scholarly journals Strategies development for the formation of resource-saving regional energy systems based on data mining methods

2021 ◽  
Vol 288 ◽  
pp. 01052
Author(s):  
Evgeny Lisin ◽  
Galina Kurdiukova ◽  
Irina Zameshaeva
Keyword(s):  
Data Mining ◽  
Power Systems ◽  
Energy Intensity ◽  
Energy Systems ◽  
Structural Features ◽  
Resource Saving ◽  
Regional Energy ◽  
Clustering And Classification ◽  
Using Data ◽  
Regional Energy Systems

The Russian regions have been tasked with reducing the energy intensity of the country’s gross product through the implementation of regional state programs for the formation of resource-saving energy systems. The required reduction in the GRP energy intensity can be achieved by different types of strategies for the development of the energy economy of the regions, the effectiveness of which largely depends on the production, technological and structural features of regional energy systems. In the work, using data mining tools, clustering and classification of regional power systems are carried out and the best resource saving strategies are proposed for each of the selected groups of power systems.

scholarly journals Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 495
Author(s):  
Jessica Thomsen ◽  
Noha Saad Hussein ◽  
Arnold Dolderer ◽  
Christoph Kost
Keyword(s):  
Computation Time ◽  
Energy Systems ◽  
Energy System ◽  
Variable Cost ◽  
Optimization Approach ◽  
Or Efficiency ◽  
Electricity Grid ◽  
Regional Energy ◽  
Long Run ◽  
Regional Energy Systems

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

scholarly journals An Indicator-Based Approach for Analysing the Resilience of Transitions for Energy Regions. Part II: Empirical Application to the Case of Weiz-Gleisdorf, Austria

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2263 ◽  
Author(s):  
Romano Wyss ◽  
Susan Mühlemeier ◽  
Claudia Binder
Keyword(s):  
Path Length ◽  
Energy Systems ◽  
Energy System ◽  
Degree Centrality ◽  
Study Region ◽  
Regional Energy ◽  
Stabilization Phase ◽  
Full Picture ◽  
Regional Energy Systems

In this paper, we apply an indicator-based approach to measure the resilience of energy regions in transition to a case study region in Austria. The indicator-based approach allows to determine the resilience of the transition of regional energy systems towards higher shares of renewables and potentially overall higher sustainability. The indicators are based on two core aspects of resilience, diversity and connectivity. Diversity is thereby operationalized by variety, disparity and balance, whereas connectivity is operationalized by average path length, degree centrality and modularity. In order to get a full picture of the resilience of the energy system at stake throughout time, we apply the measures to four distinct moments, situated in the pre-development, take-off, acceleration and stabilization phase of the transition. By contextually and theoretically embedding the insights in the broader transitions context and empirically applying the indicators to a specific case, we derive insights on (1) how to interpret the results in a regional context and (2) how to further develop the indicator-based approach for future applications.

scholarly journals Governing Decentral Energy Systems

2021 ◽  
pp. 159-174
Author(s):  
Peter Hettich
Keyword(s):  
Energy Systems ◽  
Energy System ◽  
Energy Markets ◽  
European Level ◽  
Top Down ◽  
Market Participants ◽  
Regional Energy ◽  
Changing Roles ◽  
Local Actors ◽  
Regional Energy Systems

AbstractAgainst the backdrop of an energy system moving from vertically integrated monopolies towards a decentral system with a multitude of actors in ever-changing roles, we observe a gradual strengthening of central governance mechanisms on the nation-state and on the European level. Such a top-down approach to the governance of the energy system might have been necessary to open up energy markets to competitive processes and innovation. With social goals shifting and security of supply and environmental concerns gaining importance, the governance of the energy system has to be reshaped anew, enabling, e.g., the optimization of regional energy systems by local actors. In particular, strict unbundling rules may hinder or preclude system-serving behavior, to the detriment of all market participants and consumers. Lawmakers and regulators should provide some leeway to cooperative approaches, such as the empowerment of local actors to devise their own energy regimes.

Institutional Research Using Data Mining

2011 ◽  
pp. 66-102 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Radu Mogos
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Virtual Learning Environment ◽  
Student Activities ◽  
Participatory Behavior ◽  
Student Profiles ◽  
Clustering And Classification ◽  
E Learning ◽  
Using Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program in project management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

A Model for Extracting Most Desired Web Pages

2020 ◽  
pp. 119-145
Author(s):  
Jayanti Mehra ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Data Mining ◽  
Path Length ◽  
Web Mining ◽  
Statistical Information ◽  
Web Pages ◽  
Systems Practice ◽  
Clustering And Classification ◽  
Using Data ◽  
Access Logs ◽  
Manipulation Process

Weblog analysis takes raw data from access logs and performs study on this data for extracting statistical information. This info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed and successful cached hits, average time of view, average path length over a website; analytical information such as page was not found errors and server errors; server information, which includes exit and entry pages, single access pages, and top visited pages; requester information like which type of search engines is used, keywords and top referring sites, and so on. In general, the website administrator uses this kind of knowledge to make the system act better, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced web mining systems practice this kind of information to take out more difficult or complex interpretations using data mining procedures like association rules, clustering, and classification.

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