2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2050-7038.12885

A brief review on microgrids: Operation, applications, modeling, and control

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Artificial intelligence techniques lead to data-driven energy services in distribution power systems by extracting value from the data generated by the deployed metering and sensing devices. This paper performs a holistic analysis of artificial intelligence applications to distribution networks, ranging from operation, monitoring and maintenance to planning. The potential artificial intelligence techniques for power system applications and needed data sources are identified and classified. The following data-driven services for distribution networks are analyzed: topology estimation, observability, fraud detection, predictive maintenance, non-technical losses detection, forecasting, energy management systems, aggregated flexibility services and trading. A review of the artificial intelligence methods implemented in each of these services is conducted. Their interdependencies are mapped, proving that multiple services can be offered as a single clustered service to different stakeholders. Furthermore, the dependencies between the AI techniques with each energy service are identified. In recent years there has been a significant rise of deep learning applications for time series prediction tasks. Another finding is that unsupervised learning methods are mainly being applied to customer segmentation, buildings efficiency clustering and consumption profile grouping for non-technical losses detection. Reinforcement learning is being widely applied to energy management systems design, although more testing in real environments is needed. Distribution network sensorization should be enhanced and increased in order to obtain larger amounts of valuable data, enabling better service outcomes. Finally, the future opportunities and challenges for applying artificial intelligence in distribution grids are discussed.

Artificial intelligence techniques for enabling Big Data services in distribution networks: A review

The IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

/pdf/environmentally-data-driven-smart-sustainable-cities-applied-1xypxx7uvr.pdf

Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism

This work analyzes interlinking converter control in hybrid AC/DC microgrids. The paper addresses the state-of-the-art general hybrid microgrid structure. The key power electronics topologies are used as bidirectional interface converters in the AC and DC parts. Different control structures of hybrid microgrids are categorized, followed by the classification of the main control functions, their control strategies, and the control techniques and a summary of their positive and negative aspects and applications. Control functions, strategies and techniques are classified in the interlinking-converter based. Finally, overall control objectives, time-scaled control structures, and their strategies are outlined. The prospects, main challenges, research gaps, and the trend of the hybrid microgrid structure and control are reviewed and summarized in the conclusions.

/pdf/recent-contributions-future-prospects-and-limitations-of-5f9ari7xfd.pdf

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

https://onlinelibrary.wiley.com/doi/epdf/10.1002/2050-7038.12106

A review on real-time simulation and analysis methods of microgrids

Optimal energy management of grid-connected multi-microgrid systems considering demand-side flexibility: A two-stage multi-objective approach

Aggregated demand-side energy flexibility: A comprehensive review on characterization, forecasting and market prospects

There are some environmental factors, such as ambient temperature, dust, etc, which cause a reduction in the efficiency of Photovoltaic (PV) systems Installation of PV panels on the water surface, commonly known as Floating Photovoltaic (FPV) systems, is one solution to employ PV panels in a cooler environment, achieve higher efficiency, and reduce water evaporation FPV systems open up new opportunities for scaling up solar generating capacity, especially in countries with high population density and valuable lands, as well as countries with high evaporation rates and water resources deficiency Since the FPV system is an almost new concept, its cleaning techniques have not been comprehensively studied While FPV systems are located on the surface of water resources and reservoirs, the water quality can limit the application of different cleaning techniques Therefore, this paper investigates different techniques of FPV systems cleaning and categorizes them into water-based and water-free approaches In addition, their cleaning frequencies, as well as economic aspects, are presented and discussed to determine their merits and demerits for using them in FPV systems

https://www.mdpi.com/1996-1073/14/7/2018/pdf

Cleaning of Floating Photovoltaic Systems: A Critical Review on Approaches from Technical and Economic Perspectives

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Roya Ahmadiahangar

Author Tools

Chat about Author