Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques

Muñoz, Víctor Manuel Gómez; Porta-Gándara, Miguel Ángel

doi:10.1016/s0960-1481(01)00013-1

Cited by 42 publications

(20 citation statements)

References 22 publications

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“…Xu and Wunsch [29] describe foundations and current state-of-the-art of the method. The combination of network design and clustering as a data analysis technique is used for different applications in context to infrastructure such as to detect and map water pollution [30], to profile road accident hotspots [31] or to identify local wind patterns for the modelling of renewable energy systems [32]. Fazlollahi et.…”

Section: Hierarchical and Partitioning Clusteringmentioning

confidence: 99%

Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids

2016

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Retrofitting the existing building stock is among the most important objectives and imperative to meet societal goals to reduce primary energy demand and anthropogenic greenhouse gas emissions. District heating systems have proven to supply heat for buildings both energy-and cost-efficiently. Thermal microgrids can be understood as a subcategory of district heating systems: small scale, bi-directional, low temperature and potentially fed by different thermal sources. Given a suitable combination of loads, number of and distance between buildings they can offer economic and environmental advantages compared to the supply by individual heating systems per building. We present a novel method using data analysis techniques on geo-referenced building stock data to identify suitable configurations of buildings that yield a cost-efficient thermal microgrid. For the identification both semantic and spatial data from a database are combined using fuzzy logics and cost-benefit analysis. We apply the method using a case study featuring a database of 306 buildings potentially to be retrofitted. As a result, we can identify 9 groups of in total 25 buildings that would form a microgrid featuring up to 17.4% cost benefits compared to an individual heat supply. This would save approximately 30% of the building induced CO 2 emission of the community.

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Section: Hierarchical and Partitioning Clusteringmentioning

confidence: 99%

Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids

2016

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“…And then the wind power output model of wind farms is established by using wind power data. By a principal component feature extraction technology [11] and a hierarchical clustering algorithm [12], it shows that the representative samples of wind power from historical time series, and then the UGF equivalent of wind farms is expressed as time variant according to the patterns after clustering.…”

Section: Ugf Expressionmentioning

confidence: 99%

“…According to [12], a day can be taken as a clustering time unit. Because of inhomogeneities in the measurement data, this hierarchical clustering algorithm combined with PCA feature extraction technology is applied to achieving the patterns of wind power.…”

Section: Ugf Expressionmentioning

confidence: 99%

“…On UGF modelling aspect of wind power, a hierarchal clustering method for constructing the timevariance UGF is proposed according to the non-stationary stochastic characteristics of wind power. Firstly, principal component analysis (PCA) [11] combined with a hierarchal clustering algorithm [12] is adopted to obtain the typical time-sequential samples of wind power output. An apportioning method [13] is used to establish the sequential UGF of the wind power output.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

Wang

Liu

Wang

2013

J. Mod. Power Syst. Clean Energy

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Universal Generating Function (UGF) techniques have been applied to Multi-State System (MSS) reliability analysis, such as long term reserve expansion of power systems with high wind power penetration. However, using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals. To more accurately assess the power system reliability, UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement. Firstly, a principal component analysis (PCA) combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power, then a sequential UGF equivalent model of wind power output is established by an apportioning method. Secondly, other than the traditional two-state models, the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform. In the construction process of such a UGF model, the state values are transformed into the integral multiples of one common factor by choosing proper common factors, thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof. The method is suitable for reliability assessment with dynamic probabilistic distributed random variables. In addition, by acquiring the clustering information of wind power, the system reliability indices, such as fuel cost and CO 2 emissions through different seasons and on different workdays, are calculated. Finally, the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.

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“…The goal of this study is to apply a clustering method [5] to classify winds patterns of a gridded data set in Santos Basin, Brazil, using reanalysis data from the National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) [6] [7]. Section 2 presented data set and study area.…”

Section: Introductionmentioning

confidence: 99%

Santos Basin Wind Patterns for Planning Offshore Pre-Salt Activities

Filho¹,

Ebecken²,

Oliveira³

2015

JEP

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Santos Basin contains the major hub of oil and gas exploration in Brazil. Consequently, knowledge of ocean surface winds in this area is very important for operational and planning activities. In addition, the importance of renewable energies is nowadays unquestionable, specifically in the case of the wind energy. In this paper, a data clustering technique is applied in order to obtain representative local wind patterns in Santos Basin. Reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) have been used in this study.

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Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques

Cited by 42 publications

References 22 publications

Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids

Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids

Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

Santos Basin Wind Patterns for Planning Offshore Pre-Salt Activities

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