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.

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.

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.

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.

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.

Introduction: Almost 50% of the European Union’s final energy consumption is used for heating and cooling, 80% of which in buildings. The European Commission recently issued the “Efficiency Energy First Principle,” a formal recommendation to EU countries prioritizing energy efficiency measures over other energy-related investments. Decarbonizing the aging housing stock represents a significant challenge to Southern Europe and the remaining Member States. This exploratory research aims to understand why Portuguese people fail to increase their energy efficiency; it then proposes potential interventions. Several studies have looked into the effect of technology-based and behavior-based strategies (individual, socioeconomic and demographic, as well as contextual factors) regarding residential energy consumption. Few, however, have brought all these factors together in one project as in this case.Methods: We used the integrative COM-B model to investigate three core influences of behavior, namely, capability, opportunity, and motivation in a qualitative analysis of a sample of citizens of one specific Lisbon, Portugal community. The Behavior Change Wheel model was then used to propose interventions that might promote energy-responsible behavior.Results: Our finding suggests that investments in structural strategies, and, above all, in behavioral strategies are needed to achieve efficient residential electricity consumption. Specifically, we found a lack of capability (i.e., people’s physical skills and strength, knowledge, and regulation skills) represented the greatest barrier to energy consumption efficiency. A lack of motivation (involving habits and self-conscious intentions or beliefs) was the least decisive factor in the adoption of efficient energy consumption behaviors.Discussion: We therefore recommend the following interventions: 1) training and enablement addressing residents’ physical capability (primarily the replacement of high consumption equipment); 2) training, restriction, environmental restructuring, and enablement would increase residents’ physical opportunity (arising from poor home insulation and citizens’ lack of financial resources to invest in energy solutions); and 3) education, training, and enablement to change psychological capability (regarding insufficient or confusing energy use information).

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.

The carbon emissions from the building sector are one of the major sources of carbon emissions globally. In order to address global climate change, the Chinese government has proposed the 3,060 dual carbon goals. In this context, the government urgently needs a predictive model for calculating and forecasting the energy consumption and carbon emissions in the construction industry to help formulate decarbonization strategies. The review and analysis of a predictive model for the current total carbon dioxide emissions in the Chinese construction sector can provide a basis for calculating and predicting carbon emissions, as well as for formulating corresponding emission reduction policies. This article analyzes the Carbon emission factor and the methods of obtaining building energy consumption data, which are crucial for predicting carbon emissions. Furthermore, it examines the predictive models for total CO2 emissions in the Chinese construction sector and summarizes their respective advantages and limitations. Finally, it highlights the shortcomings of existing research in terms of carbon emission factors, energy consumption data, and accounting scope, while suggesting future research directions.

High-temperature superconducting (HTS) cable can transmit massive power with little dissipation in a limited corridor, which is an attractive solution for future power transmission. Shenzhen Power Supply energized a 400 m/10 kV/2.5 kA concentric HTS cable system on 28 September 2021 to power Ping’an Financial Centre, a Shenzhen’s landmark building, which was the debut of concentric HTS cable made of YBCO in the urban area of a megacity. For more than 1 year, the HTS cable system has been operating well and will continue to operate for a long time. This paper systematically reviews the system specification, cable and its accessories design, cryogenic cooling system design, type and exploratory test, project construction, pre-commissioning test, and operation of the demonstration cable system. Based on our R&D practice, we identify that cost, long-term reliability, and operational simplicity are the main hinders factors that need to be addressed to advance the large-scale application and we propose a set of solution ideas meanwhile.