Towards Forecasting Demand and Production of Electric Energy in Smart Grids

Filipowska, Agata; Fabisz, Karol; Hossa, Tymoteusz; Mucha, Michał; Hofman, Radosław

doi:10.1007/978-3-642-40823-6_24

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…Now, considering the abovementioned profile for a one particular household, an aggregator is ready to prepare similar calculations for the whole group of prosumers in the microgrid. We believe that this way, the aggregator is able to lower the overall costs of the electric energy, strengthen users' awareness (by presenting results to users) and improve the energy efficiency (Filipowska et al, 2013).…”

Section: Sum Check 715mentioning

confidence: 99%

“…Based on the energy consumption patterns, we differentiate between the following stereotypes (where a stereotype is a set of data that determines a family type, a building type, a number of persons in a household, appliances used (nominal power, number of devices, an energy class), energy properties of a building, prosumer behavior habits, etc. (Filipowska et al, 2013)):…”

Section: Prosumer Stereotypes In Microgridsmentioning

confidence: 99%

“…This information feeds the FEMS tool that analyzes it and recommends further actions (i.e. provides tips for the energy saving) to prosumers and to a microgrid administrator (Filipowska et al, 2013).…”

Section: Assumptions Of the Proposed Approachmentioning

confidence: 99%

“…and "How to determine a role of a prosumer?" (Shandurkova et al, 2013;Brendal, 2013;Filipowska et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Fabisz

Filipowska

Hossa

2015

International Journal of Energy Optimization and Engineering

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Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.

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Section: Sum Check 715mentioning

confidence: 99%

Section: Prosumer Stereotypes In Microgridsmentioning

confidence: 99%

Section: Assumptions Of the Proposed Approachmentioning

confidence: 99%

“…and "How to determine a role of a prosumer?" (Shandurkova et al, 2013;Brendal, 2013;Filipowska et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Fabisz

Filipowska

Hossa

2015

International Journal of Energy Optimization and Engineering

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“…In [7], a new data mining scheme is proposed to forecast the peak load of a particular consumer entity in the smart grid for a future time unit. The authors utilize the least squares version of support vector regression with an online learning strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of the k-means clustering algorithm to predict load shedding of the Southern Electrical Grid of Libya

Alkilany

Ahmed

Said

et al. 2014

Fourth Edition of the International Conference on the Innovative Computing Technology (INTECH 2014)

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In Libya, from time to time, the National Electricity Grid is directed by the National Electricity Company to conduct load shedding to mitigate pressure on supply at times of peak demand. This involves hours' of power outages in the area covered by this study, namely, the Southern Electrical Grid of Libya (SEGL). This paper discusses the results of a pattern extraction process using the kmeans clustering algorithm to predict load shedding for this scenario. The data consist of all loads shed in 40 electrical power stations in southern Libya for a two-year period from 2009 through 2010. An experiment was conducted to assess the effectiveness of the k-means clustering algorithm in grouping (clustering) the data as a means to predict future load shedding in the SEGL. Each cluster was generated five times to create five different cluster sizes (1, 2, 5, 7 and 10) with different seed values. The pattern extracted provided information on all attributes. The obtained results showed that the generated clusters are fit to be used for the future load shedding schedule problem in the SEGL.Index Terms-data mining, decision support system, k-means algorithm, load shedding, clusters.

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