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Review of performance optimization techniques applied to wind turbines

Chehouri, Adam; Younes, Rafic; Ilinca, Adrian; Perron, Jean - In: Applied Energy 142 (2015) C, pp. 361-388

This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the...

Persistent link: https://www.econbiz.de/10011208387

ANN based MPPT method for rapidly variable shading conditions

Rizzo, Santi Agatino; Scelba, Giacomo - In: Applied Energy 145 (2015) C, pp. 124-132

–voltage characteristic scansions, the greater the ANN’s ability to meet the maximum and its prediction accuracy. The algorithm is cost …–voltage characteristic scansions, the size of the ANN and its prediction accuracy. …

Persistent link: https://www.econbiz.de/10011263355

Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks

Kialashaki, Arash; Reisel, John R. - In: Applied Energy 108 (2013) C, pp. 271-280

This paper describes the development of energy-demand models which are able to predict the future energy demand in the residential sector of the United States. One set of models use an artificial neural network (ANN) technique, and the other set of models use a multiple linear regression (MLR)...

Persistent link: https://www.econbiz.de/10010665641

Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

Canizes, Bruno; Soares, João; Faria, Pedro; Vale, Zita - In: Applied Energy 108 (2013) C, pp. 261-270

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic...

Persistent link: https://www.econbiz.de/10010665644

ANN virtual sensors for emissions prediction and control

Yap, Wai Kean; Karri, Vishy - In: Applied Energy 88 (2011) 12, pp. 4505-4516

This paper demonstrates the use of artificial neural networks virtual sensors in emissions prediction and control for a …

Persistent link: https://www.econbiz.de/10010571984

Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

Mohandes, M.; Rehman, S.; Rahman, S.M. - In: Applied Energy 88 (2011) 11, pp. 4024-4032

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to...

Persistent link: https://www.econbiz.de/10010572059

Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction

Liu, Hui; Tian, Hong-qi; Li, Yan-fei - In: Applied Energy 98 (2012) C, pp. 415-424

Wind speed prediction is important to protect the security of wind power integration. The performance of hybrid methods … is always better than that of single ones in wind speed prediction. Based on Time Series, Artificial Neural Networks (ANN … performance, which can be applied to the non-stationary wind speed prediction in wind power systems. …

Persistent link: https://www.econbiz.de/10010702587

Artificial neural network based daily local forecasting for global solar radiation

Amrouche, Badia; Le Pivert, Xavier - In: Applied Energy 130 (2014) C, pp. 333-341

When a part of the power is generated by grid connected photovoltaic installations, an effective global solar irradiation (GSI) forecasting tool becomes a must to ensure the quality and the security of the electrical grid. GSI forecasts allow the quantification of generated photovoltaic (PV)...

Persistent link: https://www.econbiz.de/10011040851

Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends

Mohamed Ismail, Harun; Ng, Hoon Kiat; Queck, Cheen Wei; … - In: Applied Energy 92 (2012) C, pp. 769-777

This paper reports an artificial neural networks (ANN) modelling programme for a light-duty diesel engine powered using blends of various biodiesel fuels with conventional fossil diesel. ANN was used here to predict nine different engine-out responses, namely carbon monoxide (CO), carbon dioxide...

Persistent link: https://www.econbiz.de/10011040986

A naive Bayes model for robust remaining useful life prediction of lithium-ion battery

Ng, Selina S.Y.; Xing, Yinjiao; Tsui, Kwok L. - In: Applied Energy 118 (2014) C, pp. 114-123

Online state-of-health (SoH) estimation and remaining useful life (RUL) prediction is a critical problem in battery … temperatures, which is seldom considered in the literature. Li-ion battery RUL prediction under constant operating conditions at … prediction of batteries under different operating conditions. It is shown in this analysis that under constant discharge …

Persistent link: https://www.econbiz.de/10010743599