Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

Laurinec, Peter; Lóderer, Marek; Lucká, Mária; Rozinajová, Viera

doi:10.1007/s10844-019-00550-3

Cited by 21 publications

(10 citation statements)

References 29 publications

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“…For verifying the accuracy of the RF predictor and analyzing the influence of the accumulative error on different predictors, two types of rolling forecast models are designed using the RF and the ANN [16]. The prediction error for the rolling forecast is mainly affected by two factors: (1) the predictor performance and (2) the use of the predicted values as the eigenvalues for the subsequent forecast.…”

Section: B Prediction Accuracy Of Rf In Rolling Prediction Modelmentioning

confidence: 99%

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Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Huang

Wang

Sining³

et al. 2020

IEEE Access

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Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network. INDEX TERMS Aggregated load forecasting, feature importance, load fluctuation, crow search algorithm, S-Dbw, random forest.

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Section: B Prediction Accuracy Of Rf In Rolling Prediction Modelmentioning

confidence: 99%

“…Ref. [16] proves that the ensemble learning method can significantly improve the prediction accuracy of electricity load with high fluctuation. For deep learning methods, Ref.…”

Section: Introductionmentioning

confidence: 97%

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Huang

Wang

Sining³

et al. 2020

IEEE Access

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“…Clustering Historic energy demand [21,48,49,81,82,[91][92][93]109,114,118,155,163,232,[234][235][236]263,268,279,322,323,326,328,342,353,355,360,369,372,395,403,428,476] Weather data [21,82,[91][92][93]114,118,163,263,275,348,353,476] Calendar data [48,92,93,109,…”

Section: Methods Input Referencesmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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“…In AR, the future observations depend on the recent past observations and a random error together with a constant term. COMPEL 41,1 3.3 K-Means algorithm K-Means algorithm is considered one of the most popular and simplest clustering techniques (Laurinec et al, 2019). It is a distance-based technique.…”

Section: Autoregressive Methodsmentioning

confidence: 99%

“…Vrablecova et al (2018) carried out a STLF using the online support vector regression (SVR) to predict the aggregated load of 3,639 Irish smart meters that included no missing values. Laurinec et al (2019) introduced two unsupervised ensemble learning methods include the newly proposed density-clustering based and bootstrap aggregating based to assess the performance of prediction on clustered or aggregated load. Peng et al (2019) focused on STLF for small-and-medium enterprise and residential customers at the aggregate level.…”

Section: Literature Reviewmentioning

confidence: 99%

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

Rostum

Moustafa

Ziedan

et al. 2021

COMPEL

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Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

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Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

Cited by 21 publications

References 29 publications

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Modeling Energy Demand—A Systematic Literature Review

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

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