The benefits of energy efficiency (EE) improvements in industry include multiple energy benefits (MEBs) that have not been studied extensively. Although MEBs are assumed to result from energy efficiency measures (EEMs), they have not been examined simultaneously with EE, creating a knowledge gap that needs to be addressed. This paper investigates MEBs among industrial small and medium-sized enterprises (SMEs) in different regions in Europe. The study suggests that MEBs are social constructs and argues that EEMs and MEBs belong to the same culture, with interactions occurring at macro, meso, and micro levels. This research examines the influence of regional energy policy contexts on SMEs using a questionnaire distributed across five European regions. The analysis considers regional differences and explores how contextual factors influence the prioritization of various MEB categories. The results show that “increased knowledge about energy use in the company” is the top-ranked MEB among the investigated SMEs. Although all regions have supportive energy policies available, there is a tendency to prioritize certain benefit categories over others. Companies in Groningen, the Netherlands, demonstrate stronger engagement with market, competition, and production-related benefits. SMEs in Hessen, Germany, focus mainly on production-related benefits, while companies in Burgenland, Austria, show engagement in organizational, knowledge, production, and waste-related benefits. Meanwhile, companies in La Rioja, Spain, and South-West Oltenia, Romania, do not appear to be engaged in a particular category of benefits but demonstrate strengths in specific areas and noticeable gaps in others. The findings indicate that MEBs are socially constructed outcomes shaped by interactions between regional contexts and company-level practices. The study highlights the importance of considering MEBs alongside EEMs rather than as separate outcomes. Further research examining meso- and micro-level interactions is suggested to broaden the understanding of how MEBs emerge and develop among industrial SMEs.
Electric energy theft is a major issue for the sustained use of modern smart grids (SGs). It affects the electric system’s overall long-term reliability and affordability. Even though advanced metering infrastructure can gather a large amount of data regarding electric energy utilization, it is quite difficult to address non-technical losses (NTLs). This is because electric energy consumption patterns exhibit high-dimensional characteristics, time-varying features, and are not invariably steady. Adapting the simple machine learning and artificial intelligence approach generally requires a single multidimensional electric energy consumption estimation and topical feature extraction mechanism, a limitation that prevents it from characterizing long-range temporal dependencies and intermittent behavior. Consequently, such procedures tend to have lower detection and elevated rates of false positives. In this regard, in this research, a novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs. The suggested TWiDeCNN model is trained and tested on a real-world dataset of electric energy use collected from working SG environments, which makes it useful in the real world. Experimental results show that the suggested model works better than modern advanced methods that use binary classification metrics. These results show that the model performs well, is stable, and can be used at a large scale. They also show that it could be used in cutting-edge ways to improve energy management and find more electric energy theft. In-depth simulation results on the State Grid Corporation of China (SGCC) dataset demonstrate that TWiDeCNN outperforms the original wide model. In addition, it outperforms deep convolutional neural network (CNN) and other benchmarks in terms of ranking capability and detection performance with MAP@100 of 96.00% and MAP@200 of 93.58% at a 70% split. Moreover, parameter sensitivity analysis shows that proper tuning of parameters α, β, γ, and R would help the model build a deep feature-based, powerful representation and a better adaptation ability. Our proposed model maintains robust performance, demonstrating its stability and effectiveness for electric energy theft detection.
Manufactured sand (M-Sand) is a low-cost granular medium for sensible heat storage and characterising the internal temperature field during charging requires either dense instrumentation or a physics-consistent model. In a previous experimental study, an M-Sand packed-bed thermal energy storage unit was characterised at three flow rates – 1.00, 1.88, and 2.66 LPM–using five internal thermocouples. A physics-informed neural network was trained on this dataset while enforcing the three-dimensional transient heat conduction equation through a physics-constrained loss term. The trained model predicted temperature as a continuous function of space, time, and flow rate. The model achieved R2 = 0.9616, RMSE = 1.493 °C, and MAE = 0.984 °C across all sensor locations and flow rates. Spatiotemporal maps showed the thermal front advanced faster laterally than axially, with an estimated front velocity of 1.8×10−5 m/s in the z-direction, consistent with the thermal diffusivity of M-Sand (α = 1.538×10−7 m2/s). Peak temporal heating rate reached 1.2 °C/min near the tube surface. Spatial temperature gradient maps showed peak gradients of 620 °C/m at the tube legs. Three-dimensional isosurfaces captured the progression from tube-adjacent lobes to volume-filling surfaces. The results confirm that a PINN trained on sparse experimental data from five thermocouples and three flow rates can reconstruct the full three-dimensional thermal behaviour of the M-Sand bed within the training operating functioning as a physics-consistent surrogate model that reduces the need for dense sensor instrumentation in packed-bed Thermal energy storage characterisation.
Electricity demand in Sub-Saharan Africa is rising, and in Nigeria, over 40% lack access to electricity. A photovoltaic-thermal (PV-T) system was simulated in TRNSYS, using shea butter for active cooling of photovoltaic (PV) modules. Its performance was compared with that of conventional paraffin wax, and the effects of system design parameters were examined. Efficiency gains from using shea butter and paraffin wax were 0.18% and 0.03%, respectively, during peak solar hours. Notably, shea butter reduced cell temperatures by 1.75 °C during peak sunlight compared with paraffin wax. Peak electrical power and efficiency reached 130.53 W and 11.35%, versus 109.78 W and 9.10% for conventional PV. The optimal flow rate was identified as 2.0 LPM, with a shea butter layer thickness of 0.04 m to optimise thermal capacity and heat transfer. Environmentally, shea butter is renewable and biodegradable, with a lower carbon footprint of 2.20 kg CO2/kg compared with 3.78 kg CO2/kg for paraffin wax, according to CarbonCloud data. As a bio-based energy storage medium, shea butter not only reduces global warming potential but also supports the SDGs, including affordable energy, climate action, and responsible consumption.
Using a comprehensive analysis, this research intends to assess the performance of an air-to-ground heat exchanger system designed for cooling air compression equipment in oil pipeline operations. Both numerical simulations and experimental investigations are conducted to compare the performance of un-finned tubes with finned tubes of different configurations, including square perforated annular fin tubes, annular fin tubes and circular perforated annular fin tubes. In turn, this would identify optimal configurations for maximum heat transfer. The system uses a 1.5 m long PVC pipe with a 6-inch diameter, buried 3.5 m underground. A relatively stable ground temperature of approximately 30°C at a depth of 3 m throughout the year contributes to the cooling efficiency of the system during summer and heating in winter. The results obtained between July and August 2023 indicate that annular finned tubes can provide superior heat transfer rates in both experiments and simulations compared to un-finned tubes. Furthermore, annular fin tubes demonstrate the highest cooling efficiency. However, they also exhibit the greatest pressure drop among the tested configurations. Overall, this study highlights the effectiveness of the proposed system in delivering cooling under demanding environmental conditions.
This study provides a theoretical and practical assessment of the potential benefits of live data streams, as used in digital twins, relating to a building’s running cost and carbon emissions. This is a significant issue, as there is a rise in the demand of digital twins and smart buildings in industry, and the energy savings that could be achieved should be assessed, as it relates to building automation and function. The study considers theoretical assessments based on existing standards and practical assessments on university buildings in London, United Kingdom and New Haven, Connecticut, United States, and is particularly focused on how data-driven controls can improve the energy efficiency of the buildings considered. Using data from the building’s building management systems and energy monitoring software, operational building data analysis was carried out on buildings in London and New Haven. Informed by the studies of both buildings, an assessment against ISO 52120-1:2021 was carried out for the existing building in New Haven, and a planned new university complex in New Haven. The comparative study between the London and New Haven building found that 29% of noteworthy cases identified for the London building were also observed in New Haven. The most significant energy saving opportunities, such as signs of overcooling and not adhering to scheduled shutdowns, were found to be present in both buildings. From a baseline state of low building automation, three states of increasing Building Automation and Control capability were defined as “must have”, “should have”, and “could have”. The mean expected energy savings for these were 5%, 7%, and 9% respectively. The implications of this study are that energy savings can be achieved in existing and planned education buildings, and that opportunities can be identified using enhanced building automation described in ISO 52120-1:2021. It also highlights the challenges with using ISO 52120 to fully quantify the energy savings due to enhanced automation and live data streams, especially for buildings with laboratory areas, which are not covered by ISO 52120, and which typically make up a much higher proportion of a building’s energy footprint than office areas.
Buildings are an important part of the energy consumption of cities. With recent developments in integrated energy systems in buildings, the need for a smart energy management system (EMS) has significantly increased. In this regard, AI-EMS can help to enhance operational efficiency, occupant comfort, and environmental sustainability in urban areas. However, a comprehensive framework categorizing the tools and algorithms used in buildings and urban EMS is still lacking, which limits the ability to evaluate the effectiveness of these technologies. This paper addresses this gap by analyzing and comparing some of the most widely used AI tools, algorithms, and simulation environments for optimizing building energy systems, offering insights into the applications, strengths, and limitations of each tool. We provide a structured overview of AI control methods and available EMS tools, as well as a comparative analysis of their capabilities for energy management in both individual buildings and district-level systems. We aim to help researchers, policymakers, building designers, and engineers to better understand the available simulation tools for making informed decisions when selecting and using them.
Power quality is a global concern, particularly as electronic devices are increasingly supporting modern economies. This research evaluates and proposes improvements for power quality of the distribution network at Covenant University, Ota, Nigeria, where electrical equipment usage contributes to power quality challenges. Measurements and evaluations were carried out in three stages: first, measuring power quality at five campus powerhouses using a Circutor aR6 power analyzer; second, assessing these measurements with Power Vision software; third, simulating the evaluated network with NEPLAN software. The study was conducted during an active school session, with measurements taken at 500 kVA, 11 kV/415 V/230 V on the outgoing circuits for each transformer. The results were benchmarked against IEEE power quality standards and identified issues such as harmonics, total harmonic distortion (THD), overload, and a lagging power factor. The proposed improvements, derived from NEPLAN simulation, included active harmonic filters to reduce harmonics, a shunt capacitor for power factor correction, and load sharing for managing transformer overloads. Simulation results demonstrated that THD was significantly reduced across all powerhouses: CDS from 7.28% to 0.91%, EIE from 10.52% to 3.54%, CST from 16.03% to 0.58%, the Library from 11.92% to 0.12%, and the Male Hostel from 16.71% to 0.24%. These adjustments enhanced THD within specified limits. Additionally, the shunt capacitor increased the power factor to 0.96 from −0.96. These enhancements are expected to extend equipment life, reduce heat loss, and lower utility costs.
The advent of global climate change and rising incomes, particularly in some developing countries such as Egypt, means that the use of air conditioning is poised for a dramatic increase over the next few decades. Although this anticipated increase appears inevitable, it is often associated with a negative connotation because of the increased energy demands and greenhouse gas emissions associated with expanded air conditioning use. Yet, the benefits of air conditioning are not often described in existing literature in conjunction with its associated negative externalities. For example, higher productivity in commercial buildings, and positive health benefits in all manner of buildings (residential, commercial, and industrial) could potentially offset the greater energy consumption and related disadvantages. A levelized cost of cooling (LCOC) analysis is presented to quantify under what circumstances building air conditioning provides benefits that exceed its costs, and vice versa. The LCOC is calculated for the application of air conditioning to a small office building located in either Phoenix, Arizona, United States, or in Cairo, Egypt. The electrical energy required for cooling is calculated with EnergyPlus software. The results indicate that the benefits of air conditioning far outweigh its disadvantages for the Phoenix location, largely because of the productivity benefits derived from maintaining the interior temperature at a comfortable setting. The results for Egypt are more nuanced, but still indicate the overall benefits of air conditioning in an office environment.
This paper focuses on predicting the total transportation and energy costs (TTEC) for single-family households. A system boundary consisting of grid-powered electricity (GE) and solar-powered electricity (SE) as energy inputs and transportation vehicles that include Gasoline Vehicles (GV) and Electric Vehicles (EV) as transportation methods for energy outputs is studied. A novel three-stage evaluation framework is proposed to predict the TTEC under varying single-family household parameters. In the first stage, an energy balance simulation model is proposed to estimate the TTEC for an individual household. In the second stage, the simulation model is run several times under varying parameters to develop synthetic data that is used as input for the third stage supervised machine learning (SML) models. In the third stage, numerous SML models are trained and tested to determine the best SML model that enables us to predict the TTEC with high accuracy. This best SML model can be used as a substitute for simulation model, thereby reducing the computation burden of running the simulation model for each new single-family household. A case study of single-family households in Central Texas in the US is used as an application of the framework. The results indicate that regression SML models are best in predicting the total costs with an adjusted R-squared of 99.13% and 98.88% on training and testing datasets, respectively. In addition, the parameter analysis of regression SML models suggests that the house size, number of GVs, number of EVs, EV and GV ownership costs, and solar implementation at households are the most important parameters to predict TTEC for single-family households. Counterintuitively, number of residents, GV and EV mileage, solar system size, battery capacity and peak solar hours are not significant parameters that contribute to TTEC prediction.
Net zero energy building (NZEB) is an efficient approach to boost the world’s sustainable development and climate mitigation. This paper provides a comprehensive review of multi-objective optimization (MOO)-based case studies of NZEB design. The critical analysis focuses on the “3Ps” criteria of sustainability, which encompass social, environmental, and economic aspects of protecting the people, planet, and profit. This study identifies the gaps in exploring case studies based on MOO techniques for decision-making regarding NZEB design in India and its requirements. Moreover, it demonstrates various passive design strategies, energy-efficient technologies, and renewable energy, and their impact on occupant comfort, energy model, and the building life cycle. It presents the global needs and requirements of NZEB, and definitions of NZEB in different countries and their policies. This review suggests a four-step methodological framework for NZEB design in India. It provides the potential and reliability of various algorithms, tools, and simulation engines to solve building problems. The framework presented here can aid designers in the decision-making of NZEB design, refurbishment, and renovation. Moreover, this study highlights the future research direction and potential challenges for implementing the proposed framework in India.
With the rapid development and increased demand for renewable energy sources, standalone hybrid generation systems have become an essential energy solution. Power optimization control is thus critical to achieving the efficient operation and stability of this system. The distributed ADMM (alternating direction method of multipliers)-based approach has the full potential to deal with the power optimization problem of standalone hybrid generation systems. This study uses an optimization algorithm with a Gaussian penalty function, ADMM-ρ, to alternately optimize the power reference values of wind, light, and battery-containing power generation subsystems. The local controller regulates the output power of the converter according to this reference value. This ensures that the wind and photovoltaic power generation subsystem work in load-tracking or maximum power-tracking modes so that the optimal operation of hybrid power generation meets the balance of supply and demand while prolonging the service life of the batteries. Simulation experiments show that the distributed ADMM algorithm can reliably address the power optimization challenge of hybrid power generation systems.
The expected increase in green hydrogen demand in the near future necessitates scaling-up the hydrogen production plants with the goal of reducing the hydrogen production costs. Nevertheless, a quick scale-up limits the time to test new designs, optimize operation schedules and build up knowledge for production parameters. The Digital Twin concept applied on a fleet of electrolysers is proposed as a digitization tool to contribute to this scale-up process by providing a comprehensive view of the entire electrolysers fleet as well as constructing the feedback connection to the electrolysers manufacturing process. Such Fleet Digital Twin approach can improve the efficiency and scalability of green hydrogen production using water electrolysis. This paper presents a concept of a Fleet Digital Twin and discusses its architecture requirements and design. By applying the Digital Twin concept at different levels of the system, fleet knowledge services are enabled by leveraging the availability of fleet-wide data. The proposed architecture design provides a solid foundation for future development and implementation of Fleet Digital Twins in industrial applications.
Heat pumps are an important technology for reducing residential building emissions, however their adoption rate in North America is far below what is needed to meet emission reduction targets. This paper uses a representative web-based survey of Canadian homeowners (n = 3,804) to identify and describe characteristic and attitudinal trends of three market segments of Canadian homeowners: Pioneers (heat pump owners), Potential Early Mainstream buyers (homeowners currently willing to purchase a heat pump), and Late Mainstream buyers (homeowners currently unwilling to purchase a heat pump). We find that personal capability, contextual and attitudinal factors are significant determinants of market segments. For example, being younger, more educated and wealthier is positively associated with market segmentation in Canada. A novel finding is that voting and living in rural areas is strongly associated with willingness to install a heat pump. The Atlantic Provinces, Quebec and British Columbia are all more likely than Ontario and Alberta to adopt heat pumps while the Prairies are less likely. This is true even after controlling for personal capability, contextual and attitudinal variables. We find an important role for contextual variables in explaining the geographical distribution of heat pump market segments.
The integration of the digital economy with rural development is of great significance as it plays a pivotal role in mitigating carbon emissions and environmental pollution in agriculture, thereby contributing to the evolution of agriculture in a green and sustainable manner. This study aims to examine the impact and mechanisms of rural digital economy development (RDED) on agricultural eco-efficiency (AEE). Specifically, based on provincial-level panel data from China spanning from 2011 to 2021, we evaluate China’s AEE by employing the super-efficiency slacks-based measure (Super SBM) model, taking into account the positive externality of agricultural carbon sinks. Then we analyze the impact and mechanisms of RDED on AEE using the two-way fixed effects model. The findings indicate that: (1) RDED significantly promotes AEE, and this conclusion remains robust even after being tested by replacing the explained variable, altering the sample interval, and including more control variables; (2) RDED can significantly drive AEE in the midwestern regions of China, but the promotion effect on the eastern region has not been fully demonstrated. Additionally, the promotion effect in southern China is greater than that in northern China; (3) agricultural science and technology investment partially mediates the impact of RDED on AEE. Moreover, agricultural science and technology innovation has a positive moderating effect on the relationship between RDED and AEE. Lastly, this study provides new evidence and policy recommendations for developing countries, such as China, to proactively facilitate the coordinated development of the rural digital economy and agricultural ecology, and attain green and sustainable ecological agriculture.
Introduction: Integrating renewables in the distribution sector is a rapidly growing reality in many countries, amongst which Chile’s stands out with an increasingly diversifiable electricity matrix. However, incorporating RES into the electricity distribution sector is altogether a steep climb at present, and seen by some as a formidable challenge for utilities. Likewise, the introduction of the Smart Grid agenda in Chile is imposing new challenges to electric utilities, mainly from a regulatory and technical viewpoint. In spite of this, big players like ENEL are moving forward decisively to meet this challenge, together with academia experts. Methods: We model a sustainable energy system in the form of a smart microgrid operated by ENEL Chile comprising a hypothetical community we term a Sustainable Block™ representing an average residential building in Santiago. We then run simulations under different operating scenarios. The model takes into account the most recent innovation in the legal regulatory framework that governs the energy market in Chile ―Law 20,571―which allows for benefits to those that generate and consume part or all of their energy needs while connected to the grid. Thus, the community considers the option of consuming green energy from the microgrid with an energy storage unit to supply electricity to the 60-apartment complex of various sizes. Under this scenario, a set of energy homeostasis strategies that comprise the homeostatic control and energy management systems help balance the electricity supply versus demand. Results: The model proposed comprises a set of energy homeostasis management strategies that have been designed in the power control and energy management system to balance supply and demand while optimizing the availability and use of green energy. Thus, the energy homeostasis model optimizes the microgrid supply while injecting excess power to the grid. In this context, the community residents exhibit different consumption profiles, therefore they may willingly participate of the sustainable energy strategy as prosumers, displaying a thriftier consumption, and enjoying a lower electric bill while using more renewable energy. The model’s energy homeostasis control and energy management system, especially designed for electric power systems, seeks to maintain a dynamic balance between supply and demand and is being currently discussed with ENEL Chile as part of the intelligent control options for the introduction of distributed generation systems tied to the grid, in order to complement their electric power distribution services. Discussion: The model being proposed comprises a community of residents that we term a sustainable block™ representing an average residential building in Santiago, Chile, which aims to take advantage of Law 20,571 in Chile that allows independent electric power generators to benefit by selling electricity to the grid and also allows independent consumers (mostly residential) to generate part or all of their energy needs while connected to the grid. The community may consume electricity from the microgrid with energy storage, operated by the local electric company, supplying electricity to the 60-apartment complex of various sizes. In his regard, just like in the human body where the brain, particularly the hypothalamus, is primarily responsible for the regulation of energy homeostasis, by monitoring changes in the body’s energy state through various mechanisms, the role of energy storage as well as the role of prosumers are the key enabling factors of energy homeostasis and their interaction are highlighted in the overall analysis.
With electrohydrodynamics (EHD), we can propel air in a low-energy fashion. EHD airflow, or ionic wind, arises when a high voltage gradient is applied to a set of electrodes. The air ionizes between electrodes via corona discharge and accelerates in an electric field, exchanging momentum with the surrounding air. While the ionization process is energy-efficient, reaching competitive flow rates remains challenging from a high-voltage engineering perspective. To increase EHD-generated flow rates, this study experimentally investigates a novel concept called EHD air amplification. The concept uses ionic wind as bleed flow to induce a more significant bulk flow by the air-amplifying Coanda effect. Due to the complex interactions between EHD and dielectric structures for air amplification, the conceptual EHD air amplifier device is designed stage-wise, starting with a simple emitter-collector electrode configuration. First, regular EHD flow was studied in a 150 × 150 × 500 mm3 channel. Then, a dielectric material was added to determine its influence on the electric field. The impact of a converging nozzle on the EHD-generated airflow was subsequently studied. Lastly, the converged nozzle airflow was used to create a bleed flow on a plate to facilitate air amplification of the surrounding air. We show the proof-of-concept for an EHD air amplification system. After a voltage threshold of 14 kV, amplified airstreams up to an amplification factor of 3 were measured. Maximum airflow rates of about 15 m3 h−1 were obtained shortly before electric breakdown at 22 kV. Compared to regular EHD, we achieved a higher aerodynamic performance for the same electric energy invested. The flow rate to electric power ratio increased to 66% in EHD air amplification compared to regular EHD. The proposed EHD air amplifier operates at atmospheric pressure. It lays the groundwork for further optimization studies to position EHD air amplification as a low-energy, low-maintenance, motor- and noiseless airflow generation technology.
Non-intrusive load monitoring (NILM) is a technique used to monitor energy consumption in buildings without requiring hardware installation on individual appliances. This approach offers a cost-effective and scalable solution to enhance energy efficiency and reduce energy usage. Recent advancements in NILM primarily employ deep-learning algorithms for appliance identification. However, the substantial number of parameters in deep learning models presents challenges in quickly and effectively identifying appliances. An effective technique for appliance identification is analyzing the appliances’ voltage-current (V-I) trajectory signature. This research introduces a novel hashing method that learns compact binary codes to achieve highly efficient appliance V-I trajectory identification. Specifically, this paper uses a profound structure to acquire V-I trajectory image features by acquiring multi-level non-linear transformations. Subsequently, we merge these intermediary traits with high-level visual data from the uppermost layer to carry out the V-I trajectory image retrieval process. These condensed codes are subjected to three distinct standards: minimal loss in quantization, uniformly distributed binary components, and autonomous bits that are not interdependent. As a result, the network easily encodes newly acquired query V-I images for appliance identification by propagating them through the network and quantizing the network’s outputs into binary code representations. Through extensive experiments conducted on the PLAID dataset, we demonstrate the promising performance of our approach compared to state-of-the-art methods.
With the rising global temperatures, developing countries are one of the most adversely affected countries by climate change. Furthermore, changes in lifestyle and unsustainable ways of development have resulted in a shift away from passive strategies in the construction industry, which contribute to excessive energy consumption. This demands immediate action to use passive strategies and one of the most widely used passive strategies is shading devices, which can significantly lower the indoor temperature and give the structure the most efficient energy performance. Shading devices were a dominant identity of traditional architecture in Pakistan; however, it has been evident during the past decade the use of such devices has become obsolete due to modernized solutions. This study aims to examine the performance and effectiveness of shading devices in terms of heat gain and daylight levels in residential areas. A comparative case study methodology has been used. The fixed overhanging shading devices of six residential units in Mansehra City, Khyber Pakhtunkhwa province, Pakistan, have been used. Sun angles are calculated through the SketchUp tool Curic Sun to analyze and determine the performance of overhanging in both summers and winters. This article reveals south shading devices as an essential part of houses built before 2,000 in Mansehra City. Though, houses built after 2,000 do not consider using south shading devices to maximize energy use. This study emphasizes considering the type, design, and use of shading devices according to the building’s orientation to improve building performance and energy efficiency.
