Using participatory-planning-based cooperative housing approaches as housing improvement solutions for Xizhou indigenous squatter settlements in New Taipei City, Taiwan
In: City, Culture and Society, Band 23, S. 100370
ISSN: 1877-9166
90 Ergebnisse
Sortierung:
In: City, Culture and Society, Band 23, S. 100370
ISSN: 1877-9166
In: Asian survey, Band 25, Heft 5, S. 512-520
ISSN: 1533-838X
In: Asian survey: a bimonthly review of contemporary Asian affairs, Band 25, Heft 5, S. 512-520
ISSN: 0004-4687
World Affairs Online
In: Asian survey: a bimonthly review of contemporary Asian affairs, Band 25, Heft 5, S. 512-520
ISSN: 0004-4687
In: Research Policy, Band 37, Heft 4, S. 580-595
In: International journal of the sociology of language: IJSL, Band 2008, Heft 189
ISSN: 1613-3668
In: International journal of the sociology of language: IJSL, Band 122, Heft 1
ISSN: 1613-3668
In: The international journal of sociology and social policy, Band 21, Heft 8/9/10, S. 116-127
ISSN: 1758-6720
In: Environmental science and pollution research: ESPR, Band 27, Heft 17, S. 21140-21158
ISSN: 1614-7499
In: Journal of the City Planning Institute of Japan, Band 52, Heft 3, S. 560-567
ISSN: 2185-0593
In: Springer eBook Collection
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 .
In: Lecture Notes in Energy 10
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
In: Lecture notes in energy, 10
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.
In: Contributions to economics
In: Journal of Literature and Art Studies, September 2021, Vol. 11, No. 9, 620-625 doi: 10.17265/2159-5836/2021.09.002
SSRN