Search Results (906)

Multi-story residential buildings present distinct challenges for demand-side management due to shared infrastructure, diverse occupant behaviors, and complex load profiles. Although demand-side management strategies are well established in industrial sectors, their application in high-density residential communities remains limited. This study proposes a cost-optimized energy management framework for urban multi-story apartment buildings, integrating rooftop solar photovoltaic (PV) generation, shared battery energy storage, and flexible electric vehicle (EV) charging. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. EVs are modeled as flexible common loads under strata ownership, alongside shared facilities such as hot water systems and pool pumps. The optimization framework ensures equitable access to battery storage and prioritizes energy allocation from the most cost-effective source solar, battery, or grid on an hourly basis. Two seasonal scenarios, representing summer (February) and spring (September), are evaluated using location-specific irradiance data from Joondalup, Western Australia. The results demonstrate that flexible EV charging enhances solar utilization, mitigates peak grid demand, and supports fairness in shared energy usage. In the high-solar summer scenario, the total building energy cost was reduced to AUD 29.95/day, while in the spring scenario with lower solar availability, the cost remained moderate at AUD 31.92/day. At the apartment level, energy bills were reduced by approximately 34–38% compared to a grid-only baseline. Additionally, the system achieved solar export revenues of up to AUD 4.19/day. These findings underscore the techno-economic effectiveness of the proposed optimization framework in enabling cost-efficient, low-carbon, and grid-friendly energy management in multi-residential urban settings. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi’an, simulation results show that the model reduces gas supply costs by 3.45% for residential users and 6.82% for non-residential users, increases user welfare by 4.64% and 88.87%, and decreases carbon emissions by 115.18 kg and 2156.8 kg, respectively. Moreover, non-residential users achieve an additional reduction in carbon trading costs of 183.85 CNY. The findings demonstrate the effectiveness of integrating dynamic price signals, user behavior modeling, and carbon constraints into a unified optimization framework, offering decision support for sustainable and flexible natural gas scheduling. Full article

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This paper introduces a decentralized control framework using multi-agent deep reinforcement learning. We assign an independent Soft Actor–Critic (SAC) agent to each building’s battery energy storage system (BESS), enabling it to learn a control policy tailored to local conditions while responding to shared grid signals. Evaluated in a high-fidelity simulation environment of CityLearn using real-world data, our multi-agent system demonstrated a reduction of approximately 50% in both electricity costs and carbon emissions. Crucially, this decentralized approach considerably outperformed all benchmarks, including a rule-based controller, tabular Q-learning, and even a centralized single-agent SAC controller. At the district level, learned policies flatten the net load profile, lowering daily peaks by 16% and ramping by 26%, and improve the load factor. The resulting dispatch patterns are interpretable and consistent with operator objectives such as peak shaving and valley filling. These findings indicate that decentralized reinforcement learning can translate local optimization into system-level benefits and offers a scalable pathway for aggregators and utilities to operationalize the flexibility of residential storage at scale. Full article

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

Centralized heating systems in severe cold regions suffer from widespread load estimation deviations due to architectural heterogeneity and a lack of construction drawings, leading to substantial energy waste. This study proposes a lightweight load calculation method that facilitates efficient calculation of heating loads for heterogeneous building clusters via spatial thermal pattern decoupling and matrix reorganization. First, a 3 × 3 load characteristic matrix is developed to characterize the spatial variation in thermal demand across different building positions (corner vs. intermediate units × top, middle, and bottom floors), revealing that corner units exhibit higher thermal loads than intermediate units, while top and bottom floors show significantly higher loads than middle floors. Second, two complementary matrices are established: the load characteristic matrix, which represents the building’s thermal behavior, and the structural feature matrix, which encodes the architectural configuration in terms of unit count (a) and floor count (b). Together, they enable rapid hourly load synthesis using only lightweight input parameters. The method is validated on 56 heterogeneous residential buildings in Northeast China. Using a decoupled 4U/6F standard model, the synthesized cluster heating load achieves an R² of 0.88, an RMSE of 24.15 GJ, a MAPE of 4.94%, and a Mean Percentage Error (MPE) of −0.82% against actual heating supply data, demonstrating high accuracy and negligible systematic bias—particularly during cold waves. This approach allows the seasonal variation in heat demand across an entire residential area to be estimated even in the absence of detailed construction drawings, offering practical guidance for operational heating management. Full article

As the world’s largest anthropogenic emitter of black carbon (BC), China exhibits substantial regional disparities in emissions. This study integrates provincial data into an endogenized multi-regional input–output (MRIO) framework and applies structural path analysis (SPA) to trace embodied BC emissions across 30 Chinese regions throughout the full economic cycle. The results indicate that Southern China is the region with the highest emissions (191.85 Kt), while the northwest region, despite having the lowest absolute emissions, exhibits the highest emission intensity (9.59 kg per 10⁵ CNY). Only 8.94% and 15.66% of the BC emissions linked to Shanghai and Beijing were produced locally, compared to 79.23% for Shandong and 79.21% for Hebei. Most BC emissions in the supply chain originate from direct emissions by the residential sector, followed by indirect emissions from carbon-intensive industries such as construction. This pattern reflects a mechanism whereby final demand in developed provinces stimulates economic output in less developed provinces, thereby driving BC emissions there. These findings highlight the need for differentiated regional mitigation strategies—such as residential clean energy transitions in underdeveloped regions and sustainable supply chain management in developed ones—to advance national sustainability goals. Full article

In recent decades, China has witnessed remarkable growth in housing construction, yet housing-related complaints have not declined significantly, highlighting the gap between housing quality and public expectations. Against this background, this study analyzes 32,277 national surveys to unpack residential satisfaction with green-livable communities in China. Entropy and standard-deviation weighting identified 16 priority indicators; artificial neural networks revealed weak direct influence of basic demographics on satisfaction, highlighting non-linear demand patterns. While 65–75% of respondents are satisfied with most attributes, significant city-level gaps persist—Beijing peaks near 90%, Chongqing falls below 50%. Dissatisfaction converges on three domains: infrastructure (parking, barrier-free access), building performance (leakage, noise, thermal defects) and smart systems (security, energy, health monitoring). Residents’ improvement priorities have shifted from basic shelter to health safety, smart technology, humanistic care and ecological amenities. A “basic-security + quality-upgrade” strategy is proposed: short-term repairs of common defects, medium-term smart-sustainable upgrades and long-term participatory governance. The findings not only enrich the theoretical framework of community satisfaction research but also provide practical guidance for enhancing community quality and meeting residents’ expectations in the context of China’s rapid urbanization and housing development. Full article

Underground hydrogen storage in aquifers is a promising solution to address the imbalance between energy supply and demand, yet its practical implementation requires optimized strategies to ensure high efficiency and economic viability. To improve the storage and production efficiency of hydrogen, it is essential to select the appropriate cushion gas and to study the influence of reservoir and process parameters. Based on the conceptual model of aquifer with single-well injection and production, three potential cushion gas (carbon dioxide, nitrogen and methane) were studied, and the changes in hydrogen recovery for each cushion gas were compared. The effects of temperature, initial pressure, porosity, horizontal permeability, vertical to horizontal permeability ratio, permeability gradient, hydrogen injection rate and hydrogen production rate on the purity of recovered hydrogen were investigated. Additionally, the impact of different well pattern on the purity of recovered hydrogen was studied. The results indicate that methane is the most effective cushion gas for improving hydrogen recovery in UHS. Different well patterns have significant impacts on the purity of recovered hydrogen. The mole fractions of methane in the produced gas for the single-well, line-drive pattern and five-spot pattern were 16.8%, 5%, and 3.05%, respectively. Considering the economic constraints, the five-spot well pattern is most suitable for hydrogen storage in aquifers. Reverse rhythm reservoirs with smaller permeability differences should be chosen to achieve relatively high hydrogen recovery and purity of recovered hydrogen. An increase in hydrogen production rate leads to a significant decrease in the purity of the recovered hydrogen. In contrast, hydrogen injection rate has only a minor effect. These findings provide actionable guidance for the selection of cushion gas, site selection, and operational design of aquifer-based hydrogen storage systems, contributing to the large-scale seasonal storage of hydrogen and the balance of energy supply and demand. Full article

Agriculture and forestry remain vital sources of rural employment; yet, both sectors face challenges of low labour productivity, demographic change, and structural inefficiencies. Modernisation improves productivity but often reduces labour demand, creating a policy dilemma between innovation and job preservation. Therefore, this study aims to quantify labour input across different land use types and farm sizes in agriculture and forestry. Latvia was used as a case region representing a sparsely populated territory suitable for both agricultural activities and forestry. This study develops a multi-stage framework to quantify labour inputs across agricultural and forestry land uses. The research findings suggest that labour use intensity decreases as farm size increases; however, it exhibits greater variation across agricultural production types. Perennial plantations, vegetable and potato cultivation, and dairy farming show the highest labour demands, whereas energy crops and grass-based systems require the least. In forestry, establishment and tending dominate labour needs, while mechanised harvesting reduces input requirements. These findings highlight the strategic role of labour-intensive, high-value activities in sustaining rural employment and the need for targeted rural development policies that recognise this pattern, supporting employment in rural areas without discouraging improvements in labour productivity. Full article

The global transition toward carbon neutrality has accelerated the adoption of electric vehicles (EVs), prompting the need for smarter infrastructure planning in urban environments. This study presents a novel framework that integrates machine learning–based EV adoption forecasting with multi-objective optimization (MOO) using the NSGA-II algorithm. The forecasting component leverages neural networks to predict the percentage of EV sales relative to total vehicle sales, which is then used to derive infrastructure demand, energy consumption, and traffic congestion. These derived forecasts inform the optimization model, which balances conflicting objectives—namely infrastructure costs, energy usage, and traffic congestion—to support data-driven decision-making for smart city planners. A comprehensive dataset covering EV metrics from 2011 to 2024 is used to validate the framework. Experimental results demonstrate strong predictive performance for EV adoption, while downstream derivations highlight expected patterns in infrastructure cost and energy usage, and greater variability in traffic congestion. The NSGA-II algorithm successfully identifies Pareto-optimal trade-offs, offering urban planners flexible strategies to align infrastructure development with sustainability goals. This research underscores the benefits of integrating adoption forecasting with optimization in dynamic, real-world planning contexts. These results can significantly inform future smart city planning and optimization of EV infrastructure deployment in rapidly urbanizing regions. Full article

Bridges, as critical transportation infrastructure, are highly vulnerable to aerodynamic forces, particularly vortex-induced vibrations (VIV), which severely compromise their structural integrity and operational safety. These low-frequency, high-amplitude vibrations are a primary challenge to serviceability and fatigue life. Ensuring the resilience of these structures demands advanced understanding and robust mitigation strategies. This paper comprehensively addresses the multifaceted challenges of bridge aerodynamics, presenting an in-depth analysis of contemporary testing methodologies and innovative solutions. We critically examine traditional wind tunnel modeling, elucidating its advantages and inherent limitations, such as scale effects, Reynolds number dependence, and boundary interference, which can lead to inaccurate predictions of aerodynamic forces and vibration amplitudes. This scale discrepancy is critical, as demonstrated by peak pressure coefficients being underestimated by up to 64% in smaller-scale wind tunnel environments compared to high-Reynolds-number open-jet testing. To overcome these challenges, the paper details the efficacy of open-jet testing at facilities like the Windstorm Impact, Science, and Engineering (WISE) Laboratory, demonstrating its superior capability in replicating realistic atmospheric boundary layer flow conditions and enabling larger-scale, high-Reynolds-number testing for more accurate insights into bridge behavior under dynamic wind loads. Furthermore, we explore the design principles and applications of various aerodynamic mitigation devices, including handrails, windshields, guide vanes, and spoilers, which are essential for altering airflow patterns and suppressing vortex-induced vibrations. The paper critically investigates the innovative integration of green energy solutions, specifically solar panels, with bridge structures. This study presents the application of solar panel arrangements to provide both renewable energy production and verifiable aerodynamic mitigation. This strategic incorporation is shown not only to harness renewable energy but also to actively improve aerodynamic performance and mitigate wind-induced vibrations, thereby fostering both bridge safety and sustainable infrastructure development. Unlike previous studies focusing primarily on wind loads on PV arrays, this work demonstrates how the specific geometric integration of solar panels can serve as an active aerodynamic mitigation device for bridge decks. This dual functionality—harnessing renewable energy while simultaneously serving as a passive geometric countermeasure to vortex-induced vibrations—marks a novel advancement over single-purpose mitigation technologies. Through this interdisciplinary approach, the paper seeks to advance bridge engineering towards more resilient, efficient, and environmentally responsible solutions. Full article

The rising penetration of electric vehicles is driving huge demand for lithium batteries, making low-carbon manufacturing a critical objective. This goal is challenged by insufficient production scheduling flexibility and the neglect of carbon-reduction technologies. To address these challenges, this paper develops a low-carbon planning methodology for lithium battery plant energy systems by leveraging manufacturing chain flexibilities. First, a lithium battery energy–carbon material modeling approach is developed that accounts for process production delays and intermediate product storage to capture schedulable process energy consumption patterns. A nitrogen–oxygen coupling production framework is introduced to facilitate oxygen-enriched combustion technology application, while energy recovery pathways are incorporated given the high energy consumption of the formation stage. Subsequently, a process scheduling-driven planning model for lithium battery industrial integrated energy systems (IIES) is developed. Finally, the planning model is validated through four contrasting case studies and systematically evaluated using multi-criteria decision analysis (MCDA). The results demonstrate three principal conclusions: (1) incorporating process scheduling effectively enhances process energy flexibility and reduces total system costs by 19.4%, with MCDA closeness coefficient improving from 0.257 to 0.665; (2) oxygen-enriched combustion increases maximum combustion and carbon capture (CCS) rates from 90% to 95%, reducing carbon tax to 40.5% of the baseline; (3) energy recovery on the basis of process scheduling further reduces costs and carbon emissions, with battery recovery achieving an additional 30.2% cost reduction compared to 24.1% for heat recovery, and MCDA identifies this integrated approach as the optimal solution with a closeness coefficient of 0.919. Full article

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools. Full article

As a core clean energy source in the global energy transition, natural gas price fluctuations directly affect the energy market supply demand balance, industrial chain cost control, etc. Thus, accurate natural gas price prediction is crucial for market participants’ decision making and policymakers’ regulation. To tackle the issue that traditional single models fail to capture data patterns of the New York Mercantile Exchange (NYMEX) natural gas futures daily prices—due to their nonlinearity, high volatility, and multi-scale features—this study proposes a hybrid model: VMD-CNN-BiLSTM-attention, integrating Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and an attention mechanism. A one-step to four-step forecasting comparison was conducted using NYMEX natural gas futures daily closing prices, with the proposed model vs. CNN-BiLSTM-Attention and Autoregressive Integrated Moving Average (ARIMA) models. The empirical results show that the VMD-CNN-BiLSTM-attention model outperforms the comparison models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), etc. Specifically, its four-step forecast MAPE stays ≤3.5% and $R^2$ ≥ 98%, demonstrating a stronger ability to capture complex price fluctuations, better accuracy, and stability than traditional single models and deep learning models without VMD, and provides reliable technical support for short-to-medium-term natural gas price prediction. Full article