5,017 research outputs found
Days of autonomy for optimal Battery Sizing in Stand-alone Photovoltaic Systems
The main purpose of our article is to optimize the battery sizing by identifying the most appropriate number of autonomy days. A case study has been established and simulated to define the optimal number. In the others current researches, only a small importance has been attributed to the battery autonomy. The objective is generally to ensure a continuous presence of energy especially for isolated systems while this is not always optimal nor economical and does not necessarily guarantee a safe supply. Nevertheless, an over dimensioning of the battery will lead to a consequent cost and a loss of energy. The results show that the number of days of autonomy must correspond to the minimum ratio linking the lack of energy to the surplus during a specific period
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II
The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
Discovering the New Place of Learning
The book explores the potential of learning outside the traditional classroom when students gain real-world experiences in a variety of contexts and public spaces such as built, natural and virtual landscapes, museums, heritage sites, science centres and community venues. The authors of the book promote and put the flexible and ‘plastic’ concept of a place of learning into action by including physical geographical location, digital, virtual and textual spaces into the analysis. The book illuminates the importance of innovative educational strategies in connecting formal, non-formal and informal education – experiential learning in museums, heritage places and communities, inquiry-based pedagogy, digital storytelling, environmental online games, narrative geographies, and the use of geospatial technologies
Land Use and Land Cover Mapping in a Changing World
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems
Industry 4.0 Based Efficient Energy Management in Microgrid
Industry 4.0 which includes new technologies such as artificial intelligence, machine learning, and the internet of things etc. has brought the revolution in the field of energy management of a microgrid. Energy management is the backbone of a microgrid that needs to be controlled efficiently for a low system failure. There are a lot of issues, such as the intermittent nature of generation, proper voltage distribution, and harmonics, which may arise while implementing an energy management for a microgrid. Machine learning establishes the core of industry 4.0 and is one of the best-suited methods to mitigate such challenges in the current industry 4.0 scenario. In this paper, a Back Propagation Neural Network (BPNN) based machine learning approach is applied for forecasting of a photovoltaic (PV) generation in a microgrid to deal with its intermittent nature for efficient energy management. Further, a firefly optimization technique is utilized to mitigate the harmonics in the voltage. This model is implemented on a real dataset of a solar power plant in Delhi, India. The proposed approach achieves the results of high precision, recall, and accuracy, which shows the efficiency of the system to monitor and regulate uncertainties in the PV microgrid systems
Emergency shelter allocation planning technology for large-scale evacuation based on quantum genetic algorithm
IntroductionShelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods.MethodsThis article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints.Results and discussionResults show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter
Formation control of autonomous vehicles with emotion assessment
Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks
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