Search Results (2,155)

Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for household electricity consumption, grow into common problems across countries. Residential load forecasting can assist utility companies in determining effective electricity pricing structures and demand response operations, thereby improving renewable energy utilization efficiency and reducing the share of thermal power generation. However, due to the randomness and uncertainty of user load data, forecasting residential load remains challenging. According to prior research, the accuracy of residential load forecasting using machine learning and deep learning methods still has room for improvement. This paper proposes an improved load-forecasting model based on a time-localized attention (TLA) mechanism integrated with LSTM, named TLA-LSTM. The model is composed of a full-text regression network, a date-attention network, and a time-point attention network. The full-text regression network consists of a traditional LSTM, while the date-attention and time-point attention networks are based on a local attention model constructed with CNN and LSTM. Experimental results on real-world datasets show that compared to standard LSTM models, the proposed method improves R² by 14.2%, reduces MSE by 15.2%, and decreases RMSE by 8.5%. These enhancements demonstrate the robustness and efficiency of the TLA-LSTM model in load forecasting tasks, effectively addressing the limitations of traditional LSTM models in focusing on specific dates and time-points in user load data. Full article

Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system. Full article

This research studies the current changes in Polish renewable resources employment and Poland’s position in rankings in comparison to European countries. The research aims to define the actual situation and the future perspectives of renewable energy sources (RES) employment in Poland. Several key research problems were formulated: What are the main directions of employment development in the Polish energy sector? How is employment changing in Poland? What are the forecasts for the Polish RES labor market? What are the main tendencies in competencies changing? The research was made by means of literature study and statistical analysis. Particular attention was paid to comparing RES employment in Poland and Germany. The research reveals the high position of Poland in total EU RES employment and the optimistic perspectives for the labor market development, despite the many challenges defined. It was concluded that the Polish education systems need to adapt rapidly to the demand of the energy market and continue to broaden the sustainable and renewable energy perspective to prepare a workforce capable of supporting a greener future. Full article

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals. Full article

This research explores how climate change will affect building energy use across the UK by analysing both a conventional reference building design and a net-zero energy (NZEBs) alternative to assess how each would perform under future weather conditions. Using climate projections from databases like Prometheus and Meteonorm, along with simulation tools like EnergyPlus and Freds4Buildings, the study evaluates the energy performance, costs, and GHG emissions of a case study building under current weather conditions, with 2030, 2050, and 2080 forecasts in three different UK locations: Exeter, Manchester, and Aberdeen. Results indicate that heating demand will decrease consistently over time across all locations by as much as 21% by 2080 while cooling demand will rise sharply. NZEBs proved more resilient to these changes, using less energy and producing fewer GHG emissions than conventional buildings, with 89% reductions in emissions even with increased cooling needs. Accounting for future weather helps both understand the risks of conventional design, with a number of scenarios experiencing overheating in 2080 and ensure NZEBs can meet their goals during their entire lifespan despite the increases in energy needs. The study highlights both the impact of accounting for future weather forecasts during design and the increasing relevance of net-zero energy designs in mitigating the effects of climate change while offering practical insights for architects, policymakers, and energy planners, showing why future weather patterns need to be considered in sustainable building design to ensure buildings will achieve their carbon targets throughout their life. Full article

Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority. Full article

A decrease in group travel and increase in individual and family travel has led to the diversification of travel demand needs in Japan. In Japan, railways and airlines are the main competitors of personal vehicles for mid- and long-distance travel. The use of a personal vehicle can better meet diverse travel needs by offering greater flexibility; moreover, the development of motorization and the improvement of road networks have placed vehicles in a leading position among mode choices for tourism purposes. At present, Level 3 autonomous driving on expressways has become technically feasible; hence, a mode shift from public transportation to automated vehicles is anticipated because of the reduction in driving fatigue and inherent advantage in terms of greater flexibility conferred by autonomous driving. This shift could contribute to more sustainable travel patterns by optimizing route planning and reducing congestion through more efficient vehicle operations. In this study, a survey was conducted on tourism travel to Kagoshima Prefecture. The collected data were used to construct tourist behavior models, including a mid- and long-distance mode choice model that considers driving fatigue and a tourist attraction visit duration model based on a random utility model. The validity of the model is corroborated by statistical tests showing high goodness-of-fit to the observed data. The results of this model forecast a change in the modal share after the introduction of automated vehicles, with a focus on reducing driving fatigue. These predictions can contribute to the development of future transportation policies and the promotion of tourism. Full article

The residential minimum water demand characterisation is of fundamental importance for water distribution system management. During the minimum consumption, indeed, maximum pressures are on network pipes, and at the same time, tank levels rise. The water consumption analysis during the period of low demand and high pressure is thus of great interest for leakage estimation due to the increase in water loss with pressure. In order to contribute to the study of and forecast the daily minimum residential water demand, some probability distributions were tested by means of statistical inferences on a data set collected from different District Metering Areas (DMAs), showing that the stochastic minimum flow demand is defined by the Log-Normal (LN), Gumbel (Gu) and Log-Logistic (LL) distributions, as an extreme minimum value. With reference to the analysed DMAs, the parameters of such statistical distributions were estimated and the relationships are provided as a function only of the supplied users for different DMAs. The data were analysed with 1 h intervals of discretisation, with the aim of providing a useful guide to water utilities, which usually manage water distribution system data with such a resolution time. Indeed, once the minimum residential flow consumption at a 1 h interval was estimated as a function of the user number, by subtracting it to the inflow measured, it is possible to estimate the leakages rate at the DMA. Full article

This study explores the application of generalized conformable derivatives in modeling hotel demand dynamics in Mexico, using the Gompertz-type model. The research focuses on customizing conformable functions to fit the unique characteristics of the Mexican hotel industry, considering the Tourist Area Life Cycle (TALC) model and aiming to enhance forecasting accuracy. The parameter adjustment in all cases was made by designing a convex function, which represents the difference between the theoretical model and real data. Results demonstrate the effectiveness of the generalized conformable derivative approach in predicting hotel demand trends, showcasing its potential for improving decision-making processes in the Mexican hospitality sector. The comparison between the logistic and Gompertz models, in both integer and fractional versions, provides insights into the suitability of these modeling techniques for capturing the dynamics of hotel demand in the studied regions. Full article

The implantation of wind farms in the European territory is being deployed at an accelerated pace. In the proposed framework, the province of La Coruña in the autonomous community of Galicia is tested, with a wide deployment of this type of infrastructure in the territory initiated in the 80s, representing the third autonomous community with the largest exploitation of wind resources, which provides sufficient information, extrapolated to the entire community, to demonstrate the practical usefulness and potential of the method of obtaining the territorial model proposed in this article The regional has been used as the basic administrative subunit of the study variables, considering that the territory thus delimited could have common physical and cultural characteristics. The methodology presented in this article involves the collection and processing of public cartographic data on various factors most repeatedly or agreed upon in the consulted bibliography based on studies by experts in the technical, environmental, and environmental areas, including explanatory variables of risk in a broader context of climate change as the first contribution of this study. Another contribution is the inclusion in the model of the synergistic impact measured as the distance to wind farms in operation (21% of the total area of the sample) to which an area of influence of 4 times the rotor diameter of each of the wind turbines im-planted has been added as a legal and physical restriction. On a solid basis of selection of explanatory variables and with the help of Geographic Information Systems (GIS) and multi-criteria analysis (MCDM), techniques widely documented in the existing literature for the determination of optimal areas for the implementation of this type of infrastructure, a methodological proposal is presented for the development of a strategic, long-term territorial model, for the prioritization of acceptable areas for the implementation of wind farms, including forecasts of increased energy demand due to the effect of climate change and the population dynamics of the study region that may influence energy consumption. This article focuses on the use of multivariate clustering techniques and spatial analysis to identify priority areas for long-term sustainable wind energy projects. With the proposed strategic territorial model, it has been possible to demonstrate that it is not only capable of discriminating between three categories of acceptable areas for the implementation of wind farms, taking into account population and climate change forecasts, but also that it also locates areas that could require conservationist measures to protect new spaces or to recover the soil because they present high levels of risk due to natural or anthropic disasters considered. The results show acceptable areas for wind energy implementation, 23% of the total area of the sample, 3% conservation as ecological spaces to be preserved, and 7% recovery due to high-risk rates. The findings show that coastal regions generally show a more positive carrying capacity, likely due to less dense development or regulatory measures protecting these areas. In contrast, certain inland regions show more negative values, suggesting these areas might be experiencing higher ecological disturbance from construction activities. This information highlights the importance of strategic site analysis to balance energy production with conservation needs. The study provides insights into wind farm deployment that considers the visual and ecological characteristics of the landscape, promoting sustainability and community acceptance. For this reason, these insights can be effectively used for advancing renewable energy infrastructures within the European Union’s energy transition goals, particularly under the climate and energy objectives set for 2030. Full article

The pre-departure reserved time (PDRV) for high-speed railway (HSR) passengers, which encompasses all the time between passengers leaving their origin and the departure of the HSR train they are going to take, is a crucial factor in planning intercity travel. Understanding how passengers select their PDRV is not only important for developing effective strategies to improve HSR efficiency but also for optimizing the integration between HSR hubs and urban transportation networks. However, analyzing passenger choice behavior regarding PDRV is complex due to numerous influencing factors. Despite this, few studies have explored how HSR passengers make their PDRV choices. This paper, using Nanjingnan Railway Station as a case study, presents a novel investigation into the PDRV choice behavior of HSR passengers. An integrated latent class model (LCM) and ordered probit model (OPM) are applied to identify the factors affecting passengers’ PDRV choices. The sample data are segmented based on individual characteristics using the LCM, and OPM models are then constructed for each segment to analyze PDRV choice behavior. The results reveal that several factors—such as travel purpose, the number of times passengers used HSR at Nanjingnan Station in the previous year, the duration of HSR travel, the number of companions, feeder trip duration, and departure time—significantly impact PDRV choices. The integrated LCM and OPM approach also uncovers choice heterogeneity among different passenger groups. These insights can serve as a valuable reference for forecasting HSR passenger demand and for designing integrated HSR hubs and urban transport systems. Full article

The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability. Full article

As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing potential collisions. This study introduces the Deep learning-based Acceleration-aware Trajectory forecasting (DAT) model, a deep learning-based approach for object detection and trajectory forecasting, utilizing raw sensor measurements. DAT is an end-to-end model that processes sequential sensor data to detect objects and forecasts their future trajectories at each time step. The core innovation of DAT lies in its novel forecasting module, which leverages acceleration data to enhance trajectory forecasting, leading to the consideration of a variety of agent motion models. We propose a robust and innovative method for estimating ground-truth acceleration for objects, along with an object detector that predicts acceleration attributes for each detected object and a novel method for trajectory forecasting. DAT is trained and evaluated on the NuScenes dataset, demonstrating its empirical effectiveness through extensive experiments. The results indicate that DAT significantly surpasses state-of-the-art methods, particularly in enhancing forecasting accuracy for objects exhibiting both linear and nonlinear motion patterns, achieving up to a $2\times$ improvement. This advancement highlights the critical role of incorporating acceleration data into predictive models, representing a substantial step forward in the development of safer autonomous driving systems. Full article

In this paper, we investigate traffic bottlenecks, a primary cause of congestion that significantly impacts the overall efficiency of traffic networks. To address this challenge, a multi-segment variable speed limit control strategy is proposed to mitigate moving bottlenecks, particularly those on super-long-span bridges. First, an extended macro-traffic flow model, built upon the classic MetaNet framework, is proposed as a state-space model to capture the critical characteristics of long segments, which is a key contribution of this paper. Next, a fast prediction model is developed to forecast traffic flow states in lane-drop bottlenecks with restricted passing capacity over long road segments. Then, a controller leveraging the state-compensation flow model is designed to regulate the future evolution of bottleneck density. Finally, the multi-segment variable speed predictive control (MVSPC) strategy is validated on a simulation platform integrating PYTHON and SUMO, and its performance is compared with both traditional and advanced methods. The results demonstrate that under varying traffic flow levels, particularly in high-demand scenarios, the strategy achieves significant improvements in efficiency, safety, and environmental metrics. These include a 62.44% reduction in waiting time, a 95.32% decrease in potential collisions, and reductions in emissions: 26.4% in CO₂, 14.11% in CO, 26.53% in NO, and 32.90% in NOX. The proposed strategy is particularly effective for long segments, such as super-long-span bridges. Full article

The European Commission’s 2015 aging report forecasts a substantial increase in public spending on Long-Term Care (LTC) for OECD countries by 2060, posing significant fiscal challenges. This study aims to assess the efficiency and productivity of the LTC sector from 2010 to 2019 and explore whether efficiency gains can alleviate these fiscal pressures. Using a non-parametric Data Envelopment Analysis (DEA) model, combined with Tobit regression, we estimate the efficiency of OECD countries and examine the role of decentralization in shaping performance outcomes. The findings reveal that, on average, countries operate at 94% efficiency, with modest productivity growth. However, technical inefficiencies persist, especially in unitary countries, while federal countries, though initially less efficient, show greater improvements over time. Despite these gains, the current efficiency levels are insufficient to counterbalance the projected increase in LTC demand. Policymakers should prioritize reforms that enhance efficiency through decentralization, promoting accountability and competition as mechanisms to sustain the LTC system in the face of demographic shifts. Full article