Predictive segmentation of energy consumers

Albert, Adrian; Maasoumy, Mehdi

doi:10.1016/j.apenergy.2016.05.128

Cited by 28 publications

(27 citation statements)

References 18 publications

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“…Table 17 demonstrates some critical papers in this field. Albert and Maasoumy (2016) [104] presented a predictive segmentation technique to create the targeting process and highly-interpretable segmentation for energy companies. The presented model utilized demographics, consumption and program enrollment data to make predictive patterns.…”

Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Through such frameworks and processes, organisations would possess a platform that supports coherence and repeatability of analysis, irrespective of whom As impressive as the tremendous growth in the recognition of industrial CBM seems, the guidelines of the recently launched asset management standards -ISO 55000 series (initially a publicly available specification published by the British Standards Institution in 2004) in 2014 makes it crystal clear that asset performance and condition data alone are no longer sufficient for making robust MAM engineering decisions in capital intensive sectors such as power [24]. This is based on the premise that the role of MAM is changing from the classical "problem-fixer" to a very important aspect of asset life cycle management through the incorporation of the following key themes [24]: Unfortunately, despite widespread consensus that MAM is a necessity for the cost-effectiveness of any plant, some decision-makers within several organisations still view the maintenance function as a mere cost centre or necessary evil [23,24], which is perhaps why maintenance as a function has struggled to attain the same level of recognition attributed to other vital plant functions such as finance [25][26][27][28][29][30][31][32], production planning [33][34][35][36][37][38][39][40], marketing [41][42][43][44], etc. Searches within several top percentile energy-related journals clearly show a contrariety between the scanty number of academic publications advocating MAM optimisation and the abundant resources on topics such as energy policy and finance.…”

Section: Trillion Kwh In 2012 To 223 Trillion Kwh In 2040)mentioning

confidence: 99%

A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems

Ayu¹,

Yunusa‐Kaltungo

2020

Energies

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The outburst of population as well as increasing industrialisation have triggered a very prominent imbalance between electricity demand and supply in emerging economies such as Indonesia. Based on this premise, electricity generation and distribution firms such as Perusahaan Listrik Negara (PLN) are faced with an urgent need to enhance availability and reliability through capacity expansion as well as the institutionalisation of cost-effective maintenance and asset management (MAM) principles. Some of the principles recommended here involve embedding customised overall health index (OHI) and total life cycle cost (LCC) estimation principles into engineering decisions that relate to asset renewal and/or replacement. While discussions about the fundamental theories and estimation approaches for OHI and LCC for power transformers (PTs) already exist in the current body of literature, however, they are mostly in a generic form which has somewhat limited proper implementation of these valuable principles in practice. This study is unique because it provides a very systematic framework towards achieving cost-effective MAM through a case study. Additionally, the proposed framework is all-encompassing, as it also assesses the impacts of human unreliability through the application or proven risk assessment techniques. The proposed framework commences with the evaluation of existing decision support system at PLN through a MAM audit, whereby the performance of the West Java arm of PLN with regards to critical MAM elements was examined.

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“…Quantity and variability are also accounted for by use of other suitable measures. Ref [44] addresses the problem of segmentation of electricity users for the utilities by using consumption, demographics and previous program enrolment data. The final goal is to extract those users that are most probable to enroll in different energy efficiency or demand response programs and to target each group with efficient appropriate messages.…”

Section: Demand Responsementioning

confidence: 99%

A review on clustering of residential electricity customers and its applications

Rajabi

Zhang

et al. 2017

2017 20th International Conference on Electrical Machines and Systems (ICEMS)

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Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation and load profiling. The recent changes in power system structure and introduction of new technologies necessitate the new investigation of applications and benefits of clustering methods for power systems. In this regard, this paper aims at reviewing the new research for clustering techniques for residential customers.

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Predictive segmentation of energy consumers

Cited by 28 publications

References 18 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems

A review on clustering of residential electricity customers and its applications

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