1
|
Oroná JD, Zorrilla SE, Peralta JM. Sensitivity analysis on the release of food active compounds from viscoelastic matrices. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
2
|
Characterising electricity demand through load curve clustering: A case of Karnataka electricity system in India. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
3
|
Using hydrogen and ammonia for renewable energy storage: A geographically comprehensive techno-economic study. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106785] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
4
|
Abstract
Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.
Collapse
|
5
|
Alhameli F, Elkamel A, Betancourt‐Torcat A, Almansoori A. A mixed‐integer programming approach for clustering demand data for multiscale mathematical programming applications. AIChE J 2019. [DOI: 10.1002/aic.16578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Falah Alhameli
- Department of Chemical Engineering University of Waterloo Waterloo Ontario Canada
| | - Ali Elkamel
- Department of Chemical Engineering University of Waterloo Waterloo Ontario Canada
- Department of Chemical Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates
| | - Alberto Betancourt‐Torcat
- Department of Chemical Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates
| | - Ali Almansoori
- Department of Chemical Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates
| |
Collapse
|
6
|
Time-series aggregation for synthesis of distributed energy supply systems by bounding error in operational expenditure. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/b978-0-444-63428-3.50137-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
|