Generation Of Future Scenarios Research Articles

Future scenario generation and reduction methods for solar photovoltaic electricity production analysis

Abstract A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.

A Submodel as a Plug-in for the Assessment of Energy Consumption and CO2 Emissions in Urban Mobility Plans

This study provides a comprehensive view of energy consumption and CO2 emissions for different vehicle technologies in real driving cycles. Universal correlation functions have been proposed for different vehicle technologies, associating fuel consumption and CO2 emissions with speed. A submodel was developed, calibrated for the city of Brasília, Brazil, and later used to simulate future scenarios with a higher prevalence of cleaner vehicle technologies, such as ethanol, hybrid, and electric vehicles. The use of the submodel can serve as a valuable tool for decision making in transport planning, allowing for a more realistic determination of energy consumption and CO2 emissions in different traffic conditions, i.e., in real driving cycles. The results obtained using the developed submodel showed that with the increased participation of more efficient vehicles, such as BEV and HEV, and an even greater participation of ethanol-powered vehicles, there is a significant reduction in CO2 emissions. Finally, the use of the developed tool allows managers and specialists in transport planning, through the generation of future scenarios, to propose and implement more effective policies to reduce CO2 emissions, thus contributing to more sustainable mobility.

Territorial Prospective to Sustainability: Strategies for Future Successful of Water Resource Management on Andean Basins

In Latin America, water resource management in some areas is difficult when all parts of a system are not considered (including its dynamism). Therefore, it becomes necessary to prepare instruments that facilitate management using a comprehensive approach. This study aimed to develop a methodology that allows one to conduct a prospective analysis of water management over delimited territories. The Zamora Huayco basin was chosen as the study area. This work included a survey of physical-natural, socioeconomic, and political-institutional variables, as well as a system structural analysis. Also, the generation of future scenarios and the strategic and tactical orientation for the integrated management of water resources. The results show that, of the 23 variables used, 19 were classified as key system variables. Most of the variables had strong impacts on each other, but at the same time these were highly receptive to changes. The behavior of change, proposed for the different uses and land cover in the basin for 2029, was considered as the objective scenario, highlighting the gain in forest areas and shrub vegetation. The strategic plans proposed in this methodology consider the structuring and collecting information in a single repository, creating communication channels between stakeholders and decision-makers.

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

Scenario-driven modular design in managing market uncertainty

Many organisational environments are far from equilibrium. The strategic flexibilities of modular product designs can help organisations prepare for an uncertain future, but managers must develop ways to imagine possible futures in order to use such flexibilities to respond to them. This paper discusses a scenario-based approach to anticipating and responding to uncertain futures by developing modular products that increase a firm's chances of succeeding in multiple alternative futures. After introducing the theoretical basis of this study, a structured approach to operationalising the generation of future scenarios and the development of modular product designs is developed. The approach consists of four stages: (1) the generation of scenarios about future market needs, (2) the translation of those needs into goals for modular designs, (3) the modular design process itself and (4) the evaluation of the modular design process. The modular design process we discuss is a function-based design approach that is form independent and thus allows joint consideration of future market needs and the product functions that can serve those needs. We illustrate our proposed approach by applying it to generate scenarios and modular design criteria for the Closed Circuit Television (CCTV) market in the security equipment sector in Turkey.