A Hybrid fuzzy regression - SSA approach for electricity consumption optimisation

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Collection:
Mokslo publikacijos / Scientific publications
Document Type:
Straipsnis / Article
Language:
Anglų kalba / English
Title:
A Hybrid fuzzy regression - SSA approach for electricity consumption optimisation
In the Journal:
Inžinerinė ekonomika [Engineering Economics]. 2019, 30 (2), p. 151-162
Keywords:
EN
Singular spectrum analysis; Electrical demand; Electrical energy consumption; Fuzzy regression; Prediction.
Summary / Abstract:

ENThe critical impact of electricity on the quality of human life and the use of all electrical appliances and equipment is in principle self-evident. Nevertheless, governments cannot afford even productivity to consumption. The countries developed many structures to operate and improve the city and industrial electricity consumption and to manage power generation efficiency. Increase in population and the culture of consumption force the governments to raise the price. However, it is difficult to find ways and means to effectively manage trends in public behaviour and to control the harmful action of the population. This process requires knowing the need and consumption of electricity. The article discusses the primary criterion that influences electricity consumption and uses the singular spectrum analysis based on these factors to predict use. Besides, a fuzzy regression model is represented to optimise function. Results of optimisation show a considerable reduction in comparison with SSA forecasting method, indicating the efficiency of the offered method. Eventually results considerably assume that attention to a way of construction and improvements of the culture of use is a priority of the persons making decisions to reduce radical electric consumption in Iran and to become more optimistic concerning management of an electrical network. [From the publication]

DOI:
10.5755/j01.ee.30.2.21001
ISSN:
1392-2785; 2029-5839
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Permalink:
https://www.lituanistika.lt/content/78626
Updated:
2019-08-26 16:25:44
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