Suchergebnisse

Repair has been a longstanding activity in human society. However, in modern times, the production strategy of "planned obsolescence" has led to a decline in product quality and lifespan, resulting in closed and non-repairable products. Additionally, advertising manipulates consumers' desires, leading them to prefer replacing old items with new ones rather than repairing them. This has resulted in issues such as overconsumption, massive waste, and recycling challenges. Nevertheless, many DIY repair enthusiasts promote repair activities through communities and online media, highlighting the joy and creativity of repair. This unique relationship of care between people and objects extends the life of products and alters their original appearance and function, creating new possibilities to meet consumers' needs. This study analyzes actual cases to explore how DIY repairers innovate during the repair process, reinterpret old items, and elucidate the interaction between people and objects. Furthermore, it argues that repair activities represent a radical approach to mitigating waste and are crucial for advancing sustainable development.

Introduction -- Modeling for Energy Demand Forecasting -- Data Pre-processing Methods -- Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR's Parameters Determination -- Hybridizing QCM with Dragonfly algorithm to Enrich the Solution Searching Be-haviors -- Phase Space Reconstruction and Recurrence Plot Theory .

This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms

As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.