between the 30-day and 90-day home visits, with approximately two thirds reporting not being bothered at all by the noise. The qualitative interviews in a subset of 26 participants yielded consistent responses to those from the questionnaires. Size of unit, airflow, and energy consumption were additional concerns that emerged during the interviews. Conclusions: Results from the questionnaires, HOBO data, and structured interviews all suggest participants had positive reactions towards the presence of in-home APs, and therefore may be receptive to using air purifiers in their homes on a regular basis. Trial registration: ClinicalTrials.gov, NCT04279249. Registered 09 October 2019. [ABSTRACT FROM AUTHOR]

Objective In the hydrogen purification system, in order to analyze the process energy, the influence of oxygen mole fraction in feedstock on heat release from hydrogen-oxygen reaction and reactor temperature was investigated. Method The energy consumption of the electric heater and the amount of cold consumed by the cooler were further calculated by the method of model calculation to enable the optimization of the process flow. Based on this, a new purification process of heat exchange regeneration gas was proposed, and the effects of oxygen mole fraction and logistics flow on system temperature were analyzed. Results The heating can meet the energy requirements only when the oxygen mole fraction is above 0.13%. Compared with the original process, the new heat exchange regeneration gas purification process can save more than 88% of the cooling capacity, and the operating temperature of the system can be maintained without electric heating. Conclusion The new heat exchange regeneration gas purification process can fully use the heat released by the hydrogen and oxygen reaction to heat the cold flow, save the electricity energy consumption and cooling capacity, and reduce operating costs. [ABSTRACT FROM AUTHOR]

As energy demand and electricity costs continue to rise, consumers are increasingly adopting energy-efficient practices and appliances, underscoring the need for detailed metering options like appliance-level load monitoring. Non-intrusive load monitoring (NILM) is particularly favored for its minimal hardware requirements and enhanced customer experience, especially in residential settings. However, commercial power systems present significant challenges due to greater load diversity and imbalance. To address these challenges, we introduce a novel neural network architecture that combines sequence-to-sequence, WaveNet, and ensembling techniques to identify and classify single-phase and three-phase loads using appliance power signatures in commercial power systems. Our approach, validated over four months, achieved an overall accuracy exceeding 93% for nine devices, including six single-phase and four three-phase loads. The study also highlights the importance of incorporating nonlinear loads, such as two different inverter-type air conditioners, within NILM frameworks to ensure accurate energy monitoring. Additionally, we developed a web-based NILM energy dashboard application that enables users to monitor and evaluate load performance, recognize usage patterns, and receive real-time alerts for potential faults. Our findings demonstrate the significant potential of our approach to enhance energy management and conservation efforts in commercial buildings with diverse and complex load profiles, contributing to more efficient energy use and addressing climate change challenges. [ABSTRACT FROM AUTHOR]

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

The grinding process is an essential part of iron and steel smelting. Due to the changeable ore source and the complex dynamic characteristics of the process, the grinding particle size often cannot meet the requirements, resulting in an increase in process energy and material consumption, as well as reworking. In response to the above problems, the following goal was achieved through this study: a collaborative optimization problem for minimization of the grinding particle size error, process water, amountand electricity consumption has been established, which yields the grinding particle size in real time through the use of a neural network. In order to improve the optimization performance, an enhanced Harris hawks optimization (EHHO) algorithm has been developed. EHHO strengthens the team cooperation between individuals in the algorithm initialization phase and location update phase, as well as introduces an attenuation factor in the search phase to improve the stability and accuracy of the algorithm. The effectiveness of the proposed method is demonstrated through the use of some benchmark functions and an industrial grinding process. The results show that the enhanced Harris hawks optimization algorithm has better optimization accuracy and generalizability; furthermore, it can be effectively applied to the collaborative optimization of grinding process. [ABSTRACT FROM AUTHOR]

The digital economy has played a vital role in changing economic and social development patterns and has had a multifaceted impact on carbon neutrality. This paper strives to contribute to the existing research by analyzing the relationship between the digital economy and electricity consumption intensity and providing a comprehensive understanding of the mechanisms that influence the digital economy and energy consumption, particularly focusing on electricity consumption intensity through the construction of a theoretical model. To fulfill this objective, this study introduces the development level of the digital economy into the production function of representative power enterprise, constructs a partial theoretical equilibrium analysis framework, calculates provincial digital economy development indices for Chinese provinces from 2013–2021 using the CRITIC method, and examines the impact of the digital economy on power consumption intensity. The results show that the digital economy can reduce the intensity of power consumption. A unit standard deviation increase in the digital economy development level reduces power consumption intensity by 0.1008 standard deviations. The study also conducts a mechanism analysis with economic growth as the mediating variable, which shows that the digital economy promotes economic growth and reduces electricity consumption intensity. The moderating effect of the digital economy on power consumption is further examined using local government fiscal pressure as the moderating variable. The results show that the impact of the digital economy on reducing power consumption intensity decreases with the increase of government fiscal pressure. This study not only contributes to practical applications but also enhances our comprehension of the interplay between the digital economy and power consumption. On the one hand, the development of the digital economy can reduce power consumption, thus reducing carbon emissions and achieving low-carbon growth. On the other hand, central governments can promote the development of the digital economy by reducing the fiscal pressure on local governments through transfer payments. [ABSTRACT FROM AUTHOR]

The energy consumption of office buildings has increased sharply since the 21st century with the increasing urbanization coverage. At this stage, the energy consumption of office buildings in China mainly comes from air conditioning and lighting. This paper obtains the regression equation of natural lighting illumination and radiant heat gain under different curtain conditions through experiments and simulations, and gives the theoretical calculation process of curtain regulation of office thermal and light environment and energy consumption. DeST and Ecotect software were used to analyze the thermal and light environment and energy consumption of the office. The results show that setting curtains in the room can effectively improve the indoor thermal light environment, the higher the transmittance of curtains the lower the lighting energy consumption, the higher the number of high temperature hours of natural room temperature and the greater the radiant heat gain. The room with thick curtains has the lowest energy consumption for air conditioning in summer, followed by the room with thin curtains, and the room without curtains has the highest energy consumption for air conditioning. The room without curtains has the lowest energy consumption for artificial lighting, the room with thin curtains has the second lowest, and the room with thick curtains has the highest electricity consumption for artificial lighting. The air conditioning energy consumption accounts for the main building energy consumption, followed by the lighting energy consumption. After setting curtains in the office, the air conditioning energy consumption caused by the solar radiation heat entering the room through the exterior windows is smaller than the lighting energy consumption caused by artificial lighting. Therefore, the installation of internal shading with moderate transmittance and good thermal insulation performance can make the energy saving of lighting and air conditioning energy consumption reach the optimal value. [ABSTRACT FROM AUTHOR]

The global energy crisis has been one of the significant challenges for decades, threatening the global economy, and the health of our environment. The government's efforts to enhance the welfare and lifestyle of citizens have been partially undermined by a significant rise in energy intensity, resulting in increased energy consumption. Over the years, researchers have utilized historical energy consumption data to enhance energy efficiency through various technologies. Innovative smart technologies drive energy efficiency, reducing energy usage throughout all areas of the energy industry, from production and supply to consumption. This creates a balance in all sectors and indicates a decrease in energy demand in all areas of building infrastructure. Green buildings that utilize Internet of Things (IoT) technology employ sensors and software to collect data, optimizing, and enhancing building performance. This includes reducing energy and electricity consumption, improving air quality, and optimizing lighting throughout the day. These buildings can contribute to reaching zero‐energy building targets. It becomes challenging to manage green buildings without using centralized control. Therefore, managing and integrating these buildings with intelligent technologies is vital in achieving environmentally friendly management. This study offers a broad overview of the green building concept and explores the use of the green IoT in enhancing services and conserving energy within green buildings. The article aims to deliver an extensive review of green buildings and their advantages, analyze the technology behind the IoT and its integration with solar panels to lower energy consumption, and ultimately identify the challenges present in this area of research. [ABSTRACT FROM AUTHOR]

We address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030. [ABSTRACT FROM AUTHOR]

Campus buildings often face issues with high energy consumption, low efficiency, and significant carbon emissions, making the creation of a green, low-carbon campus urgent. Utilizing solar photovoltaics on rooftops can provide an effective power solution to address high energy consumption. This study focuses on a university campus, employing the DeST energy consumption simulation software to model the HVAC systems, electrical devices, and hot water loads of five typical buildings. It combines this with calculations of available rooftop areas to assess the potential for rooftop solar photovoltaics. The results confirm varying annual electricity consumption among the different buildings, which directly correlates with building size and operational schedules. Among the five building types, sports facilities and academic buildings have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest, under 20%. The entire university campus has an annual rooftop photovoltaic self-sufficiency rate of 35%, significantly addressing the issue of high energy consumption in university campuses. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism. [ABSTRACT FROM AUTHOR]

Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting. [ABSTRACT FROM AUTHOR]

Low-carbon development of ordinary Portland cement industry is of great significance to China's target "to peak carbon dioxide emissions before 2030 and to achieve carbon neutrality before 2060". Neglecting the regional heterogeneity in China, few studies emphasized the status and developments of energy intensity and CO2 emissions of ordinary Portland cement industry in Guizhou of Southwest China. To bridge this knowledge gap, we present an annual intensity dataset of energy and CO2 emissions at plant-level for Guizhou's ordinary Portland cement industry, which involves the details of clinker rotary kilns, yearly production of clinker and cement products, fuel consumption and electricity consumption, total CO2 emission of cement products, energy intensity indicators of clinker and cement products, utilization ratio of solid-waste in clinker and cement production, and CO2 emission factors of cement products. It is an important supplement and to existing energy intensity and CO2 emissions estimates at plant-level and provincial official emissions inventories that converges all regions of China. [ABSTRACT FROM AUTHOR]

Germany has not yet made significant progress toward achieving SDGs 7 and 13. This challenge can be attributed to the underlying financialization problem in Germany, as well as implementation issues related to energy efficiency, particularly in coal and gas, and renewable energy consumption. Given these circumstances, Germany is facing challenges in reducing greenhouse gas emissions. Addressing this issue may necessitate a policy realignment, which is the primary focus of this study. In this context, our study employs various wavelet-based tools, including wavelet coherence, multivariate wavelet coherence, and wavelet-based causality, to delve into the co-movements, causality, and direction of causality between the load capacity factor (LF) and key factors such as energy efficiency (gas and coal), technological innovation, renewable electricity, and financial development, focusing on Germany during the period from 1990Q1 to 2020Q4. The results from wavelet coherence, corroborated by wavelet cohesion, indicate that in the short and medium term, improvements in coal efficiency, gas efficiency, financial development, and renewable energy are associated with an increase in LF, thereby contributing to ecological quality. Furthermore, the multivariate wavelet coherence results underscore the significant impact of considering or not considering the effect of a third variable in the interrelationship between LF and its determinants, with the effect being more pronounced when the third variable is taken into account. Although this policy framework primarily targets the objectives of SDG 13 and 7, its applicability extends to other EU nations. The primary significance of this study is its proposal of an SDG-focused policy framework. [ABSTRACT FROM AUTHOR]

The use of fossil fuel-based energy systems that provide heat and electricity to a building has adverse environmental impacts. These impacts can be mitigated, to a certain extent, through the incorporation of renewable energy sources (RES). The primary objective of this study was to conduct an environmental assessment of the performance of energy systems in existing public facilities located in Poland. Based on the findings, we proposed and implemented changes to these systems and validated the environmental impact of the RES systems used. SimaPro 8.1 software and the Ecoinvent 3.0 database were employed for the analysis, which entailed an environmental assessment of six public facilities located in Poland. The installation of RES resulted in an average 27% reduction in electricity consumption from the national electricity grid. This reduction was observed to be the least in the hospital and the most in the religious building. This was reflected in the environmental assessment of heating systems. The implementation of RES reduced the environmental impact of the religious building by an average of 20%. Concurrently, the CO2 emissions decreased by 35%, SO2 by 44%, and PM10 by 42%. Significant investments and the installation of advanced RES will not prevent the occurrence of unintentional environmental consequences unless the demand for electricity and thermal energy is reduced. The use of RES in the analyzed buildings and the associated avoided emissions do not entirely offset the negative emissions resulting from the utilization of other (conventional) energy sources in the analyzed energy systems of public buildings. Consequently, the analyzed facilities collectively exert a detrimental impact on the environment. [ABSTRACT FROM AUTHOR]

This work presents an economic analysis of the use of electricity storage in PV installations, based on previously adopted assumptions, i.e., the type and location of the tested facility and comparative variants, divided into the share of the storage in the installation, and the billing system. The work takes into account the share of the energy shield and assumes a consumption limit of 2000 kWh. The cost of building the installation is based on July 2023 prices. The work assumes potential directions of changes in electricity prices, based on which the degree of investment profitability for a given price situation is determined. Depending on the adopted change in the direction of electricity prices, with a low price increase rate, for installations in the new billing system (net-billing), the optimal choice is an installation without energy storage with a power exceeding the actual energy demand. Assuming a high increase in electricity prices, the optimal choice is an installation with energy storage with an installation capacity exceeding the actual demand. For installations billed using the net-metering system, the optimal choice is an installation without storage with an appropriately selected installation power. This article shows how much you can gain after installing a PV installation and not only what costs must be incurred to complete the investment. Profit analysis will enable a more complete assessment of the profitability of investing in PV panels (with or without energy storage). It describes the verification of the profitability of a PV installation for a standard user depending on various types of settlements with the electricity supplier and the lack or installation of an energy-storage facility. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

For the purpose of effectively providing protection and utilization of historical earthen sites, the fully-enclosed exhibition halls with electricity driven air-conditioning have been successfully adopted to provide a suitable preservation air environment. However, its electricity consumption and carbon emissions are considerable due to the continuous operation of environmental control, the energy consumption of air cooling in exhibition halls can reach more than 10 times that of office buildings. To further reduce energy consumption, this current study drew on the concept of NBS (Nature Based Solution) and proposed a low-carbon environmental control system for an enclosed exhibition hall for storage of earthen relics by applying natural energy (solar photovoltaic and earth-air-cooling) and space optimization. Firstly, a numerical model was established to simulate the preservation environment in exhibition halls and the electricity consumption of each energy subsystem. Then the model was applied to optimize the design parameters (e.g. earth-air tunnel and airflow pattern) for the environmental control system. Numerical results showed that the electricity consumption was reduced by 58% by applying earth-air-cooling and solar photovoltaic. After optimization of the exhibition hall height, electricity consumption was approximately reduced by 65%. This research is of great value to mitigate energy consumption problems encountered in earthen relics museums and other similar environments. [ABSTRACT FROM AUTHOR]

This document provides a concise summary of various articles related to energy-saving practices and household energy consumption in Ecuador. The articles explore factors such as gender, education, income, and internet access that influence energy-saving behaviors in households. They also highlight regional disparities in energy-saving practices and recommend strategies such as education initiatives, improved internet access, and citizen participation in social organizations to promote energy conservation. The document emphasizes the need for caution in interpreting the findings and suggests further research on the interrelation between different energy-saving practices and comparisons between different locations. Additionally, it includes a list of references and further reading on related topics. [Extracted from the article]

In recent years, the "power restriction" measures were taken in many localities, industrial electricity consumption was interrupted, mainly in the "two high" (high energy consumption and high pollutant emission) enterprises. Take the "power restriction" event in 2021 as an example, this event affected a wide range of regions and disrupted people's livelihood. It not only refers to the power switch and rationing measures taken by individual regions, but also includes differentiated policies implemented by over 20 provinces to limit power supply and industrial production. This paper investigated in detail and summarized the different requirements of power restriction across the country, through the comparison of annual growth rates of power generation and industrial added value，the comparison results of energy intensity, electricity production, power consumption and the industrial growth rate in each province are analyzed. Industrial enterprises and related industrial chains were most heavily affected by the "power restriction" event，mainly in steel, cement, electrolytic aluminum, industrial silicon, textile printing and dyeing, chemical fiber, chemical and other enterprises with high energy consumption and high pollutants emission. The measures to limit electricity will affect the normal production of enterprises to a certain extent, leading to not only the decrease of production efficiency, but also the increase of energy consumption and pollutant emission per unit product. Energy management measures such as differentiated electricity price have limited effect on the output of "two high" industrial products, and the economic leverage of differentiated electricity price is gradually being diluted by the price rise of end products. This paper analyzed the reasons and explored several key problems that need special attention. then recommended certain countermeasures for its appropriate application in the future. [ABSTRACT FROM AUTHOR]

Building operations account for a large amount of energy use and CO2 emissions, and the morphology of buildings in residential clusters strongly impacts energy efficiency performance. However, little research has focused on the morphology and energy electricity usage of high-rise residential clusters in hot summer and cold winter (HSCW) regions. We investigated 96 residential clusters in Hangzhou, China, and established a corresponding morphology database. Additionally, we obtained annual electricity consumption for 16 of these residential clusters. With this database, we performed optimization of morphological parameters upon energy use intensity (EUI) using a genetic algorithm (GA). Specifically, the cooling, heating, and lighting EUIs of high-rise residential clusters were studied. After implementing the optimized morphological parameters, there was a reduction of up to 7.73% in EUI. According to regression analysis, the average aspect ratio was the most significant factor influencing EUI (r = −0.907), followed by floor area ratio (r = −0.755), average orientation (r = 0.502), and average number of floors (r = −0.453). These results indicate that a higher intensity of land development with a greater floor area ratio, average aspect ratio, and average number of floors can reduce total energy consumption. Additionally, we found that an average building orientation of southwest 15° (with respect to south) is optimal. The findings of this study can assist urban planners and designers in developing more sustainable residential clusters, leading to decreased energy costs and CO2 emissions. [ABSTRACT FROM AUTHOR]

Energy saving and the development of renewable energies already form part of the daily discourse in our society. Climate change and the environmental problems arising from it affect all of us on the planet. In view of the recent energy supply crisis in Europe and in line with the EU's Sustainable Development Goals, it is necessary to accelerate the ecological transition. This means, among many other things, encouraging energy efficiency and energy savings. This research is based on the EURONET 50/50 max project, which applied a methodology with no upfront capital outlay, but based on changing user habits to save energy in educational buildings. It was carried out in twelve educational centres of the Spínola Foundation throughout Spain. Significant reductions in energy and water consumption were achieved. The maximum reduction obtained in water consumption was 29%, 24% in electricity consumption and 33% in heating consumption. The average reduction in consumption obtained was 13% compared to 11% achieved in the 50/50 max project. It was concluded that, on the journey to optimising our energy consumption, working on patterns of usage is fundamental, raising awareness and involving users in order to give rise to permanent processes over time, gradually reversing climate change. [ABSTRACT FROM AUTHOR]

Heat pumps (HPs) have emerged as a key technology for reducing energy use and greenhouse gas emissions. This study evaluates the potential switch to air-to-air HPs (AAHPs) in Toulouse, France, where conventional space heating is split between electric and gas sources. In this context, we find that AAHPs reduce heating energy consumption by 57% to 76%, with electric heating energy consumption decreasing by 6% to 47%, resulting in virtually no local heating-related CO2 emissions. We observe a slight reduction in near-surface air temperature of up to 0.5 °C during cold spells, attributable to a reduction in sensible heat flux, which is unlikely to compromise AAHPs operational efficiency. While Toulouse's heating energy mix facilitates large energy savings, electric energy consumption may increase in cities where gas or other fossil fuel sources prevail. Furthermore, as AAHPs efficiency varies with internal and external conditions, their impact on the electrical grid is more complex than conventional heating systems. The results underscore the importance of matching heating system transitions with sustainable electricity generation to maximize environmental benefits. The study highlights the intricate balance between technological advancements in heating and their broader environmental and policy implications, offering key insights for urban energy policy and sustainability efforts. Switching to air-to-air heat pumps in Toulouse cuts energy use and eliminates CO2 emissions. While efficiency is high, cities transitioning from fossil fuel heating may see increased electricity use, stressing the need for sustainable power sources. [ABSTRACT FROM AUTHOR]

The most common problem faced by modern metallurgical enterprises is the improvement of their energy efficiency, which is based on the management of energy-saving projects. The paper deals with the analysis of the impact of external noise on recurrent diagrams based on short-term time series of daily energy consumption of a metallurgical enterprise. The object of this study is short time series of energy consumption of a metallurgical enterprise. The time series of energy consumption of PJSC «Electrometallurgical Plant «Dniprospetsstal» (Ukraine) for 2018–2021 were used as data. The subject of the study is the method of recurrent diagrams of short time series. In the process of research, methods of short time series analysis based on recurrent analysis were used to study the characteristics of the system state on the example of a metallurgical enterprise. An analysis of the influence of external noise on recurrent diagrams of short-term chaotic time series was carried out using the developed software in the Matlab environment for constructing recurrent diagrams of energy consumption of a metallurgical enterprise. The following tasks were solved in the work: software was developed for constructing recurrent diagrams in the Matlab package with the possibility of analyzing changes in the magnitude of quantitative indicators of recurrent diagrams under the influence of different levels of noise in time series. The obtained results are recommended to be used to characterize the state of the system and analyze the influence of external noise. The practical value of the performed work is determined by the proven usefulness of recurrent analysis for estimating electricity consumption and the improvement of modeling of this process, which will allow increasing the accuracy of forecasting future dynamics verified by empirical data. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world. [ABSTRACT FROM AUTHOR]

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO2 and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition. [ABSTRACT FROM AUTHOR]