Xueqian Fu – författare
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6 produkter
6 produkter
Häftad, Engelska, 2025
2 088 kr
Skickas inom 10-15 vardagar
The Agricultural Energy Internet: Theories, Methods, and Future Prospects provides a pioneering guide to the grid integration and impact of agricultural energy systems for a distributed and sustainable power grid. This book begins with an introduction to fundamental concepts that is followed by a comprehensive safety analysis considering crop physiological characteristics. Subsequent chapters delve into enhancing the synergy of agriculture, energy, and the environment through carbon monitoring, optimal management strategies for rural microgrids, and distributed energy planning.Later chapters cover system design insights, load modeling, control strategies, and AI-based tools. Designed for engineers, students, and researchers, this book collates the state of research and provides detailed case studies and practical applications, making it indispensable for those innovating in sustainable energy systems.Includes a clear and incisive introduction to the nexus of agricultural needs, power grids, and smart energyPresents practical toolkits for a variety of scenarios, with detailed case studies, code files, and transparent data sharingProvides a new release in the Advances in Intelligent Energy Systems series, sharing the latest developments in sustainable power for the future
E-bok
Engelska, 20252 642 kr
Läs direkt efter köp
The Agricultural Energy Internet: Theories, Methods, and Future Prospects provides a pioneering guide to the grid integration and impact of agricultural energy systems for a distributed and sustainable power grid. This book begins with an introduction to fundamental concepts that is followed by a comprehensive safety analysis considering crop physiological characteristics. Subsequent chapters delve into enhancing the synergy of agriculture, energy, and the environment through carbon monitoring, optimal management strategies for rural microgrids, and distributed energy planning.Later chapters cover system design insights, load modeling, control strategies, and AI-based tools. Designed for engineers, students, and researchers, this book collates the state of research and provides detailed case studies and practical applications, making it indispensable for those innovating in sustainable energy systems.- Includes a clear and incisive introduction to the nexus of agricultural needs, power grids, and smart energy- Presents practical toolkits for a variety of scenarios, with detailed case studies, code files, and transparent data sharing- Provides a new release in the Advances in Intelligent Energy Systems series, sharing the latest developments in sustainable power for the future
Häftad, Engelska, 2025
1 876 kr
Skickas inom 10-15 vardagar
Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers.Explores how Statistical Relational Artificial Intelligence (StaRAI) can be applied to photovoltaic power prediction, maintenance, and planningProvides a theoretical framework supported by schematic diagrams, real examples, and codeDiscusses the potential for groundbreaking AI applications in PV, future opportunities, and ethical and societal impacts
E-bok
Engelska, 20252 373 kr
Läs direkt efter köp
Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers.- Explores how Statistical Relational Artificial Intelligence (StaRAI) can be applied to photovoltaic power prediction, maintenance, and planning- Provides a theoretical framework supported by schematic diagrams, real examples, and code- Discusses the potential for groundbreaking AI applications in PV, future opportunities, and ethical and societal impacts
Inbunden, Engelska, 2027
1 558 kr
Kommande
A unified framework for photovoltaic multi-timescale uncertainty modeling Research on photovoltaic uncertainty remains fragmented: physical models lack interpretability, deep learning sacrifices generalizability, and no end-to-end solutions exist for real grid scenarios. Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling: Theory, Case Analysis, and Engineering Practice delivers a unified framework integrating real-world PV power data with complete workflows for grid planning, operation, and uncertainty-aware decision-making. The book systematically addresses how weather conditions, seasonal patterns, and time-of-day effects drive generation variability across multiple time scales. Case studies drawn from operational PV plants and real power system environments demonstrate a complete workflow from problem formulation through solution development. Practical datasets, executable code, and engineering examples show how proposed approaches translate into implementable solutions. Readers will also find: Concrete implementation guidance for statistical relational AI methods applied to data organization, pattern discovery, and supporting analytical tasksProbabilistic techniques for quantifying PV output variability for stochastic optimization and electricity market operationsA complete end-to-end technical pipeline spanning data acquisition, preprocessing, modeling, forecasting, and engineering deploymentA structured perspective on future development trajectories for AI-driven photovoltaic uncertainty research and applicationsSolutions designed specifically for real PV grid scenarios rather than idealized or purely simulated environmentsDesigned for university faculty, academic researchers, power-system engineers, and graduate students, this book provides structured methodologies and reproducible tools for modeling PV uncertainty across time scales. Grid planners and renewable energy technology practitioners will also find directly applicable workflows for operational decision-making.
Inbunden, Engelska, 2026
2 197 kr
Skickas inom 5-8 vardagar
In smart agriculture, core functions such as agricultural machinery automation, intelligent environmental sensing, and precision crop management rely heavily on continuous, high-quality energy supply. Intelligent power systems tailored to agricultural scenarios are essential to realizing unmanned farms. Moreover, as a result of rural revitalization strategies and national programmes such as China's dual carbon goals, rural areas and agriculture are no longer merely energy consumers. Agricultural and rural zones are increasingly becoming important providers of distributed renewable energy and suitable deployment sites for energy infrastructure. As such, the agricultural energy internet is now entering a critical stage of rapid development and engineering transformation.Evolution of the Agricultural Energy Internet: Modeling, simulation and engineering practice focuses on the field of agricultural energy and falls under the discipline of agricultural electrification and automation. The book systematically elaborates on the principles and applications of the agricultural energy internet through theoretical modelling, simulation techniques and engineering case studies - especially in response to the growing demand for clean, reliable, and intelligent power supply systems that enable the future development of unmanned and smart agriculture.A key feature of this book lies in its integrated structure combining theoretical analysis, code implementation, and real-world engineering cases. Chapters offer a systematic overview of modelling and simulation, methods for agricultural energy internet systems, provide reproducible code implementations, and present in-depth case studies based on projects including the aqua photonics-based agricultural energy internet at the Smart Farm in Zhuoxhou, Hebei Province, China, and the agricultural park in Langyatai, Qingdao, China.The closed-loop content design - from theory to simulation to engineering validation - makes the book suitable both for researchers developing algorithms and models, and for engineers seeking practical guidance in system deployment.