Energy Informatics最新文献

Over the past decade, the world has experienced a remarkable shift in the automotive landscape, as electric vehicles (EVs) have appeared as a viable and increasingly popular alternative to the long-standing dominance of internal combustion engine (ICE) vehicles and their ability to absorb the surplus of electricity generated from renewable sources. This paper presents a detailed examination of the different categories of EVs, charging methods and explores energy generation systems tailored for EVs. As vehicle complexity and road congestion increase with the growth of EVs, the need for intelligent transport systems to improve road safety and efficiency becomes imperative. Machine learning (ML), recognized as a powerful approach for adaptive and predictive system development, has gained importance in the vehicle domain. By employing a variety of algorithms, ML effectively addresses pressing issues related to electric vehicles, including battery management, range optimization, and energy consumption. This paper conducts a brief review of ML methods, including both traditional and applied approaches, to address energy consumption issues in EVs, such as range estimation and prediction, as well as range optimization.

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m² in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m² to 502937.83 kWh/m², photovoltaic glass reduced 552243.87 kWh/m², and roof greening reduced to 635947.35 kWh/m². All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

Introduction

Energy system simulation modeling plays an important role in understanding, analyzing, optimizing, and guiding the change to sustainable energy systems.

Objectives

This review aims to examine energy system simulation modeling, emphasizing its role in analyzing and optimizing energy systems for sustainable development.

Methods

The paper explores four key simulation methodologies; Agent-Based Modeling (ABM), System Dynamics (SD), Discrete-Event Simulation (DES), and Integrated Energy Models (IEMs). Practical applications of these methodologies are illustrated through specific case studies.

Results

The analysis covers key components of energy systems, including generation, transmission, distribution, consumption, storage, and renewable integration. ABM models consumer behavior in renewable energy adoption, SD assesses long-term policy impacts, DES optimizes energy scheduling, and IEMs provide comprehensive sector integration. Case studies demonstrate the practical relevance and effectiveness of these models in addressing challenges such as data quality, model complexity, and validation processes.

Conclusions

Simulation modeling is essential for addressing energy challenges, driving innovation, and informing policy. The review identifies critical areas for improvement, including enhancing data quality, refining modeling techniques, and strengthening validation processes. Future directions emphasize the continued importance of simulation modeling in achieving sustainable energy systems.

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified.

From the perspective of configuration, this paper takes the region of manufacturing efficiency as the explanatory variable, selects eight antecedent conditions, and applies fuzzy set qualitative comparative analysis (fsQCA) to study the paths and methods of improving manufacturing emission efficiency. The results of the study show that there are two configuration paths of carbon emission efficiency in manufacturing industry, namely, research frontier and technological innovation level and labour force structure, R&D investment, science and technology innovation level, manufacturing output value, and environmental regulation synergistic path.

To explore the optimization method of grid-connected voltage support technology in new energy stations, this study first analyzes and discusses this technology. Second, this study describes the deep learning model architecture and feature selection in detail and determines the framework used for the optimization model proposed here. Lastly, the development of optimization and control strategies is investigated, and the optimized model’s effectiveness is verified through experiments. The results reveal that the optimized model's accuracy, precision, recall, and F1 score are higher than those of the comparison model in the performance comparison experiment, reaching the highest values of 0.890, 0.888, 0.878, and 0.883, respectively. This reflects that the optimized model shows high performance on small datasets, and its performance benefits become more pronounced as the data volume increases. This feature is particularly significant because, in practical applications, power systems often need to process large amounts of data to achieve efficient voltage support. In simulation experiments, the optimized model demonstrates excellent performance in terms of response time, stability, robustness, and energy consumption. Moreover, this model effectively addresses various data challenges and uncertainties encountered in grid-connected voltage support technology for power systems, thereby providing robust support for stable and efficient voltage regulation. In light of the findings, this study offers substantial insights for advancing research in the realms of power systems and new energy technologies. The exploration into the application of deep learning and intelligent control strategies within power systems reveals significant potential for transforming grid optimization practices. This study accentuates how data-driven methodologies can revolutionize energy management, paving the way for smarter and more efficient energy systems. By enhancing both the responsiveness and operational efficiency of power grids, the study contributes to the acceleration of digital transformation within the energy sector, fostering innovation and laying a robust foundation for future advancements in energy informatics.

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy; however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their ({text{l}}_{2})-norm. Based on the results, LSTM, CNN obtained the highest accuracy followed by CNN-LSTM and lastly MLP.