Document
Generically Dependent Continuant
2007 IEEE Power Engineering Society General Meeting, PES
RDF description of Forecast techniques applied to feasibility studies for micro-hydraulic generation - http://orbis.uaslp.mx/vivo/individual/scopus7637
ARIMA; Evolutionary computation; Forecast techniques; Micro-hydraulic generation; Neural networks; Time series Evolutionary algorithms; Hydroelectric power plants; Neural networks; Time series analysis; Forecast techniques; Micro-hydraulic generation; Hydroelectric power
Forecast techniques applied to feasibility studies for micro-hydraulic generation
This paper presents the application of time series forecasting techniques to feasibility studies of micro-hydraulic generation. Available literature, details several techniques developed and implemented to perform time series forecasting. This paper will focus on the following techniques: ARIMA (Auto-regressive Integrated Moving Average), Neural Networks and Evolutionary Computation (EC). Based on the obtained results of the forecast techniques applied to the water flow time series, it is possible to determine if a micro-hydraulic plant can be installed, the theoretical power generation and the technical characteristics of each electro-mechanical component of the micro-hydraulic generation system. © 2007 IEEE.
Article
Information Content Entity
Conference Paper