Dependency-Aware Clustering of Time Series and Its Application on Energy Markets

Ruiz-Abellón, María Del Carmen; Gabaldón, A.; Guillamón, Antonio

doi:10.3390/en9100809

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Renewable energy is a critical issue for energy companies from regulation aspects and technological marketing (Bollino & Madlener, 2016) Energy companies have initiated changing their portfolios in terms of sustainability, investing in new technologies to use renewables energy sources at a larger volume and with the best allocation in the energymix. (Ruiz-Abellon et al, 2016)…”

Section: Energy Sectormentioning

confidence: 99%

Knowledge Management Barriers in the Brazilian Energy Sector

Gouveia,

Gonçalves,

Sabbadini

et al. 2023

REVA

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Paper aims: Nowadays, there are happening profoundly changes in the energy sector worldwide. The principal innovations are sustainability, the growing share of renewable energy sources, and energy efficiency. Knowledge Management (KM) is a critical process in energy companies. KM implementation is harmfully affected by factors known as KM barriers. This paper aims to identify those barriers. Originality: Knowledge management is fundamental to most organizations’ strategy and operations in the energy sector. However, the energy companies need to overcome KM barriers. These issues are rarely addressed in the literature. Research method: This empirical, theoretical study is a qualitative approach, descriptive and exploratory. The research adopts the single case study method, having semi-structured interviews and participant observation to identify KM barriers. Main findings: Results demonstrate that the lack of incentive from top management, lack of motivation for sharing, staff retention hardship, and focusing merely on operations efficiency was identified as the main barriers to develop KM. Implications for theory and practice: These perspectives have not been explored yet and shall open a new approach to knowledge management implementation in energy companies. It offers practical guidance for diffusing and deploying KM initiatives in the energy sector.

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Section: Energy Sectormentioning

confidence: 99%

Knowledge Management Barriers in the Brazilian Energy Sector

Gouveia,

Gonçalves,

Sabbadini

et al. 2023

REVA

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“…The team of REDYD-2050 has developed some tools to perform energy price forecasting based on non-parametric estimation and neural networks [10]. The study of volatility and market dependencies has been performed through the so-called permutation entropy [11]. This method allows dealing with the seasonal component of the price series before the analysis, improving the capability of the method for the detection of changes in dependence levels along time through the filtering of seasonal patterns in prices (for instance, daily and weekly).…”

Section: Economic Models For Dr and Eementioning

confidence: 99%

Methodologies and proposals to facilitate the integration of small and medium consumers in smart grids

Álvarez¹,

Moreno

López

et al. 2017

CIRED - Open Access Proceedings Journal

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Future power grids need to be flexible on the demand side to develop a credible energy policy, and in particular the integration of renewable. This objective will need a more active consumer. The demonstration of customer capabilities is an important challenge for small and medium-sized segments, since their potential (contribution to load curve) is undoubtedly of interest. REDYD-2050 (http://www.redyd2050-der.eu/) is a research network funded by Spanish Government (2015-2017) that integrates seven groups that develop research in key technologies to achieve an integral development of demand response (DR). This article presents the objectives of the network, including integrating technologies and proposing innovative solutions to DR concerns such as modelling and aggregation, automation, application of ICT, implementation in markets, price and consumption forecasts, or monitoring and verification.

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“…The aim of the present paper is to illustrate the utility of symbolic dynamic (the time series are codified by means of permutations) in clustering time series by dependency, where the main contribution respect to the works mentioned above are the absence of assumptions and the detection of linear and non-linear dependencies. For that, we have based on the work developed by Ruiz-Abellon et al [20], where the next three aspects are combined: the time series codification by means of permutations, the distance measures among time series and different linkages for hierarchical clustering.…”

Section: Introduction and Main Definitionsmentioning

confidence: 99%

Using Permutations for Hierarchical Clustering of Time Series

Cánovas

Guillamón

Ruiz-Abellón

2019

Entropy

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Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method.

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Dependency-Aware Clustering of Time Series and Its Application on Energy Markets

Cited by 7 publications

References 19 publications

Knowledge Management Barriers in the Brazilian Energy Sector

Knowledge Management Barriers in the Brazilian Energy Sector

Methodologies and proposals to facilitate the integration of small and medium consumers in smart grids

Using Permutations for Hierarchical Clustering of Time Series

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