K-Means clustering technique applied to availability of micro hydro power

Adhau, S. P.; Moharil, R. M.; Adhau, P.G.

doi:10.1016/j.seta.2014.09.001

Cited by 34 publications

(20 citation statements)

References 16 publications

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“…The K-means iterative algorithm [21] is a classic clustering method. It sets several original clustering centers and adjusts the center locations and clustering results by iteration.…”

Section: Clustering and Decomposition Methodsmentioning

confidence: 99%

What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis

Meng

Zhou

et al. 2019

Sustainability

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The rapid growth of household electricity consumption is threatening the sustainable development of China’s economy and environment because of its impacts on the operation efficiency of the electric power system. To recognize the driving factors of the consumption growth and offer policy implications, based on the consumption-related data of 2015 and 2016, this research used the rank sum ratio (RSR) method to divide China’s 30 provinces into 5 groups and a logarithmic mean Divisia index (LMDI) algorithm to decompose the composition growth of each group into the quantitative contribution of each driving factor. The following conclusions were drawn from the empirical analysis. (1) The Yangtze basin is the most vigorous region of consumption growth and should be principally monitored. (2) Climate conditions have a remarkable impact on consumption growth and should be a key consideration when making differentiated household electricity policies. (3) The rebound effect has already appeared in a few of the most developed regions. Electricity price is an effective measure in dealing with this effect. (4) The improvement of the income level is the most important driving factor for consumption growth. (5) For provinces with high growth vitality, the change in the burden level of electricity expenditure prompts consumption growth. However, for provinces with low growth vitality, the situations are opposite. (6) The impacts of electricity price and population on consumption growth are negligible.

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“…The K-means iterative algorithm [21] is a classic clustering method. It sets several original clustering centers and adjusts the center locations and clustering results by iteration.…”

Section: Clustering and Decomposition Methodsmentioning

confidence: 99%

What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis

Meng

Zhou

et al. 2019

Sustainability

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“…One of the most common partitional clustering algorithms used in energy system optimization is the k-means algorithm, which has been used in a variety of studies [14,15,24,37,57,58,63,69,74,78,[83][84][85][86][87]97,[137][138][139]141,142,[145][146][147][148][153][154][155][156][157][158][159][160][161]. The objective of the k-means algorithm is to minimize the sum of the squared distances between all cluster members of all clusters and the corresponding cluster centers, i.e., min…”

Section: Partitional Clusteringmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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“…Lloyd's clustering algorithm or k‐means algorithm is a data‐partitioning and iterative algorithm that is widely used for clustering analysis in data mining studies 61,62 . Generally, the k‐means algorithm allocates