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1. Identification of type daily diagrams of electric consumption based on cluster analysis of multi-dimensional data by neural network

Creator:
Garlík, Bohumír and Křivan , Miloš
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Artificial Neurqal Network (ANN), Self-Organizing Map (SOM), Kohonen map, counter-propagation neural network, cluster analysis, Type daily Diagram (TDD), load prediction, intelligent building, and micro grif
Language:
English
Description:
This article establishes a mathematical description of a self-organizing neural network used for cluster analysis with a subsequent sampling of its effectiveness as an example of identification of the type daily diagrams of electric energy-consumption of complex intelligent buildings within an electric micro grid, namely for a typical work day and a day off on the basis of its annual history. The mentioned type daily diagram can be used to predict power consumption. This method is given in the context of the commonly used procedure for cluster analysis. The experiment was processed in the computer program Artint © 2010.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

2. The application of artificial intelligence in the process of optimizing energy consumption in intelligent areas

Creator:
Garlík, B.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
self organizing map, simulated annealing, cluster analysis, type daily diagrams, load prediction, unit commitment, intelligent building, renewable energy, and micro grid
Language:
English
Description:
The text describes the optimization task of renewable energy sources distributed to electrical microgrid of fictitious intelligent area that consists of intelligent buildings. Firstly, to solve this task a general optimization heuristic method of simulated annealing will be described. Testing was performed on the analytical functions but those will be only covered marginally. Of the tests on the approximation functions the method of simulated annealing would be the most suitable algorithm for the optimization task. Furthermore, two experiments were introduced. The first lies in the application of cluster analysis on daily diagrams of electricity consumption in intelligent buildings. Because the modeled year history of hourly electricity consumption is represented by multidimensional data this data forms the training set during the adaptive dynamics submitted to a competence model of neural network by days. After the network adaptation process the Kohonen's map during the adaptive dynamics will be drawn, from which required clusters can be read. In the second experiment a sorting design of the resources for typical days of a week is performed in the computer program UniCon.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public