Adaptive Time Series Forecasting of Energy Consumption Using Optimized Cluster Analysis

Laurinec, Peter; Lóderer, Marek; Vrablecová, Petra; Lucká, Mária; Rozinajová, Viera; Ezzeddine, Anna Bou

doi:10.1109/icdmw.2016.0063

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…A household's or occupant's behavior patterns can be characterized through time-series data, known as load profile that indirectly informs on household energy use. Laurinec et al [53] found households with similar behavior patterns in their load profiles through k-Means clustering as a pre-step to predicting future consumption. Laurinec et al [53] examined nine different prediction approaches for forecasting the energy consumption of households, small and medium enterprises using load profile data.…”

Section: Improvement Based On Load Profilesmentioning

confidence: 99%

“…Laurinec et al [53] found households with similar behavior patterns in their load profiles through k-Means clustering as a pre-step to predicting future consumption. Laurinec et al [53] examined nine different prediction approaches for forecasting the energy consumption of households, small and medium enterprises using load profile data. They evaluated how profile load-based clustering, as a pre-step, would improve the results.…”

Section: Improvement Based On Load Profilesmentioning

confidence: 99%

“…Other works that considered OBs to improve load forecasting and other related solutions are summarized in Table 2 as an additional sample to the 18 works described in Section 4.1. These research works [19,43,53,54,56,57,59] are mainly described by their used household and building characteristics, occupant behavioural characteristics, and machine learning techniques.…”

Section: Summary Of Load Forecasting Work Considering Obsmentioning

confidence: 99%

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Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

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There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

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Section: Improvement Based On Load Profilesmentioning

confidence: 99%

Section: Improvement Based On Load Profilesmentioning

confidence: 99%

Section: Summary Of Load Forecasting Work Considering Obsmentioning

confidence: 99%

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Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

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“…They compared results from the proposed method with the completely aggregated method, completely disaggregated method, and K-means clustering. In our previous works [6][7][8], we have focused on time series representations and various forecasting methods to improve the forecasting accuracy of an aggregated load in the combination of K-means clustering. We concluded that the model-based representation methods are the best for the extracting of consumers' patterns.…”

Section: Related Workmentioning

confidence: 99%

Clustering-based forecasting method for individual consumers electricity load using time series representations

Laurinec¹,

Lucká²

2019

Energyo

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This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers. We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately.

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“…Cluster analysis is a popular unsupervised machine learning technique with diverse applications. Time series clustering in particular has been used effectively across a variety of application scenarios in the energy domain, including pricing [4], small scale renewable generation [28] and energy forecasts [18]. Cluster compactness and distinctness are two important attributes that characterise a good cluster set [22] and different analytical metrics have been proposed to measure them.…”

Section: Introductionmentioning

confidence: 99%

Identifying optimal clustering structures for residential energy consumption patterns using competency questions

Toussaint

Moodley

2020

Conference of the South African Institute of Computer Scientists and Information Technologists 2020

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Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications. CCS CONCEPTS • Computing methodologies → Cluster analysis; • Applied computing → Engineering.

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Adaptive Time Series Forecasting of Energy Consumption Using Optimized Cluster Analysis

Cited by 14 publications

References 28 publications

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

Clustering-based forecasting method for individual consumers electricity load using time series representations

Identifying optimal clustering structures for residential energy consumption patterns using competency questions

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