Search Results (4,262)

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Nannochloropsis oceanica (Suda & Miyashita) R. E. Lee holds considerable potential for the production of high-value compounds, including pigments, lipids, and polyunsaturated fatty acids. Sodium acetate, a widely used carbon source in microbial cultivation, is both cost-effective and efficient. Although it has been reported to enhance biomass production in various microalgae, its effects on metabolic pathways differ substantially across species. In this study, we investigated the transcriptional responses of N. oceanica to sodium acetate supplementation using high-throughput mRNA sequencing. Sodium acetate significantly promoted growth but elicited a distinct metabolic reprogramming in contrast to patterns commonly observed in other microalgae. We identified 747 differentially expressed genes (399 upregulated and 348 downregulated), reflecting a substantial transcriptomic shift. Pathways related to lipid metabolism, carbon fixation, and photosynthesis were markedly suppressed. Notably, genes associated with photosynthesis were downregulated by 34–43 fold, suggesting a strategic reallocation of resources away from energy-intensive photosynthetic processes in the presence of an external organic carbon source. In sharp contrast to Chlamydomonas reinhardtii P. A. Dangear and Haematococcus pluvialis (Flotow) Wille, lipid metabolism in N. oceanica was not enhanced under sodium acetate supplementation. Instead, expression of lipid metabolism genes decreased by 5–14 fold, with most fatty acid- and lipase-related genes also downregulated (4–30 fold). Together, these findings reveal that N. oceanica adopts a unique adaptive strategy, channeling acetate-derived carbon primarily into rapid biomass accumulation rather than energy storage or high-value metabolite synthesis. This work provides new insights into the species-specific responses of microalgae to organic carbon sources. Full article

Accurately predicting building heating and cooling loads is crucial for optimizing HVAC systems and enhancing energy efficiency. However, data-driven models often face overfitting issues due to scarce training data, a common challenge for new constructions or under data privacy constraints. Transfer learning (TL) offers a solution, but its effectiveness heavily depends on selecting an appropriate source domain through effective similarity measurement. This study systematically evaluates the performance of 20 prevalent similarity metrics in TL for building heating load forecasting to identify the most robust metrics for mitigating data scarcity. Experiments were conducted on data from 500 buildings, with seven distinct low-data target scenarios established for a single target building. The Relative Error Gap (REG) was employed to assess the efficacy of transfer learning facilitated by each metric. The results demonstrate that distance-based metrics, particularly Euclidean, normalized Euclidean, and Manhattan distances, consistently yielded lower REG values and higher stability across scenarios. In contrast, probabilistic measures such as the Bhattacharyya coefficient and Bray–Curtis similarity exhibited poorer and less stable performance. This research provides a validated guideline for selecting similarity metrics in TL applications for building energy forecasting. Full article

This study presents the complete analysis of a (2 + 1)-dimensional nonlinear wave-type partial differential equation with anisotropic power-law nonlinearities and a general power-law source term, which arises in physical domains such as fluid dynamics, nonlinear acoustics, and wave propagation in elastic media, yet their symmetry properties and exact solution structures remain largely unexplored for arbitrary nonlinearity exponents. To fill this gap, a complete Lie symmetry classification of the equation is performed for arbitrary values of m and n, providing all admissible symmetry generators. These generators are then employed to systematically reduce the PDE to ordinary differential equations, enabling the construction of exact analytical solutions. Traveling wave and soliton solutions are derived using Jacobi elliptic function and sine-cosine methods, revealing rich nonlinear dynamics and wave patterns under anisotropic conditions. Additionally, conservation laws associated with variational symmetries are obtained via Noether’s theorem, yielding invariant physical quantities such as energy-like integrals. The results extend the existing literature by providing, for the first time, a full symmetry classification for arbitrary m and n, new families of soliton and traveling wave solutions in multidimensional settings, and associated conserved quantities. The findings contribute both computationally and theoretically to the study of nonlinear wave phenomena in multidimensional cases, extending the catalog of exact solutions and conserved dynamics of a broad class of nonlinear partial differential equations. Full article

Electrical grids are undergoing a transformation driven by the increasing integration of renewable energy sources on the consumer side. This shift, alongside the electrification of consumption—particularly in areas such as electric mobility—has the potential to significantly reduce CO₂ emissions. However, it is also contributing to a rise in electricity prices due to growing demand and infrastructure costs. Paradoxically, these higher prices serve as a catalyst for further investment in renewable energy technologies by reducing the payback periods of such systems. Recent European legislation has accelerated this transformation by mandating the liberalization of energy markets. This regulatory shift enables the emergence of prosumers—consumers who are also producers of energy—by granting them the right to generate, store, and trade electricity using the existing distribution grid. In this new landscape, photovoltaic systems represent a viable and increasingly attractive investment option for both households and businesses. This study presents an economic evaluation of photovoltaic system investments across different European countries, focusing on key indicators such as payback periods and the impact of local solar irradiation on the resulting electricity price. The analysis provides insight into the varying economic feasibility of distributed solar energy deployment, offering a comparative perspective that supports both policymakers and potential investors in making informed decisions about renewable energy adoption. Full article

This study investigates the public acceptance and willingness to pay (WTP) for nuclear energy in Saudi Arabia, a country seeking to diversify its energy portfolio under Vision 2030. Utilizing a cross-sectional survey of 403 residents, the research employs descriptive statistics, regression analysis, and a SWOT analysis to explore the socio-economic and perceptual drivers of public attitudes. The findings reveal that 82.4% of participants support nuclear energy, with a mean WTP of 38.2% of their monthly electricity bill for its development. Key factors positively influencing acceptance include age and knowledge about nuclear energy, while environmental concerns and being married are associated with lower support. Notably, trust in government oversight (64.8%) and the prospect of a new energy source (62.7%) are major reasons for support, whereas health and environmental risks (74.6%) are the primary concerns for opponents. This study provides crucial, evidence-based insights for policymakers, marking the first Saudi-specific research to jointly examine WTP, acceptance determinants through econometric modeling, and strategic implications via SWOT analysis, highlighting the need for targeted public engagement and transparent communication strategies to address public concerns and ensure the successful and sustainable integration of nuclear energy into Saudi Arabia’s energy mix. Full article

The energy transition is an issue of fundamental importance in the current global context, as an increasing number of countries are committed to searching for minerals and elements essential for the storage, distribution, and supply of energy derived from new renewable and sustainable sources. In some countries, these elements (such as boron, lithium, and strontium) are considered to be critical raw materials (CRMs) because of their limited occurrence within their own borders and are commonly found in minerals and geothermal–formation waters, especially in brackish to brine waters. In the Italian territory, CRM-rich waters have already been identified by previously published studies (i.e., with mean concentrations in the Salsomaggiore Terme of 390 mg/L of boron, 76 mg/L of lithium, and 414 mg/L of strontium); however, their extraction is hampered by several knowledge gaps. In particular, a comprehensive understanding of the origin, accumulation processes, and migration pathways of these CRM-rich waters is still lacking. These factors are closely linked to the geological framework and evolutionary history of each specific area. To address these gaps, we investigated the Salsomaggiore Structure that is located at the northwestern front of the Apennine in Italy by integrating geological data with hydrogeochemical results. We constructed new preliminary distribution maps of the most significant CRMs around the Salsomaggiore Structure, which can be used in the future for the National Mineral Exploration Program drawn up in accordance with the European Critical Raw Materials Act. These maps, combined with the interpretation of seismic reflection profiles calibrated with surface geology and wells, allowed us to establish a close relationship between water geochemistry/CRM contents and the geological evolution of the Salsomaggiore Structure. This structure can be considered representative of the frontal ranges of the Northwestern Apennine and other mountain chains associated with the foreland basin systems. Full article

Against the backdrop of intensified governance of territorial spatial planning, investigating carbon emissions from the perspective of territorial spatial planning for transport-land use integration holds significant academic and practical value. Taking Cangnan County as the case study, this research first dissects the reciprocal feedback mechanism between regional transport and land use at the territorial spatial planning level, while exploring transport-influencing factors. Subsequently, it constructs an integrated reciprocal feedback system for regional transport and land use by integrating accessibility drivers, cost matrices, and neighborhood weights through land use simulation–prediction models and the four-stage transport model. Finally, based on critical land use factors, diverse development scenarios under this integrated system are formulated; carbon emissions from transport and land use under each scenario are quantified; and their interrelationships are analyzed across multiple dimensions to explore the nexus of carbon emissions in transport–land use integration. Results indicate the following: (1) Integrated feedback enhances model accuracy (Kappa: 0.795→0.893; overall accuracy: 0.893→0.915), facilitating more precise land use simulation. (2) The county’s core construction area demonstrates the highest carbon emissions across all scenarios, meriting prioritized attention. (3) As deduced from the analysis of territorial spatial land use patterns, the significantly higher transport carbon emissions under the ecological protection priority scenario, compared to other scenarios, originate from over-concentrated construction land and imbalanced planning of carbon source land. These findings offer insights for regional planning; policy recommendations for Cangnan County include expanding carbon sink land, scientifically planning carbon source land, optimizing transport structures, and promoting new energy vehicles to advance carbon emission reduction and sustainable development. Full article

With the world facing the twin pressures of a warming climate and an ever-increasing amount of waste, it is becoming increasingly clear that we need to rethink the way we generate energy and use materials. Despite growing awareness, our energy systems are still largely dependent on fossil fuels and characterized by a linear ‘take-make-dispose’ model. This leaves us vulnerable to supply disruptions, rising greenhouse gas emissions, and the depletion of critical raw materials. Hydrogen is emerging as a potential carbon-free energy vector that can overcome both challenges if it is produced sustainably from renewable sources. This study reviews hydrogen production from a circular economy perspective, considering industrial, agricultural, and municipal solid waste as a resource rather than a burden. The focus is on the reuse of waste as a catalyst or catalyst support for hydrogen production. Firstly, the role of hydrogen as a new energy carrier is explored along with possible routes of waste valorization in the process of hydrogen production. This is followed by an analysis of where and how catalysts from waste can be utilized within various hydrogen production processes, namely those based on using fossil fuels as a source, biomass as a source, and electrocatalytic applications. Full article

With the implementation of China’s “dual carbon” strategy, the installed capacity of new energy has grown rapidly. Wind power and photovoltaic power have accounted for more than 40%, but the integration of power electronic apparatus into the grid has resulted in the manifestation of a system with “low inertia and weak damping”, which can easily lead to transient overvoltage problems at transmitters when high-voltage direct-current (HVDC) latching faults occur. Although a variety of dynamic reactive power optimization strategies have been proposed in the existing research, most of them are aimed at single equipment, and multi-reactive power source collaborative control schemes are lacking. In this paper, we innovatively establish a transient voltage analysis model for a new energy transmitter, derive the expression of overvoltage amplitude, and propose a method for the construction of a multi-reactive source collaborative optimization model, which can effectively suppress transient overvoltage through capacity and initial output configuration. We provide a new idea for the safe operation of a significant percentage of new energy grids. The case analysis shows that the co-optimization method outlined in this paper is an effective solution to suppress the transient overvoltage triggered by AC faults and has wide application value. Full article

The design of cycloidal reducers requires a detailed knowledge of the intensity and character of load, as well as the maximum von Mises stresses on critical components. In the available literature, the load distribution and the stress–strain state of the cycloidal reducer elements are typically determined based on factors such as cycloidal disc tooth profile modifications, contact deformations, and internal clearances, whereas the influence of thermal stresses is most often neglected. To address this research gap, an innovative numerical–analytical methodology has been developed, which, for the first time, enables the prediction of the distribution of temperature fields and the quantification of the influence of temperature on the contact forces and the stress–strain state of key elements of the cycloidal reducer. Furthermore, the proposed methodology can be adapted for application within a broader context of mechanical engineering. From a practical perspective, it is expected to be beneficial to companies engaged in the design of power transmission gearboxes, as valuable practical guidelines for engineering applications are provided. This study also provides new insights into the dominant sources of heat generation and offers a clearer understanding of how thermal energy is transferred from internal heat sources to the outer surface of the housing. Full article

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals. Full article

Recycled water is commonly considered an environmentally friendly alternative water source for urban water systems, which can not only serve as a solution for water scarcity, but also reduce wastewater discharge from sewage systems. However, owing to the high degree of energy consumption during recycled water production, the utilization of recycled water may be detrimental to greenhouse gas emission reduction. In this work, we conduct a detailed investigation into greenhouse gas emissions from different sources in a typical multisource urban water system in China. Furthermore, we develop an optimization model for water resource allocation based on the rime optimization algorithm and regret theory. The results show that although greenhouse gas emissions from recycled water exceed those from other sources, their impact can be eliminated through rational water resource allocation. Specifically, compared with the original water resource allocation, the optimal results effectively reduce pollutant emissions by 7.6~11.1% without excessively increasing water resource shortages and greenhouse gas emissions. Additionally, both subjective preferences and recycled water utilization conditions have significant impacts on the optimization results, which should be carefully selected according to practical situations and technologies. Overall, the methods developed in this study provide a new general framework for the water resource allocation of multisource urban water systems in the context of greenhouse gas emission reduction and recycled water utilization, which can be employed in other areas. Full article

This study evaluates the trade-offs and environmental impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, with a focus on greenhouse gas emissions and water consumption. It addresses key questions, including how water use and emissions change as the county shifts from coal to natural gas. The research analyzes water usage and emissions of CO₂, NO_x, and SO₂ during both the extraction and combustion phases of coal and natural gas. Specifically, it compares water consumption and direct emissions from coal-fired and natural gas-fired power plants. The analysis utilizes ten years of combustion-phase data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants in New Mexico. Linear regression was applied to the historical data, and four transition scenarios were modeled: (1) 100% coal-generated electricity, (2) a 20% reduction in coal with a corresponding increase in NG, (3) a 50% reduction in coal with a corresponding increase in NG, and (4) a complete transition to NG. Regression analysis and scenario calculations indicate that switching to NG results in significant water savings and reduced emissions. Water savings in the combustion phase decrease by up to 2750 gallons per MWh, valued at USD 0.743 per MWh when electricity is generated 100% from NG. CO₂ emissions are substantially reduced, with the largest decrease being 0.6127 metric tons per MWh, valued at USD 61.26 per MWh. NO_x emissions in the combustion phase decline by 0.0018 metric tons per MWh, with an economic valuation of USD 14.61 per MWh, while SO₂ emissions decrease by 0.0006 metric tons per MWh, valued at USD 11.91 per MWh when electricity generation is 100% NG-based. The results highlight the environmental and economic advantages of transitioning from coal to NG. The findings underscore the environmental and economic advantages of transitioning from coal to natural gas. Water conservation is particularly vital in San Juan County’s semi-arid climate. Additionally, lower emissions support climate change mitigation, enhance air quality, and improve public health. The economic valuation of emissions reductions further highlights the financial benefits of this transition, positioning natural gas as a more sustainable and economically viable energy source for the region. Ultimately, this study emphasizes the need to adopt cleaner energy sources such as renewable energy to achieve long-term environmental sustainability and economic efficiency. Full article

Renewable energy sources play a crucial role in the urgent global pursuit of decarbonizing electricity systems. However, persistent grid congestion and lengthy planning approval processes remain the main barriers to the accelerated deployment of new green energy source capacities. Capitalizing on the synergies afforded by co-locating hybrid power plants—particularly those that harness temporally anti-correlated renewable sources such as wind and solar—behind a unified connection point presents a compelling opportunity. To this end, this paper pioneers a comprehensive planning framework for hybrid configurations, integrating transmission grid and renewable energy assets planning to include energy storage systems, wind, and solar energy capacities within a long-term planning horizon. A mixed-integer linear programming model is developed that considers both the technical and economic aspects of combined grid planning and hybrid power plant allocation. Additionally, the proposed framework incorporates the N − 1 contingency criterion, ensuring system reliability in the face of potential transmission line outages, thereby adding a layer of versatility and resilience to the approach. The model minimizes the net present value of costs, encompassing both capital and operational expenditures as well as curtailment costs. The efficacy of the proposed model is demonstrated through its implementation on the benchmark IEEE 24-bus RTS system, with findings underscoring the pivotal role of hybrid power plants in enabling cost-effective and rapid sustainable energy integration. Full article