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A novel approach for the forecasting of mean hourly wind speed time series

Sfetsos, A. - In: Renewable Energy 27 (2002) 2, pp. 163-174

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The … initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found … finally proposed methodology is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging …

Persistent link: https://www.econbiz.de/10010806846

A comparison of various forecasting techniques applied to mean hourly wind speed time series

Sfetsos, A. - In: Renewable Energy 21 (2000) 1, pp. 23-35

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind …

Persistent link: https://www.econbiz.de/10010803768

Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks

Abdel-Aal, R.E.; Elhadidy, M.A.; Shaahid, S.M. - In: Renewable Energy 34 (2009) 7, pp. 1686-1699

MAEs of 1.20m/s and 1.42m/s which are lower than forecasting errors based on day-to-day persistence by 14.6% and 13 …

Persistent link: https://www.econbiz.de/10010805984

Neuro-fuzzy control for autonomous wind–diesel systems using biomass

Jurado, Francisco; Saenz, José R - In: Renewable Energy 27 (2002) 1, pp. 39-56

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of...

Persistent link: https://www.econbiz.de/10010806023

Use of neural networks for the creation of hourly global and diffuse solar irradiance data at representative locations in Greece

Moustris, K.; Paliatsos, A.G.; Bloutsos, A.; Nikolaidis, K. - In: Renewable Energy 33 (2008) 5, pp. 928-932

In this work, a new approach is tested by applying neural networks treatment to meteorological time-series data sets, recorded during 1991–2000 at certain Greek locations, in order to create fully appropriate solar data information. Neural networks, in this case, are used for creating missing...

Persistent link: https://www.econbiz.de/10010806943

Maximum power point traking controller for PV systems using neural networks

Bahgat, A.B.G.; Helwa, N.H.; Ahmad, G.E.; El Shenawy, E.T. - In: Renewable Energy 30 (2005) 8, pp. 1257-1268

This paper presents a development and implementation of a PC-based maximum power point tracker (MPPT) for PV system using neural networks (NN). The system consists of a PV module via a MPPT supplying a dc motor that drives an air fan. The control algorithm is developed to use the artificial NN...

Persistent link: https://www.econbiz.de/10010807049

Power optimization of wind turbines with data mining and evolutionary computation

Kusiak, Andrew; Zheng, Haiyang; Song, Zhe - In: Renewable Energy 35 (2010) 3, pp. 695-702

A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a...

Persistent link: https://www.econbiz.de/10010807120

Wind speed forecasting in the South Coast of Oaxaca, México

Cadenas, Erasmo; Rivera, Wilfrido - In: Renewable Energy 32 (2007) 12, pp. 2116-2128

Comparison of two techniques for wind speed forecasting in the South Coast of the state of Oaxaca, Mexico is presented …

Persistent link: https://www.econbiz.de/10010807229

Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis

Cellura, M.; Cirrincione, G.; Marvuglia, A.; Miraoui, A. - In: Renewable Energy 33 (2008) 6, pp. 1237-1250

The exploitation of the renewable energy sources plays a key role for achieving the CO2 emissions reduction targets established by the Kyoto Protocol, as well as for facing the shortage of world fossil fuels reserves.

Persistent link: https://www.econbiz.de/10010804504

Support vector machines for wind speed prediction

Mohandes, M.A.; Halawani, T.O.; Rehman, S.; Hussain, … - In: Renewable Energy 29 (2004) 6, pp. 939-947

This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks. Mean daily wind speed data from Madina city, Saudi Arabia, is used for building and testing both...

Persistent link: https://www.econbiz.de/10010804534