�ݺ�ߣshows by User: PeterLaurinec

�ݺ�ߣshows by User: PeterLaurinec / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: PeterLaurinec / Mon, 29 Oct 2018 08:31:27 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: PeterLaurinec Time Series Data Mining - from PhD to Startup /slideshow/time-series-data-mining-from-phd-to-startup/121046724 satrdaybelehrad-181029083127
The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data. I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown.]]>
The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data. I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown.]]> Mon, 29 Oct 2018 08:31:27 GMT /slideshow/time-series-data-mining-from-phd-to-startup/121046724 PeterLaurinec@slideshare.net(PeterLaurinec) Time Series Data Mining - from PhD to Startup PeterLaurinec The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data. I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/satrdaybelehrad-181029083127-thumbnail.jpg?width=120&height=120&fit=bounds" /> The talk will be oriented on differences between "doing" a research and an application of time series data mining to real problems in business on a real rich data. I will discuss, why research and business need to be related and also not. Typical tasks of time series data mining in energetics with use cases in R will be shown.

Time Series Data Mining - from PhD to Startup from Peter Laurinec

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0 Time series representations for better data mining /slideshow/time-series-representations-for-better-data-mining/97203831 erumlaurinec-180515190046
Talk on eRum 2018]]>
Talk on eRum 2018]]> Tue, 15 May 2018 19:00:46 GMT /slideshow/time-series-representations-for-better-data-mining/97203831 PeterLaurinec@slideshare.net(PeterLaurinec) Time series representations for better data mining PeterLaurinec Talk on eRum 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/erumlaurinec-180515190046-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk on eRum 2018

Time series representations for better data mining from Peter Laurinec

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0 New Clustering-based Forecasting Method for Disaggregated End-consumer Electricity Load Using Smart Grid Data /slideshow/new-clusteringbased-forecasting-method-for-disaggregated-endconsumer-electricity-load-using-smart-grid-data/82114454 informaticslaurinec-171115164715
This paper presents a new method for forecasting the 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 representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer.]]>
This paper presents a new method for forecasting the 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 representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer.]]> Wed, 15 Nov 2017 16:47:15 GMT /slideshow/new-clusteringbased-forecasting-method-for-disaggregated-endconsumer-electricity-load-using-smart-grid-data/82114454 PeterLaurinec@slideshare.net(PeterLaurinec) New Clustering-based Forecasting Method for Disaggregated End-consumer Electricity Load Using Smart Grid Data PeterLaurinec This paper presents a new method for forecasting the 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 representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/informaticslaurinec-171115164715-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper presents a new method for forecasting the 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 representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer.

New Clustering-based Forecasting Method for Disaggregated End-consumer Electricity Load Using Smart Grid Data from Peter Laurinec

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0 Is Unsupervised Ensemble Learning Useful for Aggregated or Clustered Load Forecasting? /slideshow/is-unsupervised-ensemble-learning-useful-for-aggregated-or-clustered-load-forecasting/80096857 ecmlpkddlaurinec-170924083953
The research work that was presented on the workshop called New Frontiers in MINING COMPLEX PATTERNS in conjunction with ECML-PKDD 2017 (Skopje, Macedonia).]]>
The research work that was presented on the workshop called New Frontiers in MINING COMPLEX PATTERNS in conjunction with ECML-PKDD 2017 (Skopje, Macedonia).]]> Sun, 24 Sep 2017 08:39:53 GMT /slideshow/is-unsupervised-ensemble-learning-useful-for-aggregated-or-clustered-load-forecasting/80096857 PeterLaurinec@slideshare.net(PeterLaurinec) Is Unsupervised Ensemble Learning Useful for Aggregated or Clustered Load Forecasting? PeterLaurinec The research work that was presented on the workshop called New Frontiers in MINING COMPLEX PATTERNS in conjunction with ECML-PKDD 2017 (Skopje, Macedonia). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ecmlpkddlaurinec-170924083953-thumbnail.jpg?width=120&height=120&fit=bounds" /> The research work that was presented on the workshop called New Frontiers in MINING COMPLEX PATTERNS in conjunction with ECML-PKDD 2017 (Skopje, Macedonia).

Is Unsupervised Ensemble Learning Useful for Aggregated or Clustered Load Forecasting? from Peter Laurinec

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0 RSlovakia #1 meetup /slideshow/r-lt-slovakia-1-meetup/73523406 rslovakia-170323082211
Title: Using Clustering Of Electricity Consumers To Produce More Accurate Predictions. Description: I will give you tips and tricks for predicting mainly seasonal time series of electricity consumption. Appropriate methods to predict seasonal time series will be mentioned. We will pay main attention to the usage of cluster analysis to produce more accurate predictions and the following importance of dimensionality reduction. We will talk how such approaches can be efficiently programmed in R (data.table, parallel, acceleration of matrix calculus - MRO).]]>
Title: Using Clustering Of Electricity Consumers To Produce More Accurate Predictions. Description: I will give you tips and tricks for predicting mainly seasonal time series of electricity consumption. Appropriate methods to predict seasonal time series will be mentioned. We will pay main attention to the usage of cluster analysis to produce more accurate predictions and the following importance of dimensionality reduction. We will talk how such approaches can be efficiently programmed in R (data.table, parallel, acceleration of matrix calculus - MRO).]]> Thu, 23 Mar 2017 08:22:11 GMT /slideshow/r-lt-slovakia-1-meetup/73523406 PeterLaurinec@slideshare.net(PeterLaurinec) RSlovakia #1 meetup PeterLaurinec Title: Using Clustering Of Electricity Consumers To Produce More Accurate Predictions. Description: I will give you tips and tricks for predicting mainly seasonal time series of electricity consumption. Appropriate methods to predict seasonal time series will be mentioned. We will pay main attention to the usage of cluster analysis to produce more accurate predictions and the following importance of dimensionality reduction. We will talk how such approaches can be efficiently programmed in R (data.table, parallel, acceleration of matrix calculus - MRO). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rslovakia-170323082211-thumbnail.jpg?width=120&height=120&fit=bounds" /> Title: Using Clustering Of Electricity Consumers To Produce More Accurate Predictions. Description: I will give you tips and tricks for predicting mainly seasonal time series of electricity consumption. Appropriate methods to predict seasonal time series will be mentioned. We will pay main attention to the usage of cluster analysis to produce more accurate predictions and the following importance of dimensionality reduction. We will talk how such approaches can be efficiently programmed in R (data.table, parallel, acceleration of matrix calculus - MRO).

RSlovakia #1 meetup from Peter Laurinec

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0 Banalytics /slideshow/banalytics/66499554 banalytics-160928071730
Presentation about my PhD. thesis in Slovak. Prediction of electricity load in smart grids.]]>
Presentation about my PhD. thesis in Slovak. Prediction of electricity load in smart grids.]]> Wed, 28 Sep 2016 07:17:29 GMT /slideshow/banalytics/66499554 PeterLaurinec@slideshare.net(PeterLaurinec) Banalytics PeterLaurinec Presentation about my PhD. thesis in Slovak. Prediction of electricity load in smart grids. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/banalytics-160928071730-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation about my PhD. thesis in Slovak. Prediction of electricity load in smart grids.

Banalytics from Peter Laurinec

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/satrdaybelehrad-181029083127-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/time-series-data-mining-from-phd-to-startup/121046724 Time Series Data Minin... https://cdn.slidesharecdn.com/ss_thumbnails/erumlaurinec-180515190046-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/time-series-representations-for-better-data-mining/97203831 Time series representa... https://cdn.slidesharecdn.com/ss_thumbnails/informaticslaurinec-171115164715-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/new-clusteringbased-forecasting-method-for-disaggregated-endconsumer-electricity-load-using-smart-grid-data/82114454 New Clustering-based F...